eegdash.dataset.dataset module#

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds000117 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds000117 DOI: https://doi.org/10.18112/openneuro.ds000117.v1.1.0 NEMAR citation count: 77

Examples

>>> from eegdash.dataset import DS000117
>>> dataset = DS000117(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Wakeman2015', 'WakemanHenson']

class eegdash.dataset.dataset.DS000246(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

MEG-BIDS Brainstorm data sample

Study:: ds000246 (OpenNeuro)
Author (year):: Bock2018
Canonical:: —

Also importable as: DS000246, Bock2018.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 2; recordings: 3; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds000246 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds000246 DOI: https://doi.org/10.18112/openneuro.ds000246.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS000246
>>> dataset = DS000246(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS000247(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

MEG-BIDS OMEGA RestingState_sample

Study:: ds000247 (OpenNeuro)
Author (year):: Niso2018
Canonical:: OMEGA

Also importable as: DS000247, Niso2018, OMEGA.

Modality: meg; Experiment type: Resting-state; Subject type: Healthy. Subjects: 6; recordings: 10; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds000247 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds000247 DOI: https://doi.org/10.18112/openneuro.ds000247.v1.0.2 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS000247
>>> dataset = DS000247(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['OMEGA']

class eegdash.dataset.dataset.DS000248(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

MNE-Sample-Data

Study:: ds000248 (OpenNeuro)
Author (year):: Gramfort2018
Canonical:: MNE_Sample_Data

Also importable as: DS000248, Gramfort2018, MNE_Sample_Data.

Modality: meg; Experiment type: Attention; Subject type: Healthy. Subjects: 2; recordings: 3; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds000248 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds000248 DOI: https://doi.org/10.18112/openneuro.ds000248.v1.2.4 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS000248
>>> dataset = DS000248(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['MNE_Sample_Data']

class eegdash.dataset.dataset.DS001785(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Evidence accumulation relates to perceptual consciousness and monitoring

Study:: ds001785 (OpenNeuro)
Author (year):: Pereira2019_Evidence
Canonical:: —

Also importable as: DS001785, Pereira2019_Evidence.

Modality: eeg. Subjects: 18; recordings: 54; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds001785 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds001785 DOI: https://doi.org/10.18112/openneuro.ds001785.v1.1.1 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS001785
>>> dataset = DS001785(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS001787(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG meditation study

Study:: ds001787 (OpenNeuro)
Author (year):: Delorme2019
Canonical:: —

Also importable as: DS001787, Delorme2019.

Modality: eeg. Subjects: 24; recordings: 40; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds001787 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds001787 DOI: https://doi.org/10.18112/openneuro.ds001787.v1.1.1 NEMAR citation count: 6

Examples

>>> from eegdash.dataset import DS001787
>>> dataset = DS001787(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS001810(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG study of the attentional blink; before, during, and after transcranial Direct Current Stimulation (tDCS)

Study:: ds001810 (OpenNeuro)
Author (year):: Reteig2019
Canonical:: —

Also importable as: DS001810, Reteig2019.

Modality: eeg. Subjects: 47; recordings: 263; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds001810 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds001810 DOI: https://doi.org/10.18112/openneuro.ds001810.v1.1.0 NEMAR citation count: 6

Examples

>>> from eegdash.dataset import DS001810
>>> dataset = DS001810(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS001849(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

RS_TMSEEG_Data

Study:: ds001849 (OpenNeuro)
Author (year):: Freedberg2019
Canonical:: —

Also importable as: DS001849, Freedberg2019.

Modality: eeg. Subjects: 20; recordings: 120; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds001849 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds001849 DOI: https://doi.org/10.18112/openneuro.ds001849.v1.0.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS001849
>>> dataset = DS001849(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS001971(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Audiocue walking study

Study:: ds001971 (OpenNeuro)
Author (year):: Wagner2019
Canonical:: —

Also importable as: DS001971, Wagner2019.

Modality: eeg. Subjects: 20; recordings: 273; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds001971 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds001971 DOI: https://doi.org/10.18112/openneuro.ds001971.v1.1.1 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS001971
>>> dataset = DS001971(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002001(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Rivalry_Tagging

Study:: ds002001 (OpenNeuro)
Author (year):: Mendola2019
Canonical:: Mendola2020

Also importable as: DS002001, Mendola2019, Mendola2020.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 11; recordings: 69; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002001 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002001 DOI: https://doi.org/10.18112/openneuro.ds002001.v1.0.0 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS002001
>>> dataset = DS002001(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Mendola2020']

class eegdash.dataset.dataset.DS002034(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Real-time EEG feedback on alpha power lateralization leads to behavioral improvements in a covert attention task

Study:: ds002034 (OpenNeuro)
Author (year):: Schneider2019
Canonical:: —

Also importable as: DS002034, Schneider2019.

Modality: eeg. Subjects: 14; recordings: 167; tasks: 4.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002034 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002034 DOI: https://doi.org/10.18112/openneuro.ds002034.v1.0.3 NEMAR citation count: 7

Examples

>>> from eegdash.dataset import DS002034
>>> dataset = DS002034(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002094(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Single-pulse open-loop TMS-EEG dataset

Study:: ds002094 (OpenNeuro)
Author (year):: DS2094_Single_pulse
Canonical:: —

Also importable as: DS002094, DS2094_Single_pulse.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Unknown. Subjects: 20; recordings: 43; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002094 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002094 NEMAR citation count: 30

Examples

>>> from eegdash.dataset import DS002094
>>> dataset = DS002094(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002158(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Disentangling the origins of confidence in speeded perceptual judgments through multimodal imaging

Study:: ds002158 (OpenNeuro)
Author (year):: Pereira2019_Disentangling
Canonical:: —

Also importable as: DS002158, Pereira2019_Disentangling.

Modality: eeg. Subjects: 20; recordings: 117; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002158 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002158 DOI: https://doi.org/10.18112/openneuro.ds002158.v1.0.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS002158
>>> dataset = DS002158(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002181(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

CRYPTO and PROVIDE EEG Baseline Data

Study:: ds002181 (OpenNeuro)
Author (year):: Xie2019
Canonical:: —

Also importable as: DS002181, Xie2019.

Modality: eeg; Experiment type: Resting-state; Subject type: Development. Subjects: 226; recordings: 226; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002181 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002181 DOI: https://doi.org/mockDOI NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS002181
>>> dataset = DS002181(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002218(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Auditory and Visual Rhythm Omission EEG

Study:: ds002218 (OpenNeuro)
Author (year):: Comstock2019
Canonical:: —

Also importable as: DS002218, Comstock2019.

Modality: eeg. Subjects: 18; recordings: 18; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002218 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002218 DOI: https://doi.org/mockDOI NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS002218
>>> dataset = DS002218(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002312(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

OcularLDT

Study:: ds002312 (OpenNeuro)
Author (year):: Brooks2019
Canonical:: OcularLDT, ocular_ldt

Also importable as: DS002312, Brooks2019, OcularLDT, ocular_ldt.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 19; recordings: 23; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002312 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002312 DOI: https://doi.org/10.18112/openneuro.ds002312.v1.0.0

Examples

>>> from eegdash.dataset import DS002312
>>> dataset = DS002312(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['OcularLDT', 'ocular_ldt']

class eegdash.dataset.dataset.DS002336(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and fMRI acquisition during a motor imagery neurofeedback task: XP1

Study:: ds002336 (OpenNeuro)
Author (year):: Lioi2019_multi
Canonical:: —

Also importable as: DS002336, Lioi2019_multi.

Modality: eeg. Subjects: 10; recordings: 54; tasks: 6.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002336 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002336 DOI: https://doi.org/10.18112/openneuro.ds002336.v2.0.2 NEMAR citation count: 4

Examples

>>> from eegdash.dataset import DS002336
>>> dataset = DS002336(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002338(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and fMRI acquisition during a motor imagery neurofeedback task: XP2

Study:: ds002338 (OpenNeuro)
Author (year):: Lioi2019_multi_modal
Canonical:: —

Also importable as: DS002338, Lioi2019_multi_modal.

Modality: eeg. Subjects: 17; recordings: 85; tasks: 4.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002338 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002338 DOI: https://doi.org/10.18112/openneuro.ds002338.v2.0.1 NEMAR citation count: 11

Examples

>>> from eegdash.dataset import DS002338
>>> dataset = DS002338(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002550(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Differential brain mechanisms of selection and maintenance of information during working memory (MEG data)

Study:: ds002550 (OpenNeuro)
Author (year):: Quentin2020
Canonical:: —

Also importable as: DS002550, Quentin2020.

Modality: meg; Experiment type: Memory; Subject type: Healthy. Subjects: 22; recordings: 377; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002550 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002550 DOI: https://doi.org/10.18112/openneuro.ds002550.v1.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS002550
>>> dataset = DS002550(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002578(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Visual Oddball Task (256 channels)

Study:: ds002578 (OpenNeuro)
Author (year):: Delorme2020_Visual_Oddball_256
Canonical:: —

Also importable as: DS002578, Delorme2020_Visual_Oddball_256.

Modality: eeg. Subjects: 2; recordings: 2; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002578 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002578 DOI: https://doi.org/10.18112/openneuro.ds002578.v1.1.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS002578
>>> dataset = DS002578(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002680(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Go-nogo categorization and detection task

Study:: ds002680 (OpenNeuro)
Author (year):: Delorme2020_Go_nogo_categorization
Canonical:: —

Also importable as: DS002680, Delorme2020_Go_nogo_categorization.

Modality: eeg. Subjects: 14; recordings: 350; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002680 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002680 DOI: https://doi.org/10.18112/openneuro.ds002680.v1.2.0 NEMAR citation count: 5

Examples

>>> from eegdash.dataset import DS002680
>>> dataset = DS002680(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002691(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Internal attention study

Study:: ds002691 (OpenNeuro)
Author (year):: Delorme2020_Internal_attention
Canonical:: —

Also importable as: DS002691, Delorme2020_Internal_attention.

Modality: eeg. Subjects: 20; recordings: 20; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002691 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002691 DOI: https://doi.org/10.18112/openneuro.ds002691.v1.1.0 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS002691
>>> dataset = DS002691(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002712(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Numbers and Letters

Study:: ds002712 (OpenNeuro)
Author (year):: Aurtenetxe2020
Canonical:: —

Also importable as: DS002712, Aurtenetxe2020.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 25; recordings: 82; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002712 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002712 DOI: https://doi.org/10.18112/openneuro.ds002712.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS002712
>>> dataset = DS002712(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002718(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Face processing EEG dataset for EEGLAB

Study:: ds002718 (OpenNeuro)
Author (year):: Wakeman2020
Canonical:: WakemanHenson_EEG_MEG

Also importable as: DS002718, Wakeman2020, WakemanHenson_EEG_MEG.

Modality: eeg. Subjects: 18; recordings: 18; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002718 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002718 DOI: https://doi.org/10.18112/openneuro.ds002718.v1.1.0 NEMAR citation count: 11

Examples

>>> from eegdash.dataset import DS002718
>>> dataset = DS002718(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['WakemanHenson_EEG_MEG']

class eegdash.dataset.dataset.DS002720(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A dataset recorded during development of a tempo-based brain-computer music interface

Study:: ds002720 (OpenNeuro)
Author (year):: Daly2020_recorded
Canonical:: —

Also importable as: DS002720, Daly2020_recorded.

Modality: eeg. Subjects: 18; recordings: 165; tasks: 0.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002720 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002720 DOI: https://doi.org/10.18112/openneuro.ds002720.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS002720
>>> dataset = DS002720(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002721(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

An EEG dataset recorded during affective music listening

Study:: ds002721 (OpenNeuro)
Author (year):: Daly2020_recorded_affective
Canonical:: —

Also importable as: DS002721, Daly2020_recorded_affective.

Modality: eeg. Subjects: 31; recordings: 185; tasks: 0.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002721 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002721 DOI: https://doi.org/10.18112/openneuro.ds002721.v1.0.2 NEMAR citation count: 10

Examples

>>> from eegdash.dataset import DS002721
>>> dataset = DS002721(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002722(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A dataset recorded during development of an affective brain-computer music interface: calibration session

Study:: ds002722 (OpenNeuro)
Author (year):: Daly2020_recorded_development
Canonical:: —

Also importable as: DS002722, Daly2020_recorded_development.

Modality: eeg. Subjects: 19; recordings: 94; tasks: 0.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002722 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002722 DOI: https://doi.org/10.18112/openneuro.ds002722.v1.0.1 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS002722
>>> dataset = DS002722(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002723(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A dataset recorded during development of an affective brain-computer music interface: testing session

Study:: ds002723 (OpenNeuro)
Author (year):: Daly2020_session
Canonical:: —

Also importable as: DS002723, Daly2020_session.

Modality: eeg. Subjects: 8; recordings: 44; tasks: 0.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002723 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002723 DOI: https://doi.org/10.18112/openneuro.ds002723.v1.1.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS002723
>>> dataset = DS002723(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002724(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A dataset recorded during development of an affective brain-computer music interface: training sessions

Study:: ds002724 (OpenNeuro)
Author (year):: Daly2020_sessions
Canonical:: —

Also importable as: DS002724, Daly2020_sessions.

Modality: eeg. Subjects: 10; recordings: 96; tasks: 0.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002724 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002724 DOI: https://doi.org/10.18112/openneuro.ds002724.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS002724
>>> dataset = DS002724(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002725(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A dataset recording joint EEG-fMRI during affective music listening

Study:: ds002725 (OpenNeuro)
Author (year):: Daly2020_joint
Canonical:: —

Also importable as: DS002725, Daly2020_joint.

Modality: eeg. Subjects: 21; recordings: 105; tasks: 5.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002725 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002725 DOI: https://doi.org/10.18112/openneuro.ds002725.v1.0.0 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS002725
>>> dataset = DS002725(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002761(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

memoryreplay

Study:: ds002761 (OpenNeuro)
Author (year):: Wimmer2020
Canonical:: —

Also importable as: DS002761, Wimmer2020.

Modality: meg; Experiment type: Memory; Subject type: Healthy. Subjects: 25; recordings: 249; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002761 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002761 DOI: https://doi.org/10.18112/openneuro.ds002761.v1.1.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS002761
>>> dataset = DS002761(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002778(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

UC San Diego Resting State EEG Data from Patients with Parkinson’s Disease

Study:: ds002778 (OpenNeuro)
Author (year):: Rockhill2020
Canonical:: —

Also importable as: DS002778, Rockhill2020.

Modality: eeg. Subjects: 31; recordings: 46; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002778 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002778 DOI: https://doi.org/10.18112/openneuro.ds002778.v1.0.5 NEMAR citation count: 42

Examples

>>> from eegdash.dataset import DS002778
>>> dataset = DS002778(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002791(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

DataSet1

Study:: ds002791 (OpenNeuro)
Author (year):: Mheich2020_DataSet1
Canonical:: Mheich2020

Also importable as: DS002791, Mheich2020_DataSet1, Mheich2020.

Modality: eeg; Experiment type: Unknown; Subject type: Healthy. Subjects: 23; recordings: 92; tasks: 0.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002791 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002791 DOI: https://doi.org/10.18112/openneuro.ds002791.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS002791
>>> dataset = DS002791(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Mheich2020']

class eegdash.dataset.dataset.DS002799(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Human es-fMRI Resource: Concurrent deep-brain stimulation and whole-brain functional MRI

Study:: ds002799 (OpenNeuro)
Author (year):: Thompson2024
Canonical:: —

Also importable as: DS002799, Thompson2024.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 27; recordings: 16824; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002799 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002799 DOI: https://doi.org/10.18112/openneuro.ds002799.v1.0.4

Examples

>>> from eegdash.dataset import DS002799
>>> dataset = DS002799(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002814(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A Multimodal Neuroimaging Dataset to Study Spatiotemporal Dynamics of Visual Processing in Humans

Study:: ds002814 (OpenNeuro)
Author (year):: Ebrahiminia2020
Canonical:: —

Also importable as: DS002814, Ebrahiminia2020.

Modality: eeg. Subjects: 21; recordings: 168; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002814 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002814 DOI: https://doi.org/10.18112/openneuro.ds002814.v1.3.0 NEMAR citation count: 4

Examples

>>> from eegdash.dataset import DS002814
>>> dataset = DS002814(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002833(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

DataSet2

Study:: ds002833 (OpenNeuro)
Author (year):: Mheich2020_DataSet2
Canonical:: Mheich2024

Also importable as: DS002833, Mheich2020_DataSet2, Mheich2024.

Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 20; recordings: 80; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002833 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002833 DOI: https://doi.org/10.18112/openneuro.ds002833.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS002833
>>> dataset = DS002833(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Mheich2024']

class eegdash.dataset.dataset.DS002885(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

DBS Phantom Recordings

Study:: ds002885 (OpenNeuro)
Author (year):: Kandemir2020
Canonical:: —

Also importable as: DS002885, Kandemir2020.

Modality: meg; Experiment type: Other; Subject type: Other. Subjects: 2; recordings: 7; tasks: 4.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002885 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002885 DOI: https://doi.org/10.18112/openneuro.ds002885.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS002885
>>> dataset = DS002885(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002893(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Auditory-Visual Shift Study

Study:: ds002893 (OpenNeuro)
Author (year):: Westerfield2022
Canonical:: —

Also importable as: DS002893, Westerfield2022.

Modality: eeg. Subjects: 49; recordings: 52; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002893 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002893 DOI: https://doi.org/10.18112/openneuro.ds002893.v2.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS002893
>>> dataset = DS002893(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS002908(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Human MEG recordings during sequential conflict task

Study:: ds002908 (OpenNeuro)
Author (year):: Bogacz2020
Canonical:: Bogacz2024

Also importable as: DS002908, Bogacz2020, Bogacz2024.

Modality: meg; Experiment type: Attention; Subject type: Unknown. Subjects: 13; recordings: 53; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002908 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002908 DOI: https://doi.org/10.18112/openneuro.ds002908.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS002908
>>> dataset = DS002908(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Bogacz2024']

class eegdash.dataset.dataset.DS003004(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Imagined Emotion Study

Study:: ds003004 (OpenNeuro)
Author (year):: Onton2020
Canonical:: —

Also importable as: DS003004, Onton2020.

Modality: eeg. Subjects: 34; recordings: 34; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003004 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003004 DOI: https://doi.org/10.18112/openneuro.ds003004.v1.1.1 NEMAR citation count: 7

Examples

>>> from eegdash.dataset import DS003004
>>> dataset = DS003004(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003029(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Epilepsy-iEEG-Multicenter-Dataset

Study:: ds003029 (OpenNeuro)
Author (year):: Li2020
Canonical:: —

Also importable as: DS003029, Li2020.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 35; recordings: 106; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003029 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003029 DOI: https://doi.org/10.18112/openneuro.ds003029.v1.0.5 NEMAR citation count: 19

Examples

>>> from eegdash.dataset import DS003029
>>> dataset = DS003029(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003039(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

free walking study

Study:: ds003039 (OpenNeuro)
Author (year):: Jacobsen2020
Canonical:: —

Also importable as: DS003039, Jacobsen2020.

Modality: eeg. Subjects: 19; recordings: 19; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003039 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003039 DOI: https://doi.org/10.18112/openneuro.ds003039.v1.0.2 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS003039
>>> dataset = DS003039(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003061(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG data from an auditory oddball task

Study:: ds003061 (OpenNeuro)
Author (year):: Delorme2020_auditory_oddball
Canonical:: Delorme

Also importable as: DS003061, Delorme2020_auditory_oddball, Delorme.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 13; recordings: 39; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003061 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003061 DOI: https://doi.org/10.18112/openneuro.ds003061.v1.1.0 NEMAR citation count: 4

Examples

>>> from eegdash.dataset import DS003061
>>> dataset = DS003061(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Delorme']

class eegdash.dataset.dataset.DS003078(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

PROBE iEEG

Study:: ds003078 (OpenNeuro)
Author (year):: DOMENECH2020
Canonical:: —

Also importable as: DS003078, DOMENECH2020.

Modality: ieeg; Experiment type: Unknown; Subject type: Surgery. Subjects: 6; recordings: 72; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003078 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003078 DOI: https://doi.org/10.18112/openneuro.ds003078.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS003078
>>> dataset = DS003078(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003082(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Auditory Cortex Mapping Dataset

Study:: ds003082 (OpenNeuro)
Author (year):: Cote2020
Canonical:: Cote2015

Also importable as: DS003082, Cote2020, Cote2015.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 2; recordings: 3; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003082 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003082 DOI: https://doi.org/10.18112/openneuro.ds003082.v1.0.0 NEMAR citation count: 5

Examples

>>> from eegdash.dataset import DS003082
>>> dataset = DS003082(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Cote2015']

class eegdash.dataset.dataset.DS003104(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

MNE-somato-data-bids (anonymized)

Study:: ds003104 (OpenNeuro)
Author (year):: Parkkonen2020
Canonical:: MNESomato, Somato, MNESomatoData

Also importable as: DS003104, Parkkonen2020, MNESomato, Somato, MNESomatoData.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003104 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003104 DOI: https://doi.org/10.18112/openneuro.ds003104.v1.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS003104
>>> dataset = DS003104(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['MNESomato', 'Somato', 'MNESomatoData']

class eegdash.dataset.dataset.DS003190(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Assesment of the visual stimuli properties in P300 paradigm

Study:: ds003190 (OpenNeuro)
Author (year):: MendozaMontoya2020
Canonical:: —

Also importable as: DS003190, MendozaMontoya2020.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 19; recordings: 384; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003190 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003190 DOI: https://doi.org/10.18112/openneuro.ds003190.v1.0.1 NEMAR citation count: 4

Examples

>>> from eegdash.dataset import DS003190
>>> dataset = DS003190(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003194(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Neuroepo multisession

Study:: ds003194 (OpenNeuro)
Author (year):: Vega2020_Neuroepo
Canonical:: —

Also importable as: DS003194, Vega2020_Neuroepo.

Modality: eeg. Subjects: 15; recordings: 29; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003194 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003194 DOI: https://doi.org/10.18112/openneuro.ds003194.v1.0.3 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS003194
>>> dataset = DS003194(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003195(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Placebo Neuroepo multisession

Study:: ds003195 (OpenNeuro)
Author (year):: Vega2020_Placebo
Canonical:: —

Also importable as: DS003195, Vega2020_Placebo.

Modality: eeg. Subjects: 10; recordings: 20; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003195 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003195 DOI: https://doi.org/10.18112/openneuro.ds003195.v1.0.3 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS003195
>>> dataset = DS003195(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003343(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Disentangling the percepts of illusory movement and sensory stimulation during tendon vibration in the EEG

Study:: ds003343 (OpenNeuro)
Author (year):: Schneider2020
Canonical:: —

Also importable as: DS003343, Schneider2020.

Modality: eeg. Subjects: 20; recordings: 59; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003343 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003343 DOI: https://doi.org/10.18112/openneuro.ds003343.v2.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS003343
>>> dataset = DS003343(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003352(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

1 - Light Pink Spiral

Study:: ds003352 (OpenNeuro)
Author (year):: Hermann2020
Canonical:: Hermann2021

Also importable as: DS003352, Hermann2020, Hermann2021.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 18; recordings: 138; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003352 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003352 DOI: https://doi.org/10.18112/openneuro.ds003352.v1.0.0 NEMAR citation count: 4

Examples

>>> from eegdash.dataset import DS003352
>>> dataset = DS003352(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Hermann2021']

class eegdash.dataset.dataset.DS003374(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Dataset of neurons and intracranial EEG from human amygdala during aversive dynamic visual stimulation

Study:: ds003374 (OpenNeuro)
Author (year):: Fedele2020
Canonical:: —

Also importable as: DS003374, Fedele2020.

Modality: ieeg; Experiment type: Affect; Subject type: Epilepsy. Subjects: 9; recordings: 18; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003374 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003374 DOI: https://doi.org/10.18112/openneuro.ds003374.v1.1.1 NEMAR citation count: 4

Examples

>>> from eegdash.dataset import DS003374
>>> dataset = DS003374(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003380(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Corticothalamic communication under analgesia, sedation and gradual ischemia: a multimodal model of controlled gradual cerebral ischemia in pig

Study:: ds003380 (OpenNeuro)
Author (year):: Frasch2020
Canonical:: —

Also importable as: DS003380, Frasch2020.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Other. Subjects: 1; recordings: 5; tasks: 0.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003380 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003380 DOI: https://doi.org/10.18112/openneuro.ds003380.v1.0.0

Examples

>>> from eegdash.dataset import DS003380
>>> dataset = DS003380(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003392(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

NeuroSpin hMT+ Localizer DATA (MEG & aMRI)

Study:: ds003392 (OpenNeuro)
Author (year):: Zilber2020
Canonical:: —

Also importable as: DS003392, Zilber2020.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 12; recordings: 33; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003392 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003392 DOI: https://doi.org/10.18112/openneuro.ds003392.v1.0.4 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS003392
>>> dataset = DS003392(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003420(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

HD-EEGtask(Dataset 1)

Study:: ds003420 (OpenNeuro)
Author (year):: Mheich2020_HD
Canonical:: —

Also importable as: DS003420, Mheich2020_HD.

Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 23; recordings: 92; tasks: 0.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003420 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003420 DOI: https://doi.org/10.18112/openneuro.ds003420.v1.0.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS003420
>>> dataset = DS003420(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003421(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

HD-EEGtask(Dataset 2)

Study:: ds003421 (OpenNeuro)
Author (year):: Mheich2020_HD_EEGtask
Canonical:: —

Also importable as: DS003421, Mheich2020_HD_EEGtask.

Modality: eeg. Subjects: 20; recordings: 80; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003421 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003421 DOI: https://doi.org/10.18112/openneuro.ds003421.v1.0.2 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS003421
>>> dataset = DS003421(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003458(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: Three armed bandit gambling task

Study:: ds003458 (OpenNeuro)
Author (year):: Cavanagh2021_Three
Canonical:: —

Also importable as: DS003458, Cavanagh2021_Three.

Modality: eeg. Subjects: 23; recordings: 23; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003458 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003458 DOI: https://doi.org/10.18112/openneuro.ds003458.v1.1.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS003458
>>> dataset = DS003458(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003474(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: Probabilistic Selection and Depression

Study:: ds003474 (OpenNeuro)
Author (year):: Cavanagh2021_Probabilistic
Canonical:: —

Also importable as: DS003474, Cavanagh2021_Probabilistic.

Modality: eeg. Subjects: 122; recordings: 122; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003474 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003474 DOI: https://doi.org/10.18112/openneuro.ds003474.v1.1.0 NEMAR citation count: 9

Examples

>>> from eegdash.dataset import DS003474
>>> dataset = DS003474(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003478(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: Depression rest

Study:: ds003478 (OpenNeuro)
Author (year):: Cavanagh2021_Depression
Canonical:: —

Also importable as: DS003478, Cavanagh2021_Depression.

Modality: eeg. Subjects: 122; recordings: 243; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003478 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003478 DOI: https://doi.org/10.18112/openneuro.ds003478.v1.1.0 NEMAR citation count: 22

Examples

>>> from eegdash.dataset import DS003478
>>> dataset = DS003478(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003483(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Logical reasoning study

Study:: ds003483 (OpenNeuro)
Author (year):: Cognitive2021
Canonical:: Maestu2021

Also importable as: DS003483, Cognitive2021, Maestu2021.

Modality: meg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 21; recordings: 41; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003483 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003483 DOI: https://doi.org/10.18112/openneuro.ds003483.v1.0.2 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS003483
>>> dataset = DS003483(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Maestu2021']

class eegdash.dataset.dataset.DS003490(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: 3-Stim Auditory Oddball and Rest in Parkinson’s

Study:: ds003490 (OpenNeuro)
Author (year):: Cavanagh2021_3
Canonical:: —

Also importable as: DS003490, Cavanagh2021_3.

Modality: eeg. Subjects: 50; recordings: 75; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003490 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003490 DOI: https://doi.org/10.18112/openneuro.ds003490.v1.1.0 NEMAR citation count: 13

Examples

>>> from eegdash.dataset import DS003490
>>> dataset = DS003490(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003498(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

interictal iEEG during slow-wave sleep with HFO markings

Study:: ds003498 (OpenNeuro)
Author (year):: Fedele2021
Canonical:: —

Also importable as: DS003498, Fedele2021.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 20; recordings: 385; tasks: 0.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003498 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003498 DOI: https://doi.org/10.18112/openneuro.ds003498.v1.0.1 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS003498
>>> dataset = DS003498(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003505(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

VEPCON: Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes

Study:: ds003505 (OpenNeuro)
Author (year):: Pascucci2021
Canonical:: VEPCON

Also importable as: DS003505, Pascucci2021, VEPCON.

Modality: eeg. Subjects: 19; recordings: 37; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003505 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003505 DOI: https://doi.org/10.18112/openneuro.ds003505.v1.1.1 NEMAR citation count: 5

Examples

>>> from eegdash.dataset import DS003505
>>> dataset = DS003505(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['VEPCON']

class eegdash.dataset.dataset.DS003506(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: Reinforcement Learning in Parkinson’s

Study:: ds003506 (OpenNeuro)
Author (year):: Cavanagh2021_Reinforcement
Canonical:: —

Also importable as: DS003506, Cavanagh2021_Reinforcement.

Modality: eeg. Subjects: 56; recordings: 84; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003506 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003506 DOI: https://doi.org/10.18112/openneuro.ds003506.v1.1.0 NEMAR citation count: 4

Examples

>>> from eegdash.dataset import DS003506
>>> dataset = DS003506(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003509(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: Simon Conflict in Parkinson’s

Study:: ds003509 (OpenNeuro)
Author (year):: Cavanagh2021_Simon
Canonical:: —

Also importable as: DS003509, Cavanagh2021_Simon.

Modality: eeg. Subjects: 56; recordings: 84; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003509 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003509 DOI: https://doi.org/10.18112/openneuro.ds003509.v1.1.0 NEMAR citation count: 5

Examples

>>> from eegdash.dataset import DS003509
>>> dataset = DS003509(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003516(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: Attended Speaker Paradigm (Own Name in Ignored Stream)

Study:: ds003516 (OpenNeuro)
Author (year):: Holtze2021
Canonical:: —

Also importable as: DS003516, Holtze2021.

Modality: eeg. Subjects: 25; recordings: 25; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003516 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003516 DOI: https://doi.org/10.18112/openneuro.ds003516.v1.1.1 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS003516
>>> dataset = DS003516(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003517(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: Continuous gameplay of an 8-bit style video game

Study:: ds003517 (OpenNeuro)
Author (year):: Cavanagh2021_Continuous
Canonical:: —

Also importable as: DS003517, Cavanagh2021_Continuous.

Modality: eeg. Subjects: 17; recordings: 34; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003517 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003517 DOI: https://doi.org/10.18112/openneuro.ds003517.v1.1.0 NEMAR citation count: 5

Examples

>>> from eegdash.dataset import DS003517
>>> dataset = DS003517(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003518(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: Simon Conflict w/ Reinforcement + Cabergoline Challenge

Study:: ds003518 (OpenNeuro)
Author (year):: Cavanagh2021_Simon_Conflict
Canonical:: —

Also importable as: DS003518, Cavanagh2021_Simon_Conflict.

Modality: eeg. Subjects: 110; recordings: 137; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003518 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003518 DOI: https://doi.org/10.18112/openneuro.ds003518.v1.1.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS003518
>>> dataset = DS003518(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003519(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: Visual Working Memory + Cabergoline Challenge

Study:: ds003519 (OpenNeuro)
Author (year):: Cavanagh2021_Visual
Canonical:: —

Also importable as: DS003519, Cavanagh2021_Visual.

Modality: eeg. Subjects: 27; recordings: 54; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003519 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003519 DOI: https://doi.org/10.18112/openneuro.ds003519.v1.1.0 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS003519
>>> dataset = DS003519(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003522(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: Three-Stim Auditory Oddball and Rest in Acute and Chronic TBI

Study:: ds003522 (OpenNeuro)
Author (year):: Cavanagh2021_Three_Stim
Canonical:: —

Also importable as: DS003522, Cavanagh2021_Three_Stim.

Modality: eeg. Subjects: 96; recordings: 200; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003522 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003522 DOI: https://doi.org/10.18112/openneuro.ds003522.v1.1.0 NEMAR citation count: 5

Examples

>>> from eegdash.dataset import DS003522
>>> dataset = DS003522(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003523(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: Visual Working Memory in Acute TBI

Study:: ds003523 (OpenNeuro)
Author (year):: Cavanagh2021_Visual_Working
Canonical:: —

Also importable as: DS003523, Cavanagh2021_Visual_Working.

Modality: eeg. Subjects: 91; recordings: 221; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003523 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003523 DOI: https://doi.org/10.18112/openneuro.ds003523.v1.1.0 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS003523
>>> dataset = DS003523(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003555(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Dataset of EEG recordings of pediatric patients with epilepsy based on the 10-20 system

Study:: ds003555 (OpenNeuro)
Author (year):: Cserpan2021
Canonical:: —

Also importable as: DS003555, Cserpan2021.

Modality: eeg. Subjects: 30; recordings: 30; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003555 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003555 DOI: https://doi.org/10.18112/openneuro.ds003555.v1.0.1 NEMAR citation count: 8

Examples

>>> from eegdash.dataset import DS003555
>>> dataset = DS003555(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003568(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Mood induction in MDD and healthy adolescents

Study:: ds003568 (OpenNeuro)
Author (year):: Liuzzi2021
Canonical:: —

Also importable as: DS003568, Liuzzi2021.

Modality: meg; Experiment type: Affect; Subject type: Healthy. Subjects: 51; recordings: 118; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003568 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003568 DOI: https://doi.org/10.18112/openneuro.ds003568.v1.0.2 NEMAR citation count: 4

Examples

>>> from eegdash.dataset import DS003568
>>> dataset = DS003568(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003570(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: Improvisation and Musical Structures

Study:: ds003570 (OpenNeuro)
Author (year):: Goldman2021
Canonical:: —

Also importable as: DS003570, Goldman2021.

Modality: eeg. Subjects: 40; recordings: 40; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003570 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003570 DOI: https://doi.org/10.18112/openneuro.ds003570.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS003570
>>> dataset = DS003570(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003574(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Reward biases spontaneous neural reactivation during sleep

Study:: ds003574 (OpenNeuro)
Author (year):: Sterpenich2021
Canonical:: —

Also importable as: DS003574, Sterpenich2021.

Modality: eeg. Subjects: 18; recordings: 18; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003574 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003574 DOI: https://doi.org/10.18112/openneuro.ds003574.v1.0.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS003574
>>> dataset = DS003574(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003602(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Childhood Sexual Abuse and problem drinking in women: Neurobehavioral mechanisms

Study:: ds003602 (OpenNeuro)
Author (year):: Korucuoglu2021
Canonical:: —

Also importable as: DS003602, Korucuoglu2021.

Modality: eeg. Subjects: 118; recordings: 699; tasks: 6.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003602 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003602 DOI: https://doi.org/10.18112/openneuro.ds003602.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS003602
>>> dataset = DS003602(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003620(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Runabout: A mobile EEG study of auditory oddball processing in laboratory and real-world conditions

Study:: ds003620 (OpenNeuro)
Author (year):: Liebherr2021
Canonical:: Runabout

Also importable as: DS003620, Liebherr2021, Runabout.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 44; recordings: 100; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003620 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003620 DOI: https://doi.org/10.18112/openneuro.ds003620.v1.1.1 NEMAR citation count: 4

Examples

>>> from eegdash.dataset import DS003620
>>> dataset = DS003620(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Runabout']

class eegdash.dataset.dataset.DS003626(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Inner Speech

Study:: ds003626 (OpenNeuro)
Author (year):: Nieto2021
Canonical:: —

Also importable as: DS003626, Nieto2021.

Modality: eeg. Subjects: 10; recordings: 30; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003626 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003626 DOI: https://doi.org/10.18112/openneuro.ds003626.v2.0.0 NEMAR citation count: 6

Examples

>>> from eegdash.dataset import DS003626
>>> dataset = DS003626(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003633(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

ForrestGump-MEG

Study:: ds003633 (OpenNeuro)
Author (year):: Liu2021
Canonical:: ForrestGump_MEG

Also importable as: DS003633, Liu2021, ForrestGump_MEG.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 12; recordings: 96; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003633 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003633 DOI: https://doi.org/10.18112/openneuro.ds003633.v1.0.3 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS003633
>>> dataset = DS003633(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['ForrestGump_MEG']

class eegdash.dataset.dataset.DS003638(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: Electrophysiological biomarkers of behavioral dimensions from cross-species paradigms

Study:: ds003638 (OpenNeuro)
Author (year):: Cavanagh2021_Electrophysiological
Canonical:: —

Also importable as: DS003638, Cavanagh2021_Electrophysiological.

Modality: eeg. Subjects: 57; recordings: 57; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003638 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003638 DOI: https://doi.org/10.18112/openneuro.ds003638.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS003638
>>> dataset = DS003638(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003645(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Face processing MEEG dataset with HED annotation

Study:: ds003645 (OpenNeuro)
Author (year):: Wakeman2021
Canonical:: —

Also importable as: DS003645, Wakeman2021.

Modality: eeg, meg. Subjects: 19; recordings: 224; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003645 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003645 DOI: https://doi.org/10.18112/openneuro.ds003645.v2.0.2 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS003645
>>> dataset = DS003645(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003655(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

VerbalWorkingMemory

Study:: ds003655 (OpenNeuro)
Author (year):: Pavlov2021_VerbalWorkingMemory
Canonical:: —

Also importable as: DS003655, Pavlov2021_VerbalWorkingMemory.

Modality: eeg. Subjects: 156; recordings: 156; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003655 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003655 DOI: https://doi.org/10.18112/openneuro.ds003655.v1.0.0 NEMAR citation count: 4

Examples

>>> from eegdash.dataset import DS003655
>>> dataset = DS003655(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003670(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Dataset of Concurrent EEG, ECG, and Behavior with Multiple Doses of transcranial Electrical Stimulation - BIDS

Study:: ds003670 (OpenNeuro)
Author (year):: Gebodh2021
Canonical:: —

Also importable as: DS003670, Gebodh2021.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. Subjects: 25; recordings: 62; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003670 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003670 DOI: https://doi.org/10.18112/openneuro.ds003670.v1.1.0 NEMAR citation count: 6

Examples

>>> from eegdash.dataset import DS003670
>>> dataset = DS003670(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003682(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Model-based aversive learning in humans is supported by preferential task state reactivation

Study:: ds003682 (OpenNeuro)
Author (year):: Wise2021
Canonical:: —

Also importable as: DS003682, Wise2021.

Modality: meg; Experiment type: Learning; Subject type: Healthy. Subjects: 28; recordings: 336; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003682 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003682 DOI: https://doi.org/10.18112/openneuro.ds003682.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS003682
>>> dataset = DS003682(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003688(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Open multimodal iEEG-fMRI dataset from naturalistic stimulation with a short audiovisual film

Study:: ds003688 (OpenNeuro)
Author (year):: Berezutskaya2021
Canonical:: —

Also importable as: DS003688, Berezutskaya2021.

Modality: ieeg; Experiment type: Perception; Subject type: Epilepsy. Subjects: 51; recordings: 107; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003688 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003688 DOI: https://doi.org/10.18112/openneuro.ds003688.v1.0.7 NEMAR citation count: 9

Examples

>>> from eegdash.dataset import DS003688
>>> dataset = DS003688(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003690(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG, ECG and pupil data from young and older adults: rest and auditory cued reaction time tasks

Study:: ds003690 (OpenNeuro)
Author (year):: Ribeiro2021
Canonical:: —

Also importable as: DS003690, Ribeiro2021.

Modality: eeg. Subjects: 75; recordings: 375; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003690 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003690 DOI: https://doi.org/10.18112/openneuro.ds003690.v1.0.0 NEMAR citation count: 5

Examples

>>> from eegdash.dataset import DS003690
>>> dataset = DS003690(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003694(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

MEGMEM

Study:: ds003694 (OpenNeuro)
Author (year):: Griffiths2021
Canonical:: MEGMEM

Also importable as: DS003694, Griffiths2021, MEGMEM.

Modality: meg; Experiment type: Memory; Subject type: Unknown. Subjects: 28; recordings: 132; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003694 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003694 DOI: https://doi.org/10.18112/openneuro.ds003694.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS003694
>>> dataset = DS003694(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['MEGMEM']

class eegdash.dataset.dataset.DS003702(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Social Memory cuing

Study:: ds003702 (OpenNeuro)
Author (year):: Gregory2021
Canonical:: —

Also importable as: DS003702, Gregory2021.

Modality: eeg. Subjects: 47; recordings: 47; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003702 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003702 DOI: https://doi.org/10.18112/openneuro.ds003702.v1.0.1 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS003702
>>> dataset = DS003702(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003703(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Frequency Tagging of Syntactic Structure or Lexical Properties

Study:: ds003703 (OpenNeuro)
Author (year):: Kalenkovich2021
Canonical:: Kalenkovich2019

Also importable as: DS003703, Kalenkovich2021, Kalenkovich2019.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 34; recordings: 102; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003703 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003703 DOI: https://doi.org/10.18112/openneuro.ds003703.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS003703
>>> dataset = DS003703(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Kalenkovich2019']

class eegdash.dataset.dataset.DS003708(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Basis profile curve identification to understand electrical stimulation effects in human brain networks

Study:: ds003708 (OpenNeuro)
Author (year):: Hermes2021
Canonical:: Miller2021

Also importable as: DS003708, Hermes2021, Miller2021.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Unknown. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003708 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003708 DOI: https://doi.org/10.18112/openneuro.ds003708.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS003708
>>> dataset = DS003708(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Miller2021']

class eegdash.dataset.dataset.DS003710(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

APPLESEED Example Dataset

Study:: ds003710 (OpenNeuro)
Author (year):: Williams2021
Canonical:: APPLESEED

Also importable as: DS003710, Williams2021, APPLESEED.

Modality: eeg. Subjects: 13; recordings: 48; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003710 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003710 DOI: https://doi.org/10.18112/openneuro.ds003710.v1.0.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS003710
>>> dataset = DS003710(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['APPLESEED']

class eegdash.dataset.dataset.DS003739(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Perturbed beam-walking task

Study:: ds003739 (OpenNeuro)
Author (year):: Peterson2021_Perturbed_beam_walking
Canonical:: —

Also importable as: DS003739, Peterson2021_Perturbed_beam_walking.

Modality: eeg. Subjects: 30; recordings: 120; tasks: 4.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003739 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003739 DOI: https://doi.org/10.18112/openneuro.ds003739.v1.0.2 NEMAR citation count: 5

Examples

>>> from eegdash.dataset import DS003739
>>> dataset = DS003739(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003751(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Dataset on Emotion with Naturalistic Stimuli (DENS)

Study:: ds003751 (OpenNeuro)
Author (year):: Mishra2021
Canonical:: DENS

Also importable as: DS003751, Mishra2021, DENS.

Modality: eeg. Subjects: 38; recordings: 38; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003751 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003751 DOI: https://doi.org/10.18112/openneuro.ds003751.v1.0.2 NEMAR citation count: 7

Examples

>>> from eegdash.dataset import DS003751
>>> dataset = DS003751(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['DENS']

class eegdash.dataset.dataset.DS003753(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: Probabilistic Learning with Affective Feedback: Exp

Study:: ds003753 (OpenNeuro)
Author (year):: Brown2021_Probabilistic
Canonical:: —

Also importable as: DS003753, Brown2021_Probabilistic. Subjects: 25; recordings: 25; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003753 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003753

Examples

>>> from eegdash.dataset import DS003753
>>> dataset = DS003753(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003766(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking

Study:: ds003766 (OpenNeuro)
Author (year):: Chen2021
Canonical:: —

Also importable as: DS003766, Chen2021.

Modality: eeg. Subjects: 31; recordings: 124; tasks: 4.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003766 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003766 DOI: https://doi.org/10.18112/openneuro.ds003766.v2.0.3 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS003766
>>> dataset = DS003766(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003768(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Simultaneous EEG and fMRI signals during sleep from humans

Study:: ds003768 (OpenNeuro)
Author (year):: Gu2021
Canonical:: —

Also importable as: DS003768, Gu2021.

Modality: eeg. Subjects: 33; recordings: 255; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003768 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003768 DOI: https://doi.org/10.18112/openneuro.ds003768.v1.0.0 NEMAR citation count: 21

Examples

>>> from eegdash.dataset import DS003768
>>> dataset = DS003768(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003774(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Music Listening- Genre EEG dataset (MUSIN-G)

Study:: ds003774 (OpenNeuro)
Author (year):: Miyapuram2021
Canonical:: MUSING

Also importable as: DS003774, Miyapuram2021, MUSING.

Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 20; recordings: 240; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003774 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003774 DOI: https://doi.org/10.18112/openneuro.ds003774.v1.0.0 NEMAR citation count: 8

Examples

>>> from eegdash.dataset import DS003774
>>> dataset = DS003774(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['MUSING']

class eegdash.dataset.dataset.DS003775(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

SRM Resting-state EEG

Study:: ds003775 (OpenNeuro)
Author (year):: HatlestadHall2021
Canonical:: —

Also importable as: DS003775, HatlestadHall2021.

Modality: eeg; Experiment type: Resting-state; Subject type: Healthy. Subjects: 111; recordings: 153; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003775 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003775 DOI: https://doi.org/10.18112/openneuro.ds003775.v1.2.1 NEMAR citation count: 8

Examples

>>> from eegdash.dataset import DS003775
>>> dataset = DS003775(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003800(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Auditory Gamma Entrainment

Study:: ds003800 (OpenNeuro)
Author (year):: Lahijanian2021_Auditory
Canonical:: —

Also importable as: DS003800, Lahijanian2021_Auditory.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Dementia. Subjects: 13; recordings: 24; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003800 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003800 DOI: https://doi.org/10.18112/openneuro.ds003800.v1.0.0 NEMAR citation count: 4

Examples

>>> from eegdash.dataset import DS003800
>>> dataset = DS003800(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003801(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Neural Tracking to go

Study:: ds003801 (OpenNeuro)
Author (year):: Straetmans2021
Canonical:: —

Also importable as: DS003801, Straetmans2021.

Modality: eeg. Subjects: 20; recordings: 20; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003801 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003801 DOI: https://doi.org/10.18112/openneuro.ds003801.v1.0.0 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS003801
>>> dataset = DS003801(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003805(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Multisensory Gamma Entrainment

Study:: ds003805 (OpenNeuro)
Author (year):: Lahijanian2021_Multisensory
Canonical:: —

Also importable as: DS003805, Lahijanian2021_Multisensory.

Modality: eeg. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003805 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003805 DOI: https://doi.org/10.18112/openneuro.ds003805.v1.0.0 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS003805
>>> dataset = DS003805(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003810(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Motor Imagery vs Rest - Low-Cost EEG System

Study:: ds003810 (OpenNeuro)
Author (year):: Peterson2021_Motor_Imagery_vs
Canonical:: —

Also importable as: DS003810, Peterson2021_Motor_Imagery_vs.

Modality: eeg. Subjects: 10; recordings: 50; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003810 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003810 DOI: https://doi.org/10.18112/openneuro.ds003810.v2.0.2 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS003810
>>> dataset = DS003810(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003816(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The Effect of Buddhism Derived Loving Kindness Meditation on Modulating EEG: Long-term and Short-term Effect

Study:: ds003816 (OpenNeuro)
Author (year):: Sun2024
Canonical:: —

Also importable as: DS003816, Sun2024.

Modality: eeg. Subjects: 48; recordings: 1077; tasks: 8.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003816 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003816 DOI: https://doi.org/10.18112/openneuro.ds003816.v1.0.1

Examples

>>> from eegdash.dataset import DS003816
>>> dataset = DS003816(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003822(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: Probabilistic Learning with Affective Feedback: Exp

Study:: ds003822 (OpenNeuro)
Author (year):: Brown2021_Probabilistic_Learning
Canonical:: —

Also importable as: DS003822, Brown2021_Probabilistic_Learning. Subjects: 25; recordings: 25; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003822 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003822

Examples

>>> from eegdash.dataset import DS003822
>>> dataset = DS003822(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003825(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Human electroencephalography recordings from 50 subjects for 22,248 images from 1,854 object concepts

Study:: ds003825 (OpenNeuro)
Author (year):: Grootswagers2021
Canonical:: THINGS, THINGS_EEG

Also importable as: DS003825, Grootswagers2021, THINGS, THINGS_EEG.

Modality: eeg. Subjects: 50; recordings: 50; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003825 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003825 DOI: https://doi.org/10.18112/openneuro.ds003825.v1.1.0 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS003825
>>> dataset = DS003825(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['THINGS', 'THINGS_EEG']

class eegdash.dataset.dataset.DS003838(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG, pupillometry, ECG and photoplethysmography, and behavioral data in the digit span task and rest

Study:: ds003838 (OpenNeuro)
Author (year):: Pavlov2021_pupillometry
Canonical:: —

Also importable as: DS003838, Pavlov2021_pupillometry.

Modality: eeg. Subjects: 65; recordings: 130; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003838 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003838 DOI: https://doi.org/10.18112/openneuro.ds003838.v1.0.6 NEMAR citation count: 7

Examples

>>> from eegdash.dataset import DS003838
>>> dataset = DS003838(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003844(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Dataset Clinical Epilepsy iEEG to BIDS -RESPect_intraoperative_iEEG

Study:: ds003844 (OpenNeuro)
Author (year):: Zweiphenning2021
Canonical:: RESPect_intraop

Also importable as: DS003844, Zweiphenning2021, RESPect_intraop.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 6; recordings: 38; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003844 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003844 DOI: https://doi.org/10.18112/openneuro.ds003844.v1.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS003844
>>> dataset = DS003844(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['RESPect_intraop']

class eegdash.dataset.dataset.DS003846(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Prediction Error

Study:: ds003846 (OpenNeuro)
Author (year):: Gehrke2021
Canonical:: —

Also importable as: DS003846, Gehrke2021.

Modality: eeg. Subjects: 19; recordings: 50; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003846 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003846 DOI: https://doi.org/10.18112/openneuro.ds003846.v2.0.2 NEMAR citation count: 5

Examples

>>> from eegdash.dataset import DS003846
>>> dataset = DS003846(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003848(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Dataset Clinical Epilepsy iEEG to BIDS - RESPect_longterm_iEEG

Study:: ds003848 (OpenNeuro)
Author (year):: Blooijs2021
Canonical:: RESPect_longterm

Also importable as: DS003848, Blooijs2021, RESPect_longterm.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 6; recordings: 22; tasks: 6.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003848 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003848 DOI: https://doi.org/10.18112/openneuro.ds003848.v1.0.3 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS003848
>>> dataset = DS003848(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['RESPect_longterm']

class eegdash.dataset.dataset.DS003876(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Epilepsy-iEEG-Interictal-Multicenter-Dataset

Study:: ds003876 (OpenNeuro)
Author (year):: Gunnarsdottir2021
Canonical:: —

Also importable as: DS003876, Gunnarsdottir2021.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 39; recordings: 54; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003876 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003876 DOI: https://doi.org/10.18112/openneuro.ds003876.v1.0.2 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS003876
>>> dataset = DS003876(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003885(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Capacity for movement is an organisational principle in object representations: EEG data from Experiment 1

Study:: ds003885 (OpenNeuro)
Author (year):: Shatek2021_E1
Canonical:: —

Also importable as: DS003885, Shatek2021_E1.

Modality: eeg. Subjects: 24; recordings: 24; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003885 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003885 DOI: https://doi.org/10.18112/openneuro.ds003885.v1.0.7 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS003885
>>> dataset = DS003885(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003887(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Capacity for movement is an organisational principle in object representations: EEG data from Experiment 2

Study:: ds003887 (OpenNeuro)
Author (year):: Shatek2021_E2
Canonical:: —

Also importable as: DS003887, Shatek2021_E2.

Modality: eeg. Subjects: 24; recordings: 24; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003887 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003887 DOI: https://doi.org/10.18112/openneuro.ds003887.v1.2.2 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS003887
>>> dataset = DS003887(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003922(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Multisensory Correlation Detector

Study:: ds003922 (OpenNeuro)
Author (year):: Lerousseau2021
Canonical:: —

Also importable as: DS003922, Lerousseau2021.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 14; recordings: 164; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003922 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003922 DOI: https://doi.org/10.18112/openneuro.ds003922.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS003922
>>> dataset = DS003922(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003944(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: First Episode Psychosis vs. Control Resting Task 1

Study:: ds003944 (OpenNeuro)
Author (year):: Salisbury2021_First
Canonical:: —

Also importable as: DS003944, Salisbury2021_First.

Modality: eeg. Subjects: 82; recordings: 82; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003944 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003944 DOI: https://doi.org/10.18112/openneuro.ds003944.v1.0.1 NEMAR citation count: 7

Examples

>>> from eegdash.dataset import DS003944
>>> dataset = DS003944(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003947(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: First Episode Psychosis vs. Control Resting Task 2

Study:: ds003947 (OpenNeuro)
Author (year):: Salisbury2021_First_Episode
Canonical:: —

Also importable as: DS003947, Salisbury2021_First_Episode.

Modality: eeg. Subjects: 61; recordings: 61; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003947 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003947 DOI: https://doi.org/10.18112/openneuro.ds003947.v1.0.1 NEMAR citation count: 8

Examples

>>> from eegdash.dataset import DS003947
>>> dataset = DS003947(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003969(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Meditation vs thinking task

Study:: ds003969 (OpenNeuro)
Author (year):: Delorme2021
Canonical:: —

Also importable as: DS003969, Delorme2021.

Modality: eeg. Subjects: 98; recordings: 392; tasks: 4.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003969 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003969 DOI: https://doi.org/10.18112/openneuro.ds003969.v1.0.0 NEMAR citation count: 7

Examples

>>> from eegdash.dataset import DS003969
>>> dataset = DS003969(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS003987(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: Amphetamine trials 5CCPT and Probabilistic Learning

Study:: ds003987 (OpenNeuro)
Author (year):: Cavanagh2022_Amphetamine_trials_5
Canonical:: —

Also importable as: DS003987, Cavanagh2022_Amphetamine_trials_5.

Modality: eeg. Subjects: 23; recordings: 69; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003987 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003987 DOI: https://doi.org/10.18112/openneuro.ds003987.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS003987
>>> dataset = DS003987(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004000(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Fribourg Ultimatum Game in Schizophrenia Study

Study:: ds004000 (OpenNeuro)
Author (year):: Padee2022
Canonical:: —

Also importable as: DS004000, Padee2022.

Modality: eeg. Subjects: 43; recordings: 86; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004000 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004000 DOI: https://doi.org/10.18112/openneuro.ds004000.v1.0.0 NEMAR citation count: 6

Examples

>>> from eegdash.dataset import DS004000
>>> dataset = DS004000(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004010(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

MAVIS

Study:: ds004010 (OpenNeuro)
Author (year):: Waschke2022
Canonical:: MAVIS

Also importable as: DS004010, Waschke2022, MAVIS.

Modality: eeg. Subjects: 24; recordings: 24; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004010 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004010 DOI: https://doi.org/10.18112/openneuro.ds004010.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004010
>>> dataset = DS004010(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['MAVIS']

class eegdash.dataset.dataset.DS004011(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The nature of neural object representations during dynamic occlusion

Study:: ds004011 (OpenNeuro)
Author (year):: Teichmann2022
Canonical:: —

Also importable as: DS004011, Teichmann2022.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 22; recordings: 132; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004011 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004011 DOI: https://doi.org/10.18112/openneuro.ds004011.v1.0.3 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004011
>>> dataset = DS004011(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004012(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BRAR_NQ

Study:: ds004012 (OpenNeuro)
Author (year):: Rani2022
Canonical:: Rani2019

Also importable as: DS004012, Rani2022, Rani2019.

Modality: meg; Experiment type: Unknown; Subject type: Healthy. Subjects: 30; recordings: 294; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004012 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004012 DOI: https://doi.org/10.18112/openneuro.ds004012.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004012
>>> dataset = DS004012(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Rani2019']

class eegdash.dataset.dataset.DS004015(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Attended speaker paradigm (cEEGrid data)

Study:: ds004015 (OpenNeuro)
Author (year):: Holtze2022_Attended
Canonical:: —

Also importable as: DS004015, Holtze2022_Attended.

Modality: eeg. Subjects: 36; recordings: 36; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004015 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004015 DOI: https://doi.org/10.18112/openneuro.ds004015.v1.0.2 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS004015
>>> dataset = DS004015(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004017(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Embodied Learning for Literacy EEG

Study:: ds004017 (OpenNeuro)
Author (year):: Damsgaard2022
Canonical:: —

Also importable as: DS004017, Damsgaard2022.

Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 21; recordings: 63; tasks: 0.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004017 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004017 DOI: https://doi.org/10.18112/openneuro.ds004017.v1.0.3 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004017
>>> dataset = DS004017(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004018(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG recordings for 200 object images presented in RSVP sequences at 5Hz or 20Hz

Study:: ds004018 (OpenNeuro)
Author (year):: Grootswagers2022_RSVP
Canonical:: —

Also importable as: DS004018, Grootswagers2022_RSVP.

Modality: eeg. Subjects: 16; recordings: 32; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004018 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004018 DOI: https://doi.org/10.18112/openneuro.ds004018.v2.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004018
>>> dataset = DS004018(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004019(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Effect of obesity on arithmetic processing in preteens with high and low math skills. An event-related potentials study

Study:: ds004019 (OpenNeuro)
Author (year):: AlatorreCruz2022_Effect
Canonical:: —

Also importable as: DS004019, AlatorreCruz2022_Effect.

Modality: eeg; Experiment type: Other; Subject type: Obese. Subjects: 62; recordings: 62; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004019 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004019 DOI: https://doi.org/10.18112/openneuro.ds004019.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004019
>>> dataset = DS004019(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004022(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Multimodal EEG and fNIRS Biosignal Acquisition during Motor Imagery Tasks in Patients with Orthopedic Impairment

Study:: ds004022 (OpenNeuro)
Author (year):: Lee2022
Canonical:: —

Also importable as: DS004022, Lee2022.

Modality: eeg. Subjects: 7; recordings: 21; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004022 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004022 DOI: https://doi.org/10.18112/openneuro.ds004022.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004022
>>> dataset = DS004022(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004024(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

TMS-EEG-MRI-fMRI-DWI data on paired associative stimulation and connectivity (Shirley Ryan AbilityLab, Chicago, IL)

Study:: ds004024 (OpenNeuro)
Author (year):: Pavon2022
Canonical:: —

Also importable as: DS004024, Pavon2022.

Modality: eeg. Subjects: 13; recordings: 497; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004024 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004024 DOI: https://doi.org/10.18112/openneuro.ds004024.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004024
>>> dataset = DS004024(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004033(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Electrode walking study

Study:: ds004033 (OpenNeuro)
Author (year):: Scanlon2022
Canonical:: —

Also importable as: DS004033, Scanlon2022.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 18; recordings: 36; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004033 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004033 DOI: https://doi.org/10.18112/openneuro.ds004033.v1.0.0 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004033
>>> dataset = DS004033(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004040(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Trance channeling EEG study

Study:: ds004040 (OpenNeuro)
Author (year):: Cannard2022
Canonical:: —

Also importable as: DS004040, Cannard2022.

Modality: eeg. Subjects: 13; recordings: 26; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004040 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004040 DOI: https://doi.org/10.18112/openneuro.ds004040.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004040
>>> dataset = DS004040(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004043(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The time-course of feature-based attention effects dissociated from temporal expectation and target-related processes

Study:: ds004043 (OpenNeuro)
Author (year):: Moerel2022_time
Canonical:: —

Also importable as: DS004043, Moerel2022_time.

Modality: eeg. Subjects: 20; recordings: 20; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004043 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004043 DOI: https://doi.org/10.18112/openneuro.ds004043.v1.1.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004043
>>> dataset = DS004043(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004067(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Moral conviction and metacognitive ability shape multiple stages of information processing

Study:: ds004067 (OpenNeuro)
Author (year):: Yoder2022
Canonical:: —

Also importable as: DS004067, Yoder2022.

Modality: eeg. Subjects: 80; recordings: 84; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004067 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004067 DOI: https://doi.org/10.18112/openneuro.ds004067.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004067
>>> dataset = DS004067(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004075(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

what_are_we_talking_about

Study:: ds004075 (OpenNeuro)
Author (year):: Boncz2022
Canonical:: —

Also importable as: DS004075, Boncz2022.

Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 29; recordings: 116; tasks: 4.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004075 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004075 DOI: https://doi.org/10.18112/openneuro.ds004075.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004075
>>> dataset = DS004075(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004078(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A synchronized multimodal neuroimaging dataset to study brain language processing

Study:: ds004078 (OpenNeuro)
Author (year):: Wang2022_StudyBRAIN
Canonical:: —

Also importable as: DS004078, Wang2022_StudyBRAIN.

Modality: meg; Experiment type: Other; Subject type: Healthy. Subjects: 12; recordings: 720; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004078 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004078 DOI: https://doi.org/10.18112/openneuro.ds004078.v1.0.4 NEMAR citation count: 4

Examples

>>> from eegdash.dataset import DS004078
>>> dataset = DS004078(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004080(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

CCEP ECoG dataset across age 4-51

Study:: ds004080 (OpenNeuro)
Author (year):: Blooijs2023_CCEP_ECoG
Canonical:: RESPect_CCEP

Also importable as: DS004080, Blooijs2023_CCEP_ECoG, RESPect_CCEP.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 74; recordings: 117; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004080 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004080 DOI: https://doi.org/10.18112/openneuro.ds004080.v1.2.4 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004080
>>> dataset = DS004080(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['RESPect_CCEP']

class eegdash.dataset.dataset.DS004100(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

HUP iEEG Epilepsy Dataset

Study:: ds004100 (OpenNeuro)
Author (year):: Bernabei2022
Canonical:: HUPiEEG

Also importable as: DS004100, Bernabei2022, HUPiEEG.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 57; recordings: 319; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004100 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004100 DOI: https://doi.org/10.18112/openneuro.ds004100.v1.1.3 NEMAR citation count: 21

Examples

>>> from eegdash.dataset import DS004100
>>> dataset = DS004100(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HUPiEEG']

class eegdash.dataset.dataset.DS004105(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BCIT Auditory Cueing

Study:: ds004105 (OpenNeuro)
Author (year):: Garcia2022
Canonical:: BCIT_Auditory_Cueing

Also importable as: DS004105, Garcia2022, BCIT_Auditory_Cueing.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 17; recordings: 34; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004105 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004105 DOI: https://doi.org/10.18112/openneuro.ds004105.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004105
>>> dataset = DS004105(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BCIT_Auditory_Cueing']

class eegdash.dataset.dataset.DS004106(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BCIT Advanced Guard Duty

Study:: ds004106 (OpenNeuro)
Author (year):: Touryan2022
Canonical:: BCITAdvancedGuardDuty

Also importable as: DS004106, Touryan2022, BCITAdvancedGuardDuty.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 27; recordings: 29; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004106 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004106 DOI: https://doi.org/10.18112/openneuro.ds004106.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004106
>>> dataset = DS004106(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BCITAdvancedGuardDuty']

class eegdash.dataset.dataset.DS004107(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

MIND DATA

Study:: ds004107 (OpenNeuro)
Author (year):: Weisend2022
Canonical:: Weisend2007

Also importable as: DS004107, Weisend2022, Weisend2007.

Modality: meg; Experiment type: Other; Subject type: Healthy. Subjects: 9; recordings: 89; tasks: 6.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004107 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004107 DOI: https://doi.org/10.18112/openneuro.ds004107.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004107
>>> dataset = DS004107(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Weisend2007']

class eegdash.dataset.dataset.DS004117(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Sternberg Working Memory

Study:: ds004117 (OpenNeuro)
Author (year):: Onton2022
Canonical:: —

Also importable as: DS004117, Onton2022.

Modality: eeg. Subjects: 23; recordings: 85; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004117 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004117 DOI: https://doi.org/10.18112/openneuro.ds004117.v1.0.1 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004117
>>> dataset = DS004117(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004118(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BCIT Calibration Driving

Study:: ds004118 (OpenNeuro)
Author (year):: Touryan2022_BCIT_Calibration
Canonical:: Touryan1999

Also importable as: DS004118, Touryan2022_BCIT_Calibration, Touryan1999.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 156; recordings: 247; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004118 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004118 DOI: https://doi.org/10.18112/openneuro.ds004118.v1.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004118
>>> dataset = DS004118(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Touryan1999']

class eegdash.dataset.dataset.DS004119(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BCIT Basic Guard Duty

Study:: ds004119 (OpenNeuro)
Author (year):: Touryan2022_BCIT_Basic
Canonical:: BCIT

Also importable as: DS004119, Touryan2022_BCIT_Basic, BCIT.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 21; recordings: 22; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004119 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004119 DOI: https://doi.org/10.18112/openneuro.ds004119.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004119
>>> dataset = DS004119(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BCIT']

class eegdash.dataset.dataset.DS004120(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BCIT Baseline Driving

Study:: ds004120 (OpenNeuro)
Author (year):: Touryan2022_BCIT_Baseline
Canonical:: BCITBaselineDriving

Also importable as: DS004120, Touryan2022_BCIT_Baseline, BCITBaselineDriving.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 109; recordings: 131; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004120 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004120 DOI: https://doi.org/10.18112/openneuro.ds004120.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004120
>>> dataset = DS004120(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BCITBaselineDriving']

class eegdash.dataset.dataset.DS004121(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BCIT Mind Wandering

Study:: ds004121 (OpenNeuro)
Author (year):: Touryan2022_BCIT_Mind
Canonical:: BCITMindWandering

Also importable as: DS004121, Touryan2022_BCIT_Mind, BCITMindWandering.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 21; recordings: 60; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004121 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004121 DOI: https://doi.org/10.18112/openneuro.ds004121.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004121
>>> dataset = DS004121(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BCITMindWandering']

class eegdash.dataset.dataset.DS004122(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BCIT Speed Control

Study:: ds004122 (OpenNeuro)
Author (year):: Touryan2022_BCIT_Speed
Canonical:: —

Also importable as: DS004122, Touryan2022_BCIT_Speed.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 32; recordings: 63; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004122 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004122 DOI: https://doi.org/10.18112/openneuro.ds004122.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004122
>>> dataset = DS004122(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004123(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BCIT Traffic Complexity

Study:: ds004123 (OpenNeuro)
Author (year):: Touryan2022_BCIT_Traffic
Canonical:: BCIT_Traffic_Complexity

Also importable as: DS004123, Touryan2022_BCIT_Traffic, BCIT_Traffic_Complexity.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 29; recordings: 30; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004123 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004123 DOI: https://doi.org/10.18112/openneuro.ds004123.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004123
>>> dataset = DS004123(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BCIT_Traffic_Complexity']

class eegdash.dataset.dataset.DS004127(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Somatosensory Cortex Rat DISC Data

Study:: ds004127 (OpenNeuro)
Author (year):: Abrego2022
Canonical:: —

Also importable as: DS004127, Abrego2022.

Modality: ieeg; Experiment type: Other; Subject type: Other. Subjects: 8; recordings: 73; tasks: 11.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004127 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004127 DOI: https://doi.org/10.18112/openneuro.ds004127.v3.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004127
>>> dataset = DS004127(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004147(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Average Task Value

Study:: ds004147 (OpenNeuro)
Author (year):: Hassall2022_Average
Canonical:: —

Also importable as: DS004147, Hassall2022_Average.

Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 12; recordings: 12; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004147 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004147 DOI: https://doi.org/10.18112/openneuro.ds004147.v1.0.2 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004147
>>> dataset = DS004147(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004148(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A test-retest resting and cognitive state EEG dataset

Study:: ds004148 (OpenNeuro)
Author (year):: Wang2022_test_retest_resting
Canonical:: —

Also importable as: DS004148, Wang2022_test_retest_resting.

Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 60; recordings: 900; tasks: 5.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004148 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004148 DOI: https://doi.org/10.18112/openneuro.ds004148.v1.0.0 NEMAR citation count: 12

Examples

>>> from eegdash.dataset import DS004148
>>> dataset = DS004148(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004151(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Effect of obesity on inhibitory control in preadolescents during stop-signal task. An event-related potentials study

Study:: ds004151 (OpenNeuro)
Author (year):: AlatorreCruz2022_Effect_obesity
Canonical:: —

Also importable as: DS004151, AlatorreCruz2022_Effect_obesity.

Modality: eeg; Experiment type: Attention; Subject type: Obese. Subjects: 57; recordings: 57; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004151 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004151 DOI: https://doi.org/10.18112/openneuro.ds004151.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004151
>>> dataset = DS004151(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004152(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Drum Trainer

Study:: ds004152 (OpenNeuro)
Author (year):: Hassall2022_Drum
Canonical:: —

Also importable as: DS004152, Hassall2022_Drum.

Modality: eeg. Subjects: 21; recordings: 21; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004152 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004152 DOI: https://doi.org/10.18112/openneuro.ds004152.v1.1.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004152
>>> dataset = DS004152(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004166(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Effects of Forward and Backward Span Trainings on Working Memory: Evidence from a Randomized, Controlled Trial

Study:: ds004166 (OpenNeuro)
Author (year):: Li2022
Canonical:: —

Also importable as: DS004166, Li2022.

Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 71; recordings: 213; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004166 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004166 DOI: https://doi.org/10.18112/openneuro.ds004166.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004166
>>> dataset = DS004166(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004194(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Visual ECoG dataset

Study:: ds004194 (OpenNeuro)
Author (year):: Groen2022
Canonical:: —

Also importable as: DS004194, Groen2022.

Modality: ieeg; Experiment type: Perception; Subject type: Epilepsy. Subjects: 14; recordings: 209; tasks: 7.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004194 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004194 DOI: https://doi.org/10.18112/openneuro.ds004194.v3.0.0 NEMAR citation count: 4

Examples

>>> from eegdash.dataset import DS004194
>>> dataset = DS004194(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004196(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Bimodal dataset on Inner speech

Study:: ds004196 (OpenNeuro)
Author (year):: Liwicki2022
Canonical:: —

Also importable as: DS004196, Liwicki2022.

Modality: eeg. Subjects: 4; recordings: 4; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004196 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004196 DOI: https://doi.org/10.18112/openneuro.ds004196.v2.0.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004196
>>> dataset = DS004196(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004200(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Temporal Scaling

Study:: ds004200 (OpenNeuro)
Author (year):: Hassall2022_Temporal
Canonical:: —

Also importable as: DS004200, Hassall2022_Temporal.

Modality: eeg. Subjects: 20; recordings: 20; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004200 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004200 DOI: https://doi.org/10.18112/openneuro.ds004200.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004200
>>> dataset = DS004200(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004212(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

THINGS-MEG

Study:: ds004212 (OpenNeuro)
Author (year):: Hebart2022
Canonical:: THINGS_MEG, THINGSMEG

Also importable as: DS004212, Hebart2022, THINGS_MEG, THINGSMEG.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 5; recordings: 500; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004212 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004212 DOI: https://doi.org/10.18112/openneuro.ds004212.v3.0.0 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS004212
>>> dataset = DS004212(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['THINGS_MEG', 'THINGSMEG']

class eegdash.dataset.dataset.DS004229(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

amnoise

Study:: ds004229 (OpenNeuro)
Author (year):: Mittag2022
Canonical:: —

Also importable as: DS004229, Mittag2022.

Modality: meg; Experiment type: Perception; Subject type: Dyslexia. Subjects: 2; recordings: 3; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004229 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004229 DOI: https://doi.org/10.18112/openneuro.ds004229.v1.0.3 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004229
>>> dataset = DS004229(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004252(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Rotation-tolerant representations elucidate the time course of high-level object processing

Study:: ds004252 (OpenNeuro)
Author (year):: Moerel2022_Rotation
Canonical:: —

Also importable as: DS004252, Moerel2022_Rotation.

Modality: eeg. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004252 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004252 DOI: https://doi.org/10.18112/openneuro.ds004252.v1.0.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004252
>>> dataset = DS004252(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004256(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Encoding of Sound Source Elevation in Human Cortex

Study:: ds004256 (OpenNeuro)
Author (year):: Bialas2022
Canonical:: —

Also importable as: DS004256, Bialas2022.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 53; recordings: 53; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004256 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004256 DOI: https://doi.org/10.18112/openneuro.ds004256.v1.0.5 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004256
>>> dataset = DS004256(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004262(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Continuous Feedback Processing

Study:: ds004262 (OpenNeuro)
Author (year):: Hassall2022_Continuous
Canonical:: —

Also importable as: DS004262, Hassall2022_Continuous.

Modality: eeg. Subjects: 21; recordings: 21; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004262 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004262 DOI: https://doi.org/10.18112/openneuro.ds004262.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004262
>>> dataset = DS004262(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004264(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Steer the Ship

Study:: ds004264 (OpenNeuro)
Author (year):: Hassall2022_Steer
Canonical:: —

Also importable as: DS004264, Hassall2022_Steer.

Modality: eeg. Subjects: 21; recordings: 21; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004264 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004264 DOI: https://doi.org/10.18112/openneuro.ds004264.v1.1.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004264
>>> dataset = DS004264(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004276(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Auditory single word recognition in MEG

Study:: ds004276 (OpenNeuro)
Author (year):: Gaston2022
Canonical:: —

Also importable as: DS004276, Gaston2022.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 19; recordings: 19; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004276 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004276 DOI: https://doi.org/10.18112/openneuro.ds004276.v1.0.0 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004276
>>> dataset = DS004276(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004278(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Sustained Neural Representations of Personally Familiar People and Places During Cued Recall

Study:: ds004278 (OpenNeuro)
Author (year):: Kidder2022
Canonical:: Kidder2024

Also importable as: DS004278, Kidder2022, Kidder2024.

Modality: meg; Experiment type: Memory; Subject type: Healthy. Subjects: 30; recordings: 30; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004278 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004278 DOI: https://doi.org/10.18112/openneuro.ds004278.v1.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004278
>>> dataset = DS004278(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Kidder2024']

class eegdash.dataset.dataset.DS004279(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Large Spanish EEG

Study:: ds004279 (OpenNeuro)
Author (year):: Araya2022
Canonical:: —

Also importable as: DS004279, Araya2022.

Modality: eeg. Subjects: 56; recordings: 60; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004279 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004279 DOI: https://doi.org/10.18112/openneuro.ds004279.v1.1.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004279
>>> dataset = DS004279(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004284(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

eeg-neuroforecasting

Study:: ds004284 (OpenNeuro)
Author (year):: Veillette2022
Canonical:: —

Also importable as: DS004284, Veillette2022.

Modality: eeg. Subjects: 18; recordings: 18; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004284 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004284 DOI: https://doi.org/10.18112/openneuro.ds004284.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004284
>>> dataset = DS004284(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004295(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Reward gain and punishment avoidance reversal learning

Study:: ds004295 (OpenNeuro)
Author (year):: Stolz2022
Canonical:: —

Also importable as: DS004295, Stolz2022.

Modality: eeg. Subjects: 26; recordings: 26; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004295 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004295 DOI: https://doi.org/10.18112/openneuro.ds004295.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004295
>>> dataset = DS004295(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004306(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG Semantic Imagination and Perception Dataset

Study:: ds004306 (OpenNeuro)
Author (year):: Wilson2022
Canonical:: —

Also importable as: DS004306, Wilson2022.

Modality: eeg. Subjects: 12; recordings: 15; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004306 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004306 DOI: https://doi.org/10.18112/openneuro.ds004306.v1.0.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004306
>>> dataset = DS004306(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004315(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Mood Manipulation and PST, Experiment 1

Study:: ds004315 (OpenNeuro)
Author (year):: Cavanagh2022_E1
Canonical:: —

Also importable as: DS004315, Cavanagh2022_E1.

Modality: eeg. Subjects: 50; recordings: 50; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004315 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004315 DOI: https://doi.org/10.18112/openneuro.ds004315.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004315
>>> dataset = DS004315(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004317(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Mood Manipulation and PST, Experiment 2

Study:: ds004317 (OpenNeuro)
Author (year):: Cavanagh2022_E2
Canonical:: —

Also importable as: DS004317, Cavanagh2022_E2.

Modality: eeg. Subjects: 50; recordings: 50; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004317 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004317 DOI: https://doi.org/10.18112/openneuro.ds004317.v1.0.3 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004317
>>> dataset = DS004317(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004324(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

ToonFaces

Study:: ds004324 (OpenNeuro)
Author (year):: Chacon2022
Canonical:: ToonFaces

Also importable as: DS004324, Chacon2022, ToonFaces.

Modality: eeg. Subjects: 26; recordings: 26; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004324 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004324 DOI: https://doi.org/10.18112/openneuro.ds004324.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004324
>>> dataset = DS004324(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['ToonFaces']

class eegdash.dataset.dataset.DS004330(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The spatiotemporal neural dynamics of object recognition for natural images and line drawings (MEG)

Study:: ds004330 (OpenNeuro)
Author (year):: Singer2022
Canonical:: —

Also importable as: DS004330, Singer2022.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 30; recordings: 270; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004330 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004330 DOI: https://doi.org/10.18112/openneuro.ds004330.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004330
>>> dataset = DS004330(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004346(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

FLUX: A pipeline for MEG analysis

Study:: ds004346 (OpenNeuro)
Author (year):: Ferrante2022
Canonical:: FLUX

Also importable as: DS004346, Ferrante2022, FLUX.

Modality: meg; Experiment type: Attention; Subject type: Healthy. Subjects: 1; recordings: 3; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004346 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004346 DOI: https://doi.org/10.18112/openneuro.ds004346.v1.0.8 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004346
>>> dataset = DS004346(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['FLUX']

class eegdash.dataset.dataset.DS004347(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Symmetry perception and affective responses: a combined EEG/EMG study

Study:: ds004347 (OpenNeuro)
Author (year):: Makin2022
Canonical:: —

Also importable as: DS004347, Makin2022.

Modality: eeg. Subjects: 24; recordings: 24; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004347 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004347 DOI: https://doi.org/10.18112/openneuro.ds004347.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004347
>>> dataset = DS004347(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004348(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Ear-EEG Sleep Monitoring 2017 (EESM17)

Study:: ds004348 (OpenNeuro)
Author (year):: Mikkelsen2022
Canonical:: EESM17

Also importable as: DS004348, Mikkelsen2022, EESM17.

Modality: eeg. Subjects: 9; recordings: 18; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004348 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004348 DOI: https://doi.org/10.18112/openneuro.ds004348.v1.0.5 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004348
>>> dataset = DS004348(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['EESM17']

class eegdash.dataset.dataset.DS004350(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Executive Functionning Study for Assessing the Effect of Neurofeedback

Study:: ds004350 (OpenNeuro)
Author (year):: Delorme2022
Canonical:: —

Also importable as: DS004350, Delorme2022.

Modality: eeg. Subjects: 24; recordings: 240; tasks: 5.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004350 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004350 DOI: https://doi.org/10.18112/openneuro.ds004350.v2.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004350
>>> dataset = DS004350(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004356(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Subcortical responses to music and speech are alike while cortical responses diverge

Study:: ds004356 (OpenNeuro)
Author (year):: Shan2022
Canonical:: —

Also importable as: DS004356, Shan2022.

Modality: eeg. Subjects: 22; recordings: 24; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004356 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004356 DOI: https://doi.org/10.18112/openneuro.ds004356.v2.2.1 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004356
>>> dataset = DS004356(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004357(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Features-EEG

Study:: ds004357 (OpenNeuro)
Author (year):: Grootswagers2022_EEG
Canonical:: —

Also importable as: DS004357, Grootswagers2022_EEG.

Modality: eeg. Subjects: 16; recordings: 16; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004357 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004357 DOI: https://doi.org/10.18112/openneuro.ds004357.v1.0.1 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004357
>>> dataset = DS004357(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004362(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG Motor Movement/Imagery Dataset

Study:: ds004362 (OpenNeuro)
Author (year):: Schalk2022
Canonical:: PhysionetMI, EEGMotorMovementImagery

Also importable as: DS004362, Schalk2022, PhysionetMI, EEGMotorMovementImagery.

Modality: eeg. Subjects: 109; recordings: 1526; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004362 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004362 DOI: https://doi.org/10.18112/openneuro.ds004362.v1.0.0 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004362
>>> dataset = DS004362(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['PhysionetMI', 'EEGMotorMovementImagery']

class eegdash.dataset.dataset.DS004367(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Meta-rdk: Raw EEG data

Study:: ds004367 (OpenNeuro)
Author (year):: Rouy2022_Meta
Canonical:: —

Also importable as: DS004367, Rouy2022_Meta.

Modality: eeg. Subjects: 40; recordings: 40; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004367 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004367 DOI: https://doi.org/10.18112/openneuro.ds004367.v1.0.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004367
>>> dataset = DS004367(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004368(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Meta-rdk: Preprocessed EEG data

Study:: ds004368 (OpenNeuro)
Author (year):: Rouy2022_Meta_rdk
Canonical:: —

Also importable as: DS004368, Rouy2022_Meta_rdk.

Modality: eeg. Subjects: 39; recordings: 40; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004368 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004368 DOI: https://doi.org/10.18112/openneuro.ds004368.v1.0.2 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004368
>>> dataset = DS004368(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004369(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Blink-Pause-Relation (Competing Speaker Paradigm)

Study:: ds004369 (OpenNeuro)
Author (year):: Holtze2022_Blink
Canonical:: —

Also importable as: DS004369, Holtze2022_Blink.

Modality: eeg. Subjects: 41; recordings: 41; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004369 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004369 DOI: https://doi.org/10.18112/openneuro.ds004369.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004369
>>> dataset = DS004369(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004370(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

PRIOS

Study:: ds004370 (OpenNeuro)
Author (year):: Blooijs2022_PRIOS
Canonical:: PRIOS

Also importable as: DS004370, Blooijs2022_PRIOS, PRIOS.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Surgery. Subjects: 7; recordings: 15; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004370 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004370 DOI: https://doi.org/10.18112/openneuro.ds004370.v1.0.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004370
>>> dataset = DS004370(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['PRIOS']

class eegdash.dataset.dataset.DS004381(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Intraoperative EEG dataset during medianus-tibialis stimulation with 8 different rates

Study:: ds004381 (OpenNeuro)
Author (year):: Selmin2022
Canonical:: —

Also importable as: DS004381, Selmin2022.

Modality: eeg. Subjects: 18; recordings: 437; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004381 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004381 DOI: https://doi.org/10.18112/openneuro.ds004381.v1.0.2 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004381
>>> dataset = DS004381(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004388(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Somatosensory evoked potentials in the human spinal cord to mixed nerve stimulation

Study:: ds004388 (OpenNeuro)
Author (year):: Nierula2023_Somatosensory
Canonical:: —

Also importable as: DS004388, Nierula2023_Somatosensory.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 40; recordings: 399; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004388 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004388 DOI: https://doi.org/10.18112/openneuro.ds004388.v1.0.0 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS004388
>>> dataset = DS004388(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004389(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Somatosensory evoked potentials in the human spinal cord to mixed and sensory nerve stimulation

Study:: ds004389 (OpenNeuro)
Author (year):: Nierula2023_Somatosensory_evoked
Canonical:: —

Also importable as: DS004389, Nierula2023_Somatosensory_evoked.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 26; recordings: 260; tasks: 4.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004389 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004389 DOI: https://doi.org/10.18112/openneuro.ds004389.v1.0.0 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004389
>>> dataset = DS004389(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004395(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Penn Electrophysiology of Encoding and Retrieval Study (PEERS)

Study:: ds004395 (OpenNeuro)
Author (year):: Kahana2023
Canonical:: PEERS

Also importable as: DS004395, Kahana2023, PEERS.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 364; recordings: 6483; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004395 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004395 DOI: https://doi.org/10.18112/openneuro.ds004395.v2.0.0 NEMAR citation count: 6

Examples

>>> from eegdash.dataset import DS004395
>>> dataset = DS004395(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['PEERS']

class eegdash.dataset.dataset.DS004398(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

planmemreplay

Study:: ds004398 (OpenNeuro)
Author (year):: Wimmer2023
Canonical:: Wimmer2024

Also importable as: DS004398, Wimmer2023, Wimmer2024.

Modality: meg; Experiment type: Unknown; Subject type: Unknown. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004398 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004398 DOI: https://doi.org/10.18112/openneuro.ds004398.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004398
>>> dataset = DS004398(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Wimmer2024']

class eegdash.dataset.dataset.DS004408(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG responses to continuous naturalistic speech

Study:: ds004408 (OpenNeuro)
Author (year):: Liberto2023
Canonical:: —

Also importable as: DS004408, Liberto2023.

Modality: eeg. Subjects: 19; recordings: 380; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004408 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004408 DOI: https://doi.org/10.18112/openneuro.ds004408.v1.0.8 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004408
>>> dataset = DS004408(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004444(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The BMI-HDEEG dataset 1

Study:: ds004444 (OpenNeuro)
Author (year):: Iwama2023_D1
Canonical:: BMI_HDEEG_D1

Also importable as: DS004444, Iwama2023_D1, BMI_HDEEG_D1.

Modality: eeg. Subjects: 30; recordings: 465; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004444 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004444 DOI: https://doi.org/10.18112/openneuro.ds004444.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004444
>>> dataset = DS004444(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BMI_HDEEG_D1']

class eegdash.dataset.dataset.DS004446(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The BMI-HDEEG dataset 2

Study:: ds004446 (OpenNeuro)
Author (year):: Iwama2023_D2
Canonical:: BMI_HDEEG_D2

Also importable as: DS004446, Iwama2023_D2, BMI_HDEEG_D2.

Modality: eeg. Subjects: 30; recordings: 237; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004446 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004446 DOI: https://doi.org/10.18112/openneuro.ds004446.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004446
>>> dataset = DS004446(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BMI_HDEEG_D2']

class eegdash.dataset.dataset.DS004447(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The BMI-HDEEG dataset 3

Study:: ds004447 (OpenNeuro)
Author (year):: Iwama2023_D3
Canonical:: BMI_HDEEG_D3

Also importable as: DS004447, Iwama2023_D3, BMI_HDEEG_D3.

Modality: eeg. Subjects: 22; recordings: 418; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004447 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004447 DOI: https://doi.org/10.18112/openneuro.ds004447.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004447
>>> dataset = DS004447(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BMI_HDEEG_D3']

class eegdash.dataset.dataset.DS004448(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The BMI-HDEEG dataset 4

Study:: ds004448 (OpenNeuro)
Author (year):: Iwama2023_D4
Canonical:: BMI_HDEEG_D4

Also importable as: DS004448, Iwama2023_D4, BMI_HDEEG_D4.

Modality: eeg. Subjects: 56; recordings: 280; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004448 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004448 DOI: https://doi.org/10.18112/openneuro.ds004448.v1.0.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004448
>>> dataset = DS004448(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BMI_HDEEG_D4']

class eegdash.dataset.dataset.DS004457(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex

Study:: ds004457 (OpenNeuro)
Author (year):: Huang2023
Canonical:: Huang2022

Also importable as: DS004457, Huang2023, Huang2022.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Surgery. Subjects: 5; recordings: 5; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004457 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004457 DOI: https://doi.org/10.18112/openneuro.ds004457.v1.0.1 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS004457
>>> dataset = DS004457(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Huang2022']

class eegdash.dataset.dataset.DS004460(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG and motion capture data set for a full-body/joystick rotation task

Study:: ds004460 (OpenNeuro)
Author (year):: Gramann2023
Canonical:: —

Also importable as: DS004460, Gramann2023.

Modality: eeg. Subjects: 20; recordings: 40; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004460 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004460 DOI: https://doi.org/10.18112/openneuro.ds004460.v1.1.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004460
>>> dataset = DS004460(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004473(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

sEEG Forced Two-Choice Task

Study:: ds004473 (OpenNeuro)
Author (year):: Rockhill2023
Canonical:: Rockhill2022

Also importable as: DS004473, Rockhill2023, Rockhill2022.

Modality: ieeg; Experiment type: Motor; Subject type: Epilepsy. Subjects: 8; recordings: 8; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004473 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004473 DOI: https://doi.org/10.18112/openneuro.ds004473.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004473
>>> dataset = DS004473(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Rockhill2022']

class eegdash.dataset.dataset.DS004475(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Mobile EEG split-belt walking study

Study:: ds004475 (OpenNeuro)
Author (year):: Jacobsen2023
Canonical:: —

Also importable as: DS004475, Jacobsen2023.

Modality: eeg. Subjects: 30; recordings: 30; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004475 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004475 DOI: https://doi.org/10.18112/openneuro.ds004475.v1.0.3 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004475
>>> dataset = DS004475(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004477(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

PES - Pandemic Emergency Scenario

Study:: ds004477 (OpenNeuro)
Author (year):: Papastylianou2023
Canonical:: —

Also importable as: DS004477, Papastylianou2023.

Modality: eeg. Subjects: 9; recordings: 9; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004477 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004477 DOI: https://doi.org/10.18112/openneuro.ds004477.v1.0.2 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004477
>>> dataset = DS004477(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004483(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

ABSeqMEG

Study:: ds004483 (OpenNeuro)
Author (year):: Planton2023
Canonical:: ABSeqMEG

Also importable as: DS004483, Planton2023, ABSeqMEG.

Modality: meg; Experiment type: Memory; Subject type: Healthy. Subjects: 19; recordings: 282; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004483 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004483 DOI: https://doi.org/10.18112/openneuro.ds004483.v1.0.0 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004483
>>> dataset = DS004483(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['ABSeqMEG']

class eegdash.dataset.dataset.DS004502(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Anticipatory differences between Attention and Expectation

Study:: ds004502 (OpenNeuro)
Author (year):: Penalver2023
Canonical:: Penalver2024

Also importable as: DS004502, Penalver2023, Penalver2024.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 48; recordings: 48; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004502 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004502 DOI: https://doi.org/10.18112/openneuro.ds004502.v1.0.1 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS004502
>>> dataset = DS004502(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Penalver2024']

class eegdash.dataset.dataset.DS004504(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A dataset of EEG recordings from: Alzheimer’s disease, Frontotemporal dementia and Healthy subjects

Study:: ds004504 (OpenNeuro)
Author (year):: Miltiadous2023
Canonical:: —

Also importable as: DS004504, Miltiadous2023.

Modality: eeg. Subjects: 88; recordings: 88; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004504 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004504 DOI: https://doi.org/10.18112/openneuro.ds004504.v1.0.8 NEMAR citation count: 55

Examples

>>> from eegdash.dataset import DS004504
>>> dataset = DS004504(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004505(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Real World Table Tennis

Study:: ds004505 (OpenNeuro)
Author (year):: Studnicki2023
Canonical:: —

Also importable as: DS004505, Studnicki2023.

Modality: eeg. Subjects: 25; recordings: 25; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004505 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004505 DOI: https://doi.org/10.18112/openneuro.ds004505.v1.0.4 NEMAR citation count: 5

Examples

>>> from eegdash.dataset import DS004505
>>> dataset = DS004505(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004511(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Deception_data

Study:: ds004511 (OpenNeuro)
Author (year):: Makowski2023_Deception
Canonical:: —

Also importable as: DS004511, Makowski2023_Deception.

Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 45; recordings: 134; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004511 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004511 DOI: https://doi.org/10.18112/openneuro.ds004511.v1.0.2 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004511
>>> dataset = DS004511(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004514(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Simultaneous EEG and fNIRS recordings for semantic decoding of imagined animals and tools

Study:: ds004514 (OpenNeuro)
Author (year):: Rybar2023_Simultaneous
Canonical:: —

Also importable as: DS004514, Rybar2023_Simultaneous.

Modality: eeg, fnirs; Experiment type: Other; Subject type: Healthy. Subjects: 12; recordings: 24; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004514 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004514 DOI: https://doi.org/10.18112/openneuro.ds004514.v1.1.2

Examples

>>> from eegdash.dataset import DS004514
>>> dataset = DS004514(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004515(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: Alcohol imagery reinforcement learning task with light and heavy drinker participants

Study:: ds004515 (OpenNeuro)
Author (year):: Singh2023
Canonical:: —

Also importable as: DS004515, Singh2023.

Modality: eeg. Subjects: 54; recordings: 54; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004515 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004515 DOI: https://doi.org/10.18112/openneuro.ds004515.v1.0.0 NEMAR citation count: 4

Examples

>>> from eegdash.dataset import DS004515
>>> dataset = DS004515(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004517(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG recordings for semantic decoding of imagined animals and tools during auditory imagery task

Study:: ds004517 (OpenNeuro)
Author (year):: Rybar2023_semantic
Canonical:: —

Also importable as: DS004517, Rybar2023_semantic.

Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 7; recordings: 7; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004517 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004517 DOI: https://doi.org/10.18112/openneuro.ds004517.v1.0.2

Examples

>>> from eegdash.dataset import DS004517
>>> dataset = DS004517(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004519(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Internal selective attention is delayed by competition between endogenous and exogenous factors

Study:: ds004519 (OpenNeuro)
Author (year):: Ester2023_Internal
Canonical:: Ester2022

Also importable as: DS004519, Ester2023_Internal, Ester2022.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 40; recordings: 40; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004519 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004519 DOI: https://doi.org/10.18112/openneuro.ds004519.v1.0.1 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS004519
>>> dataset = DS004519(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Ester2022']

class eegdash.dataset.dataset.DS004520(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Changes in behavioral priority influence the accessibility of working memory content - Experiment 2

Study:: ds004520 (OpenNeuro)
Author (year):: Ester2023_Changes
Canonical:: Ester2024_E2

Also importable as: DS004520, Ester2023_Changes, Ester2024_E2.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 33; recordings: 33; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004520 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004520 DOI: https://doi.org/10.18112/openneuro.ds004520.v1.0.1 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS004520
>>> dataset = DS004520(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Ester2024_E2']

class eegdash.dataset.dataset.DS004521(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Changes in behavioral priority influence the accessibility of working memory content - Experiment 1

Study:: ds004521 (OpenNeuro)
Author (year):: Ester2023_Changes_behavioral
Canonical:: Ester2024_E1

Also importable as: DS004521, Ester2023_Changes_behavioral, Ester2024_E1.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 34; recordings: 34; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004521 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004521 DOI: https://doi.org/10.18112/openneuro.ds004521.v1.0.1 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS004521
>>> dataset = DS004521(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Ester2024_E1']

class eegdash.dataset.dataset.DS004532(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: Probabilistic Selection Task (PST) + PST with Cabergoline Challenge

Study:: ds004532 (OpenNeuro)
Author (year):: Cavanagh2023
Canonical:: —

Also importable as: DS004532, Cavanagh2023.

Modality: eeg. Subjects: 110; recordings: 137; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004532 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004532 DOI: https://doi.org/10.18112/openneuro.ds004532.v1.2.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004532
>>> dataset = DS004532(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004541(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Multimodal EEG-fNIRS data from patients undergoing general anesthesia

Study:: ds004541 (OpenNeuro)
Author (year):: Ferron2023
Canonical:: Ferron2019

Also importable as: DS004541, Ferron2023, Ferron2019.

Modality: eeg, fnirs; Experiment type: Clinical/Intervention; Subject type: Surgery. Subjects: 8; recordings: 18; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004541 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004541 DOI: https://doi.org/10.18112/openneuro.ds004541.v1.0.0

Examples

>>> from eegdash.dataset import DS004541
>>> dataset = DS004541(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Ferron2019']

class eegdash.dataset.dataset.DS004551(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

iEEG on children during slow wave sleep

Study:: ds004551 (OpenNeuro)
Author (year):: Sakakura2023_children_slow_wave
Canonical:: Sakakura2025

Also importable as: DS004551, Sakakura2023_children_slow_wave, Sakakura2025.

Modality: ieeg; Experiment type: Sleep; Subject type: Epilepsy. Subjects: 114; recordings: 125; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004551 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004551 DOI: https://doi.org/10.18112/openneuro.ds004551.v1.0.6 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS004551
>>> dataset = DS004551(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Sakakura2025']

class eegdash.dataset.dataset.DS004554(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Forced Picture Naming Task

Study:: ds004554 (OpenNeuro)
Author (year):: Volpert2023
Canonical:: —

Also importable as: DS004554, Volpert2023.

Modality: eeg. Subjects: 16; recordings: 16; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004554 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004554 DOI: https://doi.org/10.18112/openneuro.ds004554.v1.0.4 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004554
>>> dataset = DS004554(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004561(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Illusion of Agency over Electrically-Actuated Movements

Study:: ds004561 (OpenNeuro)
Author (year):: Veillette2023
Canonical:: —

Also importable as: DS004561, Veillette2023.

Modality: eeg. Subjects: 23; recordings: 23; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004561 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004561 DOI: https://doi.org/10.18112/openneuro.ds004561.v1.0.0 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004561
>>> dataset = DS004561(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004563(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Vicarious touch: overlapping neural patterns between seeing and feeling touch

Study:: ds004563 (OpenNeuro)
Author (year):: Smit2023
Canonical:: —

Also importable as: DS004563, Smit2023.

Modality: eeg; Experiment type: Perception; Subject type: Other. Subjects: 40; recordings: 119; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004563 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004563 DOI: https://doi.org/10.18112/openneuro.ds004563.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004563
>>> dataset = DS004563(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004572(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The effects of sham hypnosis techniques

Study:: ds004572 (OpenNeuro)
Author (year):: Kekecs2023
Canonical:: Kekecs2024

Also importable as: DS004572, Kekecs2023, Kekecs2024.

Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 52; recordings: 516; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004572 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004572 DOI: https://doi.org/10.18112/openneuro.ds004572.v1.3.2 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004572
>>> dataset = DS004572(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Kekecs2024']

class eegdash.dataset.dataset.DS004574(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Cross-modal Oddball Task.

Study:: ds004574 (OpenNeuro)
Author (year):: Singh2023_Cross_modal
Canonical:: —

Also importable as: DS004574, Singh2023_Cross_modal.

Modality: eeg. Subjects: 146; recordings: 146; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004574 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004574 DOI: https://doi.org/10.18112/openneuro.ds004574.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004574
>>> dataset = DS004574(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004577(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Dataset containing resting EEG for a sample of 103 normal infants in the first year of life

Study:: ds004577 (OpenNeuro)
Author (year):: Unit2023
Canonical:: —

Also importable as: DS004577, Unit2023.

Modality: eeg. Subjects: 103; recordings: 130; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004577 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004577 DOI: https://doi.org/10.18112/openneuro.ds004577.v1.0.1 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS004577
>>> dataset = DS004577(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004579(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Interval Timing Task

Study:: ds004579 (OpenNeuro)
Author (year):: Singh2023_Interval_Timing
Canonical:: —

Also importable as: DS004579, Singh2023_Interval_Timing.

Modality: eeg. Subjects: 139; recordings: 139; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004579 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004579 DOI: https://doi.org/10.18112/openneuro.ds004579.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004579
>>> dataset = DS004579(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004580(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Simon-conflict Task.

Study:: ds004580 (OpenNeuro)
Author (year):: Singh2023_Simon_conflict
Canonical:: —

Also importable as: DS004580, Singh2023_Simon_conflict.

Modality: eeg. Subjects: 147; recordings: 147; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004580 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004580 DOI: https://doi.org/10.18112/openneuro.ds004580.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004580
>>> dataset = DS004580(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004582(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

FakeFaceEmo_data

Study:: ds004582 (OpenNeuro)
Author (year):: Makowski2023_FakeFaceEmo
Canonical:: —

Also importable as: DS004582, Makowski2023_FakeFaceEmo.

Modality: eeg. Subjects: 73; recordings: 73; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004582 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004582 DOI: https://doi.org/10.18112/openneuro.ds004582.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004582
>>> dataset = DS004582(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004584(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Rest eyes open

Study:: ds004584 (OpenNeuro)
Author (year):: Singh2023_Rest_eyes
Canonical:: —

Also importable as: DS004584, Singh2023_Rest_eyes.

Modality: eeg. Subjects: 149; recordings: 149; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004584 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004584 DOI: https://doi.org/10.18112/openneuro.ds004584.v1.0.0 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004584
>>> dataset = DS004584(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004587(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

IllusionGameEEG_data

Study:: ds004587 (OpenNeuro)
Author (year):: Makowski2023_IllusionGameEEG
Canonical:: —

Also importable as: DS004587, Makowski2023_IllusionGameEEG.

Modality: eeg. Subjects: 103; recordings: 114; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004587 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004587 DOI: https://doi.org/10.18112/openneuro.ds004587.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004587
>>> dataset = DS004587(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004588(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Neuma

Study:: ds004588 (OpenNeuro)
Author (year):: Georgiadis2023
Canonical:: Neuma

Also importable as: DS004588, Georgiadis2023, Neuma.

Modality: eeg. Subjects: 42; recordings: 42; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004588 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004588 DOI: https://doi.org/10.18112/openneuro.ds004588.v1.2.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004588
>>> dataset = DS004588(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Neuma']

class eegdash.dataset.dataset.DS004595(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: RL Task (3-Armed Bandit) with alcohol cues in hazardous drinkers and ctls

Study:: ds004595 (OpenNeuro)
Author (year):: Campbell2023
Canonical:: —

Also importable as: DS004595, Campbell2023.

Modality: eeg. Subjects: 53; recordings: 53; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004595 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004595 DOI: https://doi.org/10.18112/openneuro.ds004595.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004595
>>> dataset = DS004595(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004598(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

LFP during linear track in 6-month old TgF344-AD rats

Study:: ds004598 (OpenNeuro)
Author (year):: Faraz2023
Canonical:: Moradi2024

Also importable as: DS004598, Faraz2023, Moradi2024.

Modality: eeg; Experiment type: Memory; Subject type: Dementia. Subjects: 9; recordings: 20; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004598 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004598 DOI: https://doi.org/10.18112/openneuro.ds004598.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004598
>>> dataset = DS004598(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Moradi2024']

class eegdash.dataset.dataset.DS004602(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Registered Replication Report of ERN/Pe Psychometrics

Study:: ds004602 (OpenNeuro)
Author (year):: Clayson2023_Registered
Canonical:: —

Also importable as: DS004602, Clayson2023_Registered.

Modality: eeg. Subjects: 182; recordings: 546; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004602 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004602 DOI: https://doi.org/10.18112/openneuro.ds004602.v1.0.1 NEMAR citation count: 5

Examples

>>> from eegdash.dataset import DS004602
>>> dataset = DS004602(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004603(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Visual Attribute-Specific Contextual Trajectory Paradigm

Study:: ds004603 (OpenNeuro)
Author (year):: Lowe2023
Canonical:: VisualContextTrajectory

Also importable as: DS004603, Lowe2023, VisualContextTrajectory.

Modality: eeg. Subjects: 37; recordings: 37; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004603 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004603 DOI: https://doi.org/10.18112/openneuro.ds004603.v1.1.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004603
>>> dataset = DS004603(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['VisualContextTrajectory']

class eegdash.dataset.dataset.DS004621(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The Nencki-Symfonia EEG/ERP dataset

Study:: ds004621 (OpenNeuro)
Author (year):: Patrycja2023_Nencki
Canonical:: NenckiSymfonia

Also importable as: DS004621, Patrycja2023_Nencki, NenckiSymfonia.

Modality: eeg. Subjects: 42; recordings: 167; tasks: 4.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004621 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004621 DOI: https://doi.org/10.18112/openneuro.ds004621.v1.0.4 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004621
>>> dataset = DS004621(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['NenckiSymfonia']

class eegdash.dataset.dataset.DS004624(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Intracranial recordings using BCI2000 and the CorTec BrainInterchange

Study:: ds004624 (OpenNeuro)
Author (year):: Mivalt2025
Canonical:: Mivalt2024, BCI2000_Intracranial

Also importable as: DS004624, Mivalt2025, Mivalt2024, BCI2000_Intracranial.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Surgery. Subjects: 3; recordings: 614; tasks: 28.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004624 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004624 DOI: https://doi.org/10.18112/openneuro.ds004624.v2.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004624
>>> dataset = DS004624(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Mivalt2024', 'BCI2000_Intracranial']

class eegdash.dataset.dataset.DS004625(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Mind in Motion Young Adults Walking Over Uneven Terrain

Study:: ds004625 (OpenNeuro)
Author (year):: Liu2023
Canonical:: —

Also importable as: DS004625, Liu2023.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 32; recordings: 543; tasks: 9.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004625 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004625 DOI: https://doi.org/10.18112/openneuro.ds004625.v1.0.2 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004625
>>> dataset = DS004625(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004626(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Can we dissociate hypervigilance to social threats from altered perceptual decision-making processes in lonely individuals? An exploration with Drift Diffusion Modelling and event-related potentials.

Study:: ds004626 (OpenNeuro)
Author (year):: Maka2023
Canonical:: —

Also importable as: DS004626, Maka2023.

Modality: eeg. Subjects: 52; recordings: 52; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004626 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004626 DOI: https://doi.org/10.18112/openneuro.ds004626.v1.0.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004626
>>> dataset = DS004626(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004635(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Gaffrey Lab Infant Microstates Reliability

Study:: ds004635 (OpenNeuro)
Author (year):: Bagdasarov2023
Canonical:: —

Also importable as: DS004635, Bagdasarov2023.

Modality: eeg. Subjects: 48; recordings: 48; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004635 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004635 DOI: https://doi.org/10.18112/openneuro.ds004635.v3.1.0 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004635
>>> dataset = DS004635(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004642(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Intraoperative recordings of medianus stimulation with low and high impedance ECoG

Study:: ds004642 (OpenNeuro)
Author (year):: Dimakopoulos2023_Intraoperative
Canonical:: —

Also importable as: DS004642, Dimakopoulos2023_Intraoperative.

Modality: ieeg; Experiment type: Other; Subject type: Surgery. Subjects: 10; recordings: 10; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004642 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004642 DOI: https://doi.org/10.18112/openneuro.ds004642.v1.0.1 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004642
>>> dataset = DS004642(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004657(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Driving with Autonomous Aids

Study:: ds004657 (OpenNeuro)
Author (year):: Metcalfe2023_Driving
Canonical:: TX20

Also importable as: DS004657, Metcalfe2023_Driving, TX20.

Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 24; recordings: 119; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004657 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004657 DOI: https://doi.org/10.18112/openneuro.ds004657.v1.0.3 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004657
>>> dataset = DS004657(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['TX20']

class eegdash.dataset.dataset.DS004660(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

TNO

Study:: ds004660 (OpenNeuro)
Author (year):: Johnson2023_TNO
Canonical:: TNO

Also importable as: DS004660, Johnson2023_TNO, TNO.

Modality: eeg. Subjects: 21; recordings: 42; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004660 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004660 DOI: https://doi.org/10.18112/openneuro.ds004660.v1.0.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004660
>>> dataset = DS004660(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['TNO']

class eegdash.dataset.dataset.DS004661(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

ANDI

Study:: ds004661 (OpenNeuro)
Author (year):: Johnson2023_ANDI
Canonical:: ANDI

Also importable as: DS004661, Johnson2023_ANDI, ANDI.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 17; recordings: 17; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004661 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004661 DOI: https://doi.org/10.18112/openneuro.ds004661.v1.1.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004661
>>> dataset = DS004661(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['ANDI']

class eegdash.dataset.dataset.DS004696(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

HAPwave_bids

Study:: ds004696 (OpenNeuro)
Author (year):: Valencia2023
Canonical:: —

Also importable as: DS004696, Valencia2023.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 8; recordings: 8; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004696 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004696 DOI: https://doi.org/10.18112/openneuro.ds004696.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004696
>>> dataset = DS004696(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004703(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

sEEG Passive listening to natural speech

Study:: ds004703 (OpenNeuro)
Author (year):: Mai2023
Canonical:: —

Also importable as: DS004703, Mai2023.

Modality: ieeg; Experiment type: Memory; Subject type: Surgery. Subjects: 10; recordings: 11; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004703 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004703 DOI: https://doi.org/10.18112/openneuro.ds004703.v1.1.0 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004703
>>> dataset = DS004703(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004706(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Spatial memory and non-invasive closed-loop stimulus timing

Study:: ds004706 (OpenNeuro)
Author (year):: Rudoler2023
Canonical:: —

Also importable as: DS004706, Rudoler2023.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 34; recordings: 298; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004706 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004706 DOI: https://doi.org/10.18112/openneuro.ds004706.v1.0.0 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS004706
>>> dataset = DS004706(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004718(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Le Petit Prince Hong Kong: Naturalistic fMRI and EEG dataset from older Cantonese speakers

Study:: ds004718 (OpenNeuro)
Author (year):: Momenian2023
Canonical:: —

Also importable as: DS004718, Momenian2023.

Modality: eeg. Subjects: 51; recordings: 51; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004718 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004718 DOI: https://doi.org/10.18112/openneuro.ds004718.v1.1.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004718
>>> dataset = DS004718(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004738(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

sfb_meg_phantom (B04/C01)

Study:: ds004738 (OpenNeuro)
Author (year):: Bahners2023
Canonical:: —

Also importable as: DS004738, Bahners2023.

Modality: meg; Experiment type: Other; Subject type: Other. Subjects: 4; recordings: 25; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004738 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004738 DOI: https://doi.org/10.18112/openneuro.ds004738.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004738
>>> dataset = DS004738(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004745(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

8-Channel SSVEP EEG Dataset with Artifact Trials

Study:: ds004745 (OpenNeuro)
Author (year):: Kumaravel2023
Canonical:: —

Also importable as: DS004745, Kumaravel2023.

Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 6; recordings: 6; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004745 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004745 DOI: https://doi.org/10.18112/openneuro.ds004745.v1.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004745
>>> dataset = DS004745(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004752(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Dataset of intracranial EEG, scalp EEG and beamforming sources from epilepsy patients performing a verbal working memory task

Study:: ds004752 (OpenNeuro)
Author (year):: Dimakopoulos2023_intracranial
Canonical:: —

Also importable as: DS004752, Dimakopoulos2023_intracranial.

Modality: eeg, ieeg. Subjects: 15; recordings: 136; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004752 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004752 DOI: https://doi.org/10.18112/openneuro.ds004752.v1.0.1 NEMAR citation count: 4

Examples

>>> from eegdash.dataset import DS004752
>>> dataset = DS004752(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004770(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

iEEG on children during gameplay

Study:: ds004770 (OpenNeuro)
Author (year):: Ueda2023
Canonical:: —

Also importable as: DS004770, Ueda2023.

Modality: ieeg; Experiment type: Memory; Subject type: Epilepsy. Subjects: 10; recordings: 22; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004770 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004770 DOI: https://doi.org/10.18112/openneuro.ds004770.v1.0.0 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004770
>>> dataset = DS004770(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004771(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG/ERP data from a Python Reading Task

Study:: ds004771 (OpenNeuro)
Author (year):: Kuo2023
Canonical:: —

Also importable as: DS004771, Kuo2023.

Modality: eeg. Subjects: 61; recordings: 61; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004771 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004771 DOI: https://doi.org/10.18112/openneuro.ds004771.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004771
>>> dataset = DS004771(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004774(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Automatic Evoked Response Detection (ER-Detect) dataset

Study:: ds004774 (OpenNeuro)
Author (year):: Boom2023
Canonical:: ERDetect, ER_Detect

Also importable as: DS004774, Boom2023, ERDetect, ER_Detect.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 14; recordings: 14; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004774 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004774 DOI: https://doi.org/10.18112/openneuro.ds004774.v1.0.0

Examples

>>> from eegdash.dataset import DS004774
>>> dataset = DS004774(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['ERDetect', 'ER_Detect']

class eegdash.dataset.dataset.DS004784(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Phantom EEG Dataset with Motion, Muscle, and Eye Artifacts and Example Scripts

Study:: ds004784 (OpenNeuro)
Author (year):: Downey2023
Canonical:: —

Also importable as: DS004784, Downey2023.

Modality: eeg. Subjects: 1; recordings: 6; tasks: 6.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004784 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004784 DOI: https://doi.org/10.18112/openneuro.ds004784.v1.0.4 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004784
>>> dataset = DS004784(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004785(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG data for paper titled - Precise cortical contributions to feedback sensorimotor control during reactive balance

Study:: ds004785 (OpenNeuro)
Author (year):: Boebinger2023
Canonical:: —

Also importable as: DS004785, Boebinger2023.

Modality: eeg. Subjects: 17; recordings: 17; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004785 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004785 DOI: https://doi.org/10.18112/openneuro.ds004785.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004785
>>> dataset = DS004785(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004789(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Delayed Free Recall of Word Lists

Study:: ds004789 (OpenNeuro)
Author (year):: Herrema2023_Delayed_Free_Recall
Canonical:: —

Also importable as: DS004789, Herrema2023_Delayed_Free_Recall.

Modality: ieeg; Experiment type: Memory; Subject type: Epilepsy. Subjects: 273; recordings: 983; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004789 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004789 DOI: https://doi.org/10.18112/openneuro.ds004789.v3.1.0 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS004789
>>> dataset = DS004789(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004796(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A Polish Electroencephalography, Alzheimer’s Risk-genes, Lifestyle and Neuroimaging (PEARL-Neuro) Database

Study:: ds004796 (OpenNeuro)
Author (year):: Patrycja2023_Polish
Canonical:: PEARLNeuro

Also importable as: DS004796, Patrycja2023_Polish, PEARLNeuro.

Modality: eeg. Subjects: 79; recordings: 235; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004796 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004796 DOI: https://doi.org/10.18112/openneuro.ds004796.v1.1.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004796
>>> dataset = DS004796(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['PEARLNeuro']

class eegdash.dataset.dataset.DS004802(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Pilot data for Loneliness in the Brain: Distinguishing Between Hypersensitivity and Hyperalertness

Study:: ds004802 (OpenNeuro)
Author (year):: Bathelt2023
Canonical:: —

Also importable as: DS004802, Bathelt2023.

Modality: eeg. Subjects: 39; recordings: 79; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004802 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004802 DOI: https://doi.org/10.18112/openneuro.ds004802.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004802
>>> dataset = DS004802(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004809(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Categorized Free Recall: Delayed Free Recall of Word Lists Organized by Semantic Categories

Study:: ds004809 (OpenNeuro)
Author (year):: Herrema2023_Categorized_Free_Recall
Canonical:: catFR_Categorized_Free_Recall, CatFR

Also importable as: DS004809, Herrema2023_Categorized_Free_Recall, catFR_Categorized_Free_Recall, CatFR.

Modality: ieeg; Experiment type: Memory; Subject type: Epilepsy. Subjects: 252; recordings: 889; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004809 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004809 DOI: https://doi.org/10.18112/openneuro.ds004809.v2.2.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004809
>>> dataset = DS004809(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['catFR_Categorized_Free_Recall', 'CatFR']

class eegdash.dataset.dataset.DS004816(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG-attention-rsvp-exp1

Study:: ds004816 (OpenNeuro)
Author (year):: Grootswagers2023_E1
Canonical:: —

Also importable as: DS004816, Grootswagers2023_E1.

Modality: eeg. Subjects: 20; recordings: 20; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004816 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004816 DOI: https://doi.org/10.18112/openneuro.ds004816.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004816
>>> dataset = DS004816(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004817(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG-attention-rsvp-exp2

Study:: ds004817 (OpenNeuro)
Author (year):: Grootswagers2023_E2
Canonical:: —

Also importable as: DS004817, Grootswagers2023_E2.

Modality: eeg. Subjects: 20; recordings: 20; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004817 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004817 DOI: https://doi.org/10.18112/openneuro.ds004817.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004817
>>> dataset = DS004817(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004819(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Flexible, Scalable, High Channel Count Stereo-Electrode for Recording in the Human Brain

Study:: ds004819 (OpenNeuro)
Author (year):: Lee2023
Canonical:: —

Also importable as: DS004819, Lee2023.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Surgery. Subjects: 1; recordings: 8; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004819 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004819 DOI: https://doi.org/10.18112/openneuro.ds004819.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004819
>>> dataset = DS004819(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004830(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Spatial Attention Decoding using fNIRS During Complex Scene Analysis

Study:: ds004830 (OpenNeuro)
Author (year):: Ning2023
Canonical:: Ning2024

Also importable as: DS004830, Ning2023, Ning2024.

Modality: fnirs; Experiment type: Attention; Subject type: Healthy. Subjects: 12; recordings: 14; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004830 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004830 DOI: https://doi.org/10.18112/openneuro.ds004830.v2.0.0

Examples

>>> from eegdash.dataset import DS004830
>>> dataset = DS004830(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Ning2024']

class eegdash.dataset.dataset.DS004837(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Magnetoencephalographic (MEG) Pitch and Duration Mismatch Negativity (MMN) in First-Episode Psychosis

Study:: ds004837 (OpenNeuro)
Author (year):: LopezCaballero2023
Canonical:: —

Also importable as: DS004837, LopezCaballero2023.

Modality: meg; Experiment type: Perception; Subject type: Schizophrenia/Psychosis. Subjects: 60; recordings: 106; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004837 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004837 DOI: https://doi.org/10.18112/openneuro.ds004837.v1.0.2 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004837
>>> dataset = DS004837(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004840(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Dataset of electrophysiological signals (EEG, ECG, EMG) during Music therapy with adult burn patients in the Intensive Care Unit.

Study:: ds004840 (OpenNeuro)
Author (year):: CordobaSilva2023
Canonical:: —

Also importable as: DS004840, CordobaSilva2023.

Modality: eeg. Subjects: 9; recordings: 51; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004840 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004840 DOI: https://doi.org/10.18112/openneuro.ds004840.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004840
>>> dataset = DS004840(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004841(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

TX14

Study:: ds004841 (OpenNeuro)
Author (year):: Larkin2023_TX14
Canonical:: TX14

Also importable as: DS004841, Larkin2023_TX14, TX14.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 20; recordings: 147; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004841 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004841 DOI: https://doi.org/10.18112/openneuro.ds004841.v1.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004841
>>> dataset = DS004841(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['TX14']

class eegdash.dataset.dataset.DS004842(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

TX15

Study:: ds004842 (OpenNeuro)
Author (year):: Larkin2023_TX15
Canonical:: TX15

Also importable as: DS004842, Larkin2023_TX15, TX15.

Modality: eeg. Subjects: 14; recordings: 102; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004842 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004842 DOI: https://doi.org/10.18112/openneuro.ds004842.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004842
>>> dataset = DS004842(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['TX15']

class eegdash.dataset.dataset.DS004843(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

T16

Study:: ds004843 (OpenNeuro)
Author (year):: Johnson2023_T16
Canonical:: —

Also importable as: DS004843, Johnson2023_T16.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 14; recordings: 92; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004843 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004843 DOI: https://doi.org/10.18112/openneuro.ds004843.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004843
>>> dataset = DS004843(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004844(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

T22

Study:: ds004844 (OpenNeuro)
Author (year):: Metcalfe2023_T22
Canonical:: —

Also importable as: DS004844, Metcalfe2023_T22.

Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 17; recordings: 68; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004844 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004844 DOI: https://doi.org/10.18112/openneuro.ds004844.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004844
>>> dataset = DS004844(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004849(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

STRONG

Study:: ds004849 (OpenNeuro)
Author (year):: Johnson2023_STRONG
Canonical:: STRONG

Also importable as: DS004849, Johnson2023_STRONG, STRONG.

Modality: eeg; Experiment type: Memory; Subject type: Unknown. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004849 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004849 DOI: https://doi.org/10.18112/openneuro.ds004849.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004849
>>> dataset = DS004849(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['STRONG']

class eegdash.dataset.dataset.DS004850(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

ODE

Study:: ds004850 (OpenNeuro)
Author (year):: Johnson2023_ODE
Canonical:: Johnson2024

Also importable as: DS004850, Johnson2023_ODE, Johnson2024.

Modality: eeg; Experiment type: Memory; Subject type: Unknown. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004850 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004850 DOI: https://doi.org/10.18112/openneuro.ds004850.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004850
>>> dataset = DS004850(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Johnson2024']

class eegdash.dataset.dataset.DS004851(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

HID

Study:: ds004851 (OpenNeuro)
Author (year):: Johnson2023_HID
Canonical:: HID

Also importable as: DS004851, Johnson2023_HID, HID.

Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 66; recordings: 66; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004851 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004851 DOI: https://doi.org/10.18112/openneuro.ds004851.v2.1.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004851
>>> dataset = DS004851(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HID']

class eegdash.dataset.dataset.DS004852(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

InsurgentCivilian

Study:: ds004852 (OpenNeuro)
Author (year):: Johnson2023_InsurgentCivilian
Canonical:: Johnson2025

Also importable as: DS004852, Johnson2023_InsurgentCivilian, Johnson2025.

Modality: eeg; Experiment type: Memory; Subject type: Unknown. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004852 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004852 DOI: https://doi.org/10.18112/openneuro.ds004852.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004852
>>> dataset = DS004852(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Johnson2025']

class eegdash.dataset.dataset.DS004853(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

TX17

Study:: ds004853 (OpenNeuro)
Author (year):: Johnson2023_TX17
Canonical:: —

Also importable as: DS004853, Johnson2023_TX17.

Modality: eeg; Experiment type: Memory; Subject type: Unknown. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004853 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004853 DOI: https://doi.org/10.18112/openneuro.ds004853.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004853
>>> dataset = DS004853(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004854(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

TX18

Study:: ds004854 (OpenNeuro)
Author (year):: Johnson2023_TX18
Canonical:: TX18

Also importable as: DS004854, Johnson2023_TX18, TX18.

Modality: eeg; Experiment type: Memory; Subject type: Unknown. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004854 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004854 DOI: https://doi.org/10.18112/openneuro.ds004854.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004854
>>> dataset = DS004854(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['TX18']

class eegdash.dataset.dataset.DS004855(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

FT

Study:: ds004855 (OpenNeuro)
Author (year):: Johnson2023_FT
Canonical:: —

Also importable as: DS004855, Johnson2023_FT.

Modality: eeg; Experiment type: Memory; Subject type: Unknown. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004855 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004855 DOI: https://doi.org/10.18112/openneuro.ds004855.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004855
>>> dataset = DS004855(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004859(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

iEEG on children during Stroop task

Study:: ds004859 (OpenNeuro)
Author (year):: Sakakura2023_children_Stroop
Canonical:: Sakakura2024

Also importable as: DS004859, Sakakura2023_children_Stroop, Sakakura2024.

Modality: ieeg; Experiment type: Attention; Subject type: Unknown. Subjects: 7; recordings: 9; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004859 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004859 DOI: https://doi.org/10.18112/openneuro.ds004859.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004859
>>> dataset = DS004859(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Sakakura2024']

class eegdash.dataset.dataset.DS004860(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Investigating the cognitive conflict triggered by moral judgment of accidental harm : an event-related potentials study

Study:: ds004860 (OpenNeuro)
Author (year):: Schwartz2023
Canonical:: —

Also importable as: DS004860, Schwartz2023.

Modality: eeg. Subjects: 31; recordings: 31; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004860 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004860 DOI: https://doi.org/10.18112/openneuro.ds004860.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004860
>>> dataset = DS004860(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004865(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

pyFR: Delayed Free Recall of Word Lists, Preliminary Cognitive Electrophysiology Study

Study:: ds004865 (OpenNeuro)
Author (year):: Herrema2023_pyFR_Delayed_Free
Canonical:: pyFR

Also importable as: DS004865, Herrema2023_pyFR_Delayed_Free, pyFR.

Modality: ieeg; Experiment type: Memory; Subject type: Surgery. Subjects: 42; recordings: 172; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004865 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004865 DOI: https://doi.org/10.18112/openneuro.ds004865.v2.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004865
>>> dataset = DS004865(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['pyFR']

class eegdash.dataset.dataset.DS004883(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Registerd Report of ERN During Three Versions of a Flanker Task

Study:: ds004883 (OpenNeuro)
Author (year):: Clayson2023_Registerd
Canonical:: —

Also importable as: DS004883, Clayson2023_Registerd.

Modality: eeg. Subjects: 172; recordings: 516; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004883 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004883 DOI: https://doi.org/10.18112/openneuro.ds004883.v1.0.0 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS004883
>>> dataset = DS004883(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004902(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A Resting-state EEG Dataset for Sleep Deprivation

Study:: ds004902 (OpenNeuro)
Author (year):: Xiang2023
Canonical:: —

Also importable as: DS004902, Xiang2023.

Modality: eeg. Subjects: 71; recordings: 218; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004902 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004902 DOI: https://doi.org/10.18112/openneuro.ds004902.v1.0.8 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS004902
>>> dataset = DS004902(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004917(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Probability Decision-making Task with ambiguity

Study:: ds004917 (OpenNeuro)
Author (year):: FigueroaVargas2024
Canonical:: —

Also importable as: DS004917, FigueroaVargas2024.

Modality: eeg. Subjects: 24; recordings: 24; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004917 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004917 DOI: https://doi.org/10.18112/openneuro.ds004917.v1.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004917
>>> dataset = DS004917(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004929(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BallSqueezingHD

Study:: ds004929 (OpenNeuro)
Author (year):: Gao2024
Canonical:: —

Also importable as: DS004929, Gao2024.

Modality: fnirs; Experiment type: Motor; Subject type: Unknown. Subjects: 12; recordings: 36; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004929 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004929 DOI: https://doi.org/10.18112/openneuro.ds004929.v1.0.0

Examples

>>> from eegdash.dataset import DS004929
>>> dataset = DS004929(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004940(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Neurophysiological measures of covert semantic processing in neurotypical adolescents actively ignoring spoken sentence inputs: A high-density event-related potential (ERP) study.

Study:: ds004940 (OpenNeuro)
Author (year):: Toffolo2024
Canonical:: —

Also importable as: DS004940, Toffolo2024.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 22; recordings: 48; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004940 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004940 DOI: https://doi.org/10.18112/openneuro.ds004940.v1.0.1

Examples

>>> from eegdash.dataset import DS004940
>>> dataset = DS004940(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004942(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

SpatialMemory

Study:: ds004942 (OpenNeuro)
Author (year):: Kieffaber2024
Canonical:: —

Also importable as: DS004942, Kieffaber2024.

Modality: eeg. Subjects: 62; recordings: 62; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004942 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004942 DOI: https://doi.org/10.18112/openneuro.ds004942.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004942
>>> dataset = DS004942(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004944(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Dataset of BCI2000-compatible intraoperative ECoG with neuromorphic encoding

Study:: ds004944 (OpenNeuro)
Author (year):: Costa2024
Canonical:: BCI2000_intraop

Also importable as: DS004944, Costa2024, BCI2000_intraop.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 22; recordings: 44; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004944 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004944 DOI: https://doi.org/10.18112/openneuro.ds004944.v1.1.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004944
>>> dataset = DS004944(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BCI2000_intraop']

class eegdash.dataset.dataset.DS004951(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Braille letters - EEG

Study:: ds004951 (OpenNeuro)
Author (year):: Haupt2024_Braille
Canonical:: Haupt2025

Also importable as: DS004951, Haupt2024_Braille, Haupt2025.

Modality: eeg; Experiment type: Learning; Subject type: Other. Subjects: 11; recordings: 23; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004951 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004951 DOI: https://doi.org/10.18112/openneuro.ds004951.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004951
>>> dataset = DS004951(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Haupt2025']

class eegdash.dataset.dataset.DS004952(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

ChineseEEG: A Chinese Linguistic Corpora EEG Dataset for Semantic Alignment and Neural Decoding

Study:: ds004952 (OpenNeuro)
Author (year):: Mou2024
Canonical:: —

Also importable as: DS004952, Mou2024.

Modality: eeg. Subjects: 10; recordings: 245; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004952 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004952 DOI: https://doi.org/10.18112/openneuro.ds004952.v1.2.2 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004952
>>> dataset = DS004952(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004973(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

An fNIRS dataset for driving risk cognition of passengers in highly automated driving scenarios

Study:: ds004973 (OpenNeuro)
Author (year):: Zhang2024_driving_risk_cognition
Canonical:: —

Also importable as: DS004973, Zhang2024_driving_risk_cognition.

Modality: fnirs; Experiment type: Attention; Subject type: Healthy. Subjects: 20; recordings: 222; tasks: 12.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004973 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004973 DOI: https://doi.org/10.18112/openneuro.ds004973.v1.0.1

Examples

>>> from eegdash.dataset import DS004973
>>> dataset = DS004973(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004977(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

CARLA: Adjusted common average referencing for cortico-cortical evoked potential data

Study:: ds004977 (OpenNeuro)
Author (year):: Huang2024
Canonical:: CARLA

Also importable as: DS004977, Huang2024, CARLA.

Modality: ieeg; Experiment type: Other; Subject type: Epilepsy. Subjects: 4; recordings: 6; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004977 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004977 DOI: https://doi.org/10.18112/openneuro.ds004977.v1.2.0 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004977
>>> dataset = DS004977(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['CARLA']

class eegdash.dataset.dataset.DS004980(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG data set for a architectural affordances task

Study:: ds004980 (OpenNeuro)
Author (year):: Wang2024_architectural_affordances
Canonical:: —

Also importable as: DS004980, Wang2024_architectural_affordances.

Modality: eeg. Subjects: 17; recordings: 17; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004980 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004980 DOI: https://doi.org/10.18112/openneuro.ds004980.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004980
>>> dataset = DS004980(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS004993(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

WIRED ICM Sample Dataset - Workshop on Intracranial Recordings in Humans, Epilepsy, DBS

Study:: ds004993 (OpenNeuro)
Author (year):: Hamilton2024
Canonical:: WIRED_ICM

Also importable as: DS004993, Hamilton2024, WIRED_ICM.

Modality: ieeg; Experiment type: Perception; Subject type: Epilepsy. Subjects: 3; recordings: 3; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004993 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004993 DOI: https://doi.org/10.18112/openneuro.ds004993.v1.1.2 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004993
>>> dataset = DS004993(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['WIRED_ICM']

class eegdash.dataset.dataset.DS004995(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The Time-Course of Food Representation in the Human Brain

Study:: ds004995 (OpenNeuro)
Author (year):: Moerel2024
Canonical:: Moerel2023

Also importable as: DS004995, Moerel2024, Moerel2023.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 20; recordings: 20; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004995 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004995 DOI: https://doi.org/10.18112/openneuro.ds004995.v1.0.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004995
>>> dataset = DS004995(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Moerel2023']

class eegdash.dataset.dataset.DS004998(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Exploring the electrophysiology of Parkinson’s disease - magnetoencephalography combined with deep brain recordings from the subthalamic nucleus.

Study:: ds004998 (OpenNeuro)
Author (year):: Rassoulou2024
Canonical:: —

Also importable as: DS004998, Rassoulou2024.

Modality: meg; Experiment type: Motor; Subject type: Parkinson's. Subjects: 20; recordings: 145; tasks: 6.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004998 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004998 DOI: https://doi.org/10.18112/openneuro.ds004998.v1.2.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004998
>>> dataset = DS004998(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005007(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Auditory naming task with questions that begin or end with a wh-interrogative

Study:: ds005007 (OpenNeuro)
Author (year):: Kitazawa2024
Canonical:: Kitazawa2025

Also importable as: DS005007, Kitazawa2024, Kitazawa2025.

Modality: ieeg; Experiment type: Other; Subject type: Healthy. Subjects: 40; recordings: 42; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005007 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005007 DOI: https://doi.org/10.18112/openneuro.ds005007.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005007
>>> dataset = DS005007(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Kitazawa2025']

class eegdash.dataset.dataset.DS005021(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Tilt Illusion by Phase

Study:: ds005021 (OpenNeuro)
Author (year):: Williams2024
Canonical:: —

Also importable as: DS005021, Williams2024.

Modality: eeg. Subjects: 36; recordings: 36; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005021 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005021 DOI: https://doi.org/10.18112/openneuro.ds005021.v1.2.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005021
>>> dataset = DS005021(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005028(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Comparing P300 Flashing paradigms in online typing with language models

Study:: ds005028 (OpenNeuro)
Author (year):: Chandravadia2024
Canonical:: Chandravadia2022

Also importable as: DS005028, Chandravadia2024, Chandravadia2022.

Modality: eeg; Experiment type: Attention; Subject type: Unknown. Subjects: 11; recordings: 105; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005028 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005028 DOI: https://doi.org/10.18112/openneuro.ds005028.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005028
>>> dataset = DS005028(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Chandravadia2022']

class eegdash.dataset.dataset.DS005034(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The effect of theta tACS on working memory

Study:: ds005034 (OpenNeuro)
Author (year):: Pavlov2024_effect_theta_tACS
Canonical:: —

Also importable as: DS005034, Pavlov2024_effect_theta_tACS.

Modality: eeg. Subjects: 25; recordings: 100; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005034 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005034 DOI: https://doi.org/10.18112/openneuro.ds005034.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005034
>>> dataset = DS005034(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005048(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

40Hz Auditory Entrainment

Study:: ds005048 (OpenNeuro)
Author (year):: Lahijanian2024
Canonical:: —

Also importable as: DS005048, Lahijanian2024.

Modality: eeg. Subjects: 35; recordings: 35; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005048 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005048 DOI: https://doi.org/10.18112/openneuro.ds005048.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005048
>>> dataset = DS005048(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005059(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Paired Associates Learning: Memory for Word Pairs in Cued Recall

Study:: ds005059 (OpenNeuro)
Author (year):: Herrema2024_Paired
Canonical:: PAL

Also importable as: DS005059, Herrema2024_Paired, PAL.

Modality: ieeg; Experiment type: Memory; Subject type: Epilepsy. Subjects: 69; recordings: 282; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005059 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005059 DOI: https://doi.org/10.18112/openneuro.ds005059.v1.0.6 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005059
>>> dataset = DS005059(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['PAL']

class eegdash.dataset.dataset.DS005065(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Heuristics in risky decision-making relate to preferential representation of information MEG data

Study:: ds005065 (OpenNeuro)
Author (year):: Russek2024
Canonical:: —

Also importable as: DS005065, Russek2024.

Modality: meg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 21; recordings: 275; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005065 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005065 DOI: https://doi.org/10.18112/openneuro.ds005065.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005065
>>> dataset = DS005065(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005079(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The Effects of Directed Therapeutic Intent on Live and Damaged Cells

Study:: ds005079 (OpenNeuro)
Author (year):: Cohen2024
Canonical:: —

Also importable as: DS005079, Cohen2024.

Modality: eeg. Subjects: 1; recordings: 60; tasks: 15.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005079 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005079 DOI: https://doi.org/10.18112/openneuro.ds005079.v2.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005079
>>> dataset = DS005079(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005083(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Safety and Accuracy of Stereoelectroencephalography for Pediatric Patients with Prior Craniotomy

Study:: ds005083 (OpenNeuro)
Author (year):: Yang2024
Canonical:: —

Also importable as: DS005083, Yang2024.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Surgery. Subjects: 61; recordings: 1357; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005083 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005083 DOI: https://doi.org/10.18112/openneuro.ds005083.v1.0.0

Examples

>>> from eegdash.dataset import DS005083
>>> dataset = DS005083(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005087(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

rapid-hemifield-object-eeg

Study:: ds005087 (OpenNeuro)
Author (year):: Robinson2024_rapid
Canonical:: —

Also importable as: DS005087, Robinson2024_rapid.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 20; recordings: 60; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005087 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005087 DOI: https://doi.org/10.18112/openneuro.ds005087.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005087
>>> dataset = DS005087(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005089(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Proactive selective attention across competition contexts

Study:: ds005089 (OpenNeuro)
Author (year):: AguadoLopez2024
Canonical:: —

Also importable as: DS005089, AguadoLopez2024.

Modality: eeg. Subjects: 36; recordings: 36; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005089 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005089 DOI: https://doi.org/10.18112/openneuro.ds005089.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005089
>>> dataset = DS005089(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005095(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

STERNBERG DIFFICULT

Study:: ds005095 (OpenNeuro)
Author (year):: Zhozhikashvili2024
Canonical:: —

Also importable as: DS005095, Zhozhikashvili2024.

Modality: eeg. Subjects: 48; recordings: 48; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005095 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005095 DOI: https://doi.org/10.18112/openneuro.ds005095.v1.0.2 NEMAR citation count: 7

Examples

>>> from eegdash.dataset import DS005095
>>> dataset = DS005095(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005106(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

200 Objects Infants EEG

Study:: ds005106 (OpenNeuro)
Author (year):: Grootswagers2024
Canonical:: —

Also importable as: DS005106, Grootswagers2024.

Modality: eeg. Subjects: 42; recordings: 42; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005106 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005106 DOI: https://doi.org/10.18112/openneuro.ds005106.v1.5.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005106
>>> dataset = DS005106(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005107(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

FACE-DEC

Study:: ds005107 (OpenNeuro)
Author (year):: Xu2024_DEC
Canonical:: —

Also importable as: DS005107, Xu2024_DEC.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 21; recordings: 350; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005107 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005107 DOI: https://doi.org/10.18112/openneuro.ds005107.v2.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005107
>>> dataset = DS005107(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005114(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: DPX Cog Ctl Task in Acute Mild TBI

Study:: ds005114 (OpenNeuro)
Author (year):: Cavanagh2024
Canonical:: —

Also importable as: DS005114, Cavanagh2024.

Modality: eeg. Subjects: 91; recordings: 223; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005114 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005114 DOI: https://doi.org/10.18112/openneuro.ds005114.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005114
>>> dataset = DS005114(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005121(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Siefert2024

Study:: ds005121 (OpenNeuro)
Author (year):: Siefert2024
Canonical:: —

Also importable as: DS005121, Siefert2024.

Modality: eeg. Subjects: 34; recordings: 39; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005121 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005121 DOI: https://doi.org/10.18112/openneuro.ds005121.v1.0.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005121
>>> dataset = DS005121(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005131(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Evoked responses to elevated sounds

Study:: ds005131 (OpenNeuro)
Author (year):: Bialas2024
Canonical:: —

Also importable as: DS005131, Bialas2024.

Modality: eeg. Subjects: 58; recordings: 63; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005131 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005131 DOI: https://doi.org/10.18112/openneuro.ds005131.v1.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005131
>>> dataset = DS005131(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005169(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Dataset of intracranial EEG during cortical stimulation evoking visual effects

Study:: ds005169 (OpenNeuro)
Author (year):: Barborica2024
Canonical:: —

Also importable as: DS005169, Barborica2024.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 20; recordings: 112; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005169 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005169 DOI: https://doi.org/10.18112/openneuro.ds005169.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005169
>>> dataset = DS005169(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005170(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Chisco

Study:: ds005170 (OpenNeuro)
Author (year):: Zhang2024_Chisco
Canonical:: Chisco

Also importable as: DS005170, Zhang2024_Chisco, Chisco.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 5; recordings: 225; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005170 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005170 DOI: https://doi.org/10.18112/openneuro.ds005170.v1.1.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005170
>>> dataset = DS005170(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Chisco']

class eegdash.dataset.dataset.DS005178(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Ear-EEG Sleep Monitoring 2023 (EESM23)

Study:: ds005178 (OpenNeuro)
Author (year):: Tabar2024
Canonical:: EESM23

Also importable as: DS005178, Tabar2024, EESM23.

Modality: eeg; Experiment type: Sleep; Subject type: Healthy. Subjects: 10; recordings: 140; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005178 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005178 DOI: https://doi.org/10.18112/openneuro.ds005178.v1.0.0

Examples

>>> from eegdash.dataset import DS005178
>>> dataset = DS005178(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['EESM23']

class eegdash.dataset.dataset.DS005185(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Ear-EEG Sleep Monitoring 2019 (EESM19)

Study:: ds005185 (OpenNeuro)
Author (year):: Mikkelsen2024_Ear_Sleep_Monitoring
Canonical:: EESM19

Also importable as: DS005185, Mikkelsen2024_Ear_Sleep_Monitoring, EESM19.

Modality: eeg; Experiment type: Sleep; Subject type: Healthy. Subjects: 20; recordings: 356; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005185 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005185 DOI: https://doi.org/10.18112/openneuro.ds005185.v1.0.2

Examples

>>> from eegdash.dataset import DS005185
>>> dataset = DS005185(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['EESM19']

class eegdash.dataset.dataset.DS005189(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Search Superiority Recollection Familiarity

Study:: ds005189 (OpenNeuro)
Author (year):: Helbing2024
Canonical:: —

Also importable as: DS005189, Helbing2024.

Modality: eeg. Subjects: 30; recordings: 30; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005189 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005189 DOI: https://doi.org/10.18112/openneuro.ds005189.v1.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005189
>>> dataset = DS005189(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005207(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Surrey cEEGrid sleep data set

Study:: ds005207 (OpenNeuro)
Author (year):: Mikkelsen2024_Surrey_cEEGrid_sleep
Canonical:: Surrey_cEEGrid_sleep

Also importable as: DS005207, Mikkelsen2024_Surrey_cEEGrid_sleep, Surrey_cEEGrid_sleep.

Modality: eeg. Subjects: 20; recordings: 39; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005207 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005207 DOI: https://doi.org/10.18112/openneuro.ds005207.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005207
>>> dataset = DS005207(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Surrey_cEEGrid_sleep']

class eegdash.dataset.dataset.DS005241(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

NeuroMorph: A High-Temporal Resolution MEG Dataset for Morpheme-Based Linguistic Analysis

Study:: ds005241 (OpenNeuro)
Author (year):: Rodriguez2024
Canonical:: NeuroMorph, neuromorph

Also importable as: DS005241, Rodriguez2024, NeuroMorph, neuromorph.

Modality: meg; Experiment type: Other; Subject type: Healthy. Subjects: 24; recordings: 117; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005241 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005241 DOI: https://doi.org/10.18112/openneuro.ds005241.v1.1.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005241
>>> dataset = DS005241(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['NeuroMorph', 'neuromorph']

class eegdash.dataset.dataset.DS005261(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Gloups_MEG

Study:: ds005261 (OpenNeuro)
Author (year):: Todorovic2024
Canonical:: Todorovic2023

Also importable as: DS005261, Todorovic2024, Todorovic2023.

Modality: meg; Experiment type: Learning; Subject type: Healthy. Subjects: 17; recordings: 128; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005261 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005261 DOI: https://doi.org/10.18112/openneuro.ds005261.v3.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005261
>>> dataset = DS005261(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Todorovic2023']

class eegdash.dataset.dataset.DS005262(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

ArEEG: Arabic Inner Speech EEG dataset

Study:: ds005262 (OpenNeuro)
Author (year):: Metwalli2024
Canonical:: ArEEG

Also importable as: DS005262, Metwalli2024, ArEEG.

Modality: eeg. Subjects: 12; recordings: 186; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005262 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005262 DOI: https://doi.org/10.18112/openneuro.ds005262.v1.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005262
>>> dataset = DS005262(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['ArEEG']

class eegdash.dataset.dataset.DS005273(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Neural representation of consciously seen and unseen information

Study:: ds005273 (OpenNeuro)
Author (year):: Esteban2024
Canonical:: —

Also importable as: DS005273, Esteban2024.

Modality: eeg. Subjects: 33; recordings: 33; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005273 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005273 DOI: https://doi.org/10.18112/openneuro.ds005273.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005273
>>> dataset = DS005273(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005274(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

UV_EEG

Study:: ds005274 (OpenNeuro)
Author (year):: Ito2024
Canonical:: —

Also importable as: DS005274, Ito2024.

Modality: eeg; Experiment type: Unknown; Subject type: Healthy. Subjects: 22; recordings: 22; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005274 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005274 DOI: https://doi.org/10.18112/openneuro.ds005274.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005274
>>> dataset = DS005274(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005279(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Picture-Word Interference Dataset

Study:: ds005279 (OpenNeuro)
Author (year):: Wei2024
Canonical:: —

Also importable as: DS005279, Wei2024.

Modality: meg; Experiment type: Other; Subject type: Healthy. Subjects: 30; recordings: 90; tasks: 0.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005279 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005279 DOI: https://doi.org/10.18112/openneuro.ds005279.v1.0.3 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005279
>>> dataset = DS005279(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005280(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

223 By BP

Study:: ds005280 (OpenNeuro)
Author (year):: Xiangyue2024_223_BP
Canonical:: —

Also importable as: DS005280, Xiangyue2024_223_BP.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 223; recordings: 669; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005280 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005280 DOI: https://doi.org/10.18112/openneuro.ds005280.v1.0.0

Examples

>>> from eegdash.dataset import DS005280
>>> dataset = DS005280(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005284(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

26 By Biosemi

Study:: ds005284 (OpenNeuro)
Author (year):: Xiangyue2024_26_Biosemi
Canonical:: —

Also importable as: DS005284, Xiangyue2024_26_Biosemi.

Modality: eeg; Experiment type: Unknown; Subject type: Healthy. Subjects: 26; recordings: 26; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005284 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005284 DOI: https://doi.org/10.18112/openneuro.ds005284.v1.0.0

Examples

>>> from eegdash.dataset import DS005284
>>> dataset = DS005284(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005285(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

29 By ANT

Study:: ds005285 (OpenNeuro)
Author (year):: Xiangyue2024_29_ANT
Canonical:: —

Also importable as: DS005285, Xiangyue2024_29_ANT.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 29; recordings: 116; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005285 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005285 DOI: https://doi.org/10.18112/openneuro.ds005285.v1.0.0

Examples

>>> from eegdash.dataset import DS005285
>>> dataset = DS005285(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005286(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

30 By ANT

Study:: ds005286 (OpenNeuro)
Author (year):: Xiangyue2024_30_ANT
Canonical:: —

Also importable as: DS005286, Xiangyue2024_30_ANT.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 30; recordings: 30; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005286 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005286 DOI: https://doi.org/10.18112/openneuro.ds005286.v1.0.0

Examples

>>> from eegdash.dataset import DS005286
>>> dataset = DS005286(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005289(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

39 By BP

Study:: ds005289 (OpenNeuro)
Author (year):: Xiangyue2024_39_BP
Canonical:: —

Also importable as: DS005289, Xiangyue2024_39_BP.

Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 39; recordings: 195; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005289 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005289 DOI: https://doi.org/10.18112/openneuro.ds005289.v1.0.0

Examples

>>> from eegdash.dataset import DS005289
>>> dataset = DS005289(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005291(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

65 By ANT

Study:: ds005291 (OpenNeuro)
Author (year):: Xiangyue2024_65_ANT
Canonical:: —

Also importable as: DS005291, Xiangyue2024_65_ANT.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 65; recordings: 65; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005291 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005291 DOI: https://doi.org/10.18112/openneuro.ds005291.v1.0.0

Examples

>>> from eegdash.dataset import DS005291
>>> dataset = DS005291(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005292(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

142 by Biosemi

Study:: ds005292 (OpenNeuro)
Author (year):: Xiangyue2024_142_Biosemi
Canonical:: —

Also importable as: DS005292, Xiangyue2024_142_Biosemi.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 142; recordings: 426; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005292 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005292 DOI: https://doi.org/10.18112/openneuro.ds005292.v1.0.0

Examples

>>> from eegdash.dataset import DS005292
>>> dataset = DS005292(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005293(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

95 By BP

Study:: ds005293 (OpenNeuro)
Author (year):: Xiangyue2024_95_BP
Canonical:: —

Also importable as: DS005293, Xiangyue2024_95_BP.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 95; recordings: 570; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005293 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005293 DOI: https://doi.org/10.18112/openneuro.ds005293.v1.0.0

Examples

>>> from eegdash.dataset import DS005293
>>> dataset = DS005293(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005296(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Assessing sensitivity to semantic and syntactic information in deaf readers: An ERP study

Study:: ds005296 (OpenNeuro)
Author (year):: Emmorey2024
Canonical:: —

Also importable as: DS005296, Emmorey2024.

Modality: eeg. Subjects: 62; recordings: 62; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005296 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005296 DOI: https://doi.org/10.18112/openneuro.ds005296.v1.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005296
>>> dataset = DS005296(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005305(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG Resting-state Microstates Correlates of Executive Functions

Study:: ds005305 (OpenNeuro)
Author (year):: Quentin2024
Canonical:: —

Also importable as: DS005305, Quentin2024.

Modality: eeg. Subjects: 165; recordings: 165; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005305 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005305 DOI: https://doi.org/10.18112/openneuro.ds005305.v1.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005305
>>> dataset = DS005305(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005307(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Laser-evoked potentials in the human spinal cord and cortex

Study:: ds005307 (OpenNeuro)
Author (year):: Nierula2024
Canonical:: Nierula2019

Also importable as: DS005307, Nierula2024, Nierula2019.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 7; recordings: 73; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005307 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005307 DOI: https://doi.org/10.18112/openneuro.ds005307.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005307
>>> dataset = DS005307(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Nierula2019']

class eegdash.dataset.dataset.DS005340(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Fundamental frequency predominantly drives talker differences in auditory brainstem responses to continuous speech

Study:: ds005340 (OpenNeuro)
Author (year):: Polonenko2024_Fundamental
Canonical:: —

Also importable as: DS005340, Polonenko2024_Fundamental.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 15; recordings: 15; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005340 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005340 DOI: https://doi.org/10.18112/openneuro.ds005340.v1.0.4 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005340
>>> dataset = DS005340(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005342(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG data offline and online during motor imagery for standing and sitting

Study:: ds005342 (OpenNeuro)
Author (year):: TrianaGuzman2024
Canonical:: —

Also importable as: DS005342, TrianaGuzman2024.

Modality: eeg. Subjects: 32; recordings: 32; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005342 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005342 DOI: https://doi.org/10.18112/openneuro.ds005342.v1.0.3 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005342
>>> dataset = DS005342(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005343(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Gaffrey Lab Infant Microstates and Attention

Study:: ds005343 (OpenNeuro)
Author (year):: Bagdasarov2024
Canonical:: —

Also importable as: DS005343, Bagdasarov2024.

Modality: eeg; Experiment type: Perception; Subject type: Development. Subjects: 43; recordings: 43; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005343 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005343 DOI: https://doi.org/10.18112/openneuro.ds005343.v1.0.0

Examples

>>> from eegdash.dataset import DS005343
>>> dataset = DS005343(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005345(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Le Petit Prince (LPP) Multi-talker: Naturalistic 7T fMRI and EEG Dataset

Study:: ds005345 (OpenNeuro)
Author (year):: Ma2024
Canonical:: LPP

Also importable as: DS005345, Ma2024, LPP.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 26; recordings: 26; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005345 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005345 DOI: https://doi.org/10.18112/openneuro.ds005345.v1.0.1

Examples

>>> from eegdash.dataset import DS005345
>>> dataset = DS005345(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['LPP']

class eegdash.dataset.dataset.DS005346(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Naturalistic fMRI and MEG recordings during viewing of a reality TV show

Study:: ds005346 (OpenNeuro)
Author (year):: Li2024_Naturalistic_fMRI_viewing
Canonical:: —

Also importable as: DS005346, Li2024_Naturalistic_fMRI_viewing.

Modality: meg; Experiment type: Memory; Subject type: Healthy. Subjects: 30; recordings: 90; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005346 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005346 DOI: https://doi.org/10.18112/openneuro.ds005346.v1.0.5

Examples

>>> from eegdash.dataset import DS005346
>>> dataset = DS005346(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005356(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

MEG: Major Depression & Probabilistic Learning Task

Study:: ds005356 (OpenNeuro)
Author (year):: DS5356_MajorDepression
Canonical:: —

Also importable as: DS005356, DS5356_MajorDepression.

Modality: meg; Experiment type: Learning; Subject type: Depression. Subjects: 85; recordings: 116; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005356 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005356 DOI: https://doi.org/10.18112/openneuro.ds005356.v1.5.0

Examples

>>> from eegdash.dataset import DS005356
>>> dataset = DS005356(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005363(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Object recognition in healthy aging (ORHA) - EEG

Study:: ds005363 (OpenNeuro)
Author (year):: Haupt2024_Object
Canonical:: ORHA

Also importable as: DS005363, Haupt2024_Object, ORHA.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 43; recordings: 43; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005363 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005363 DOI: https://doi.org/10.18112/openneuro.ds005363.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005363
>>> dataset = DS005363(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['ORHA']

class eegdash.dataset.dataset.DS005383(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments

Study:: ds005383 (OpenNeuro)
Author (year):: Bai2024
Canonical:: TMNRED

Also importable as: DS005383, Bai2024, TMNRED.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 30; recordings: 240; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005383 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005383 DOI: https://doi.org/10.18112/openneuro.ds005383.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005383
>>> dataset = DS005383(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['TMNRED']

class eegdash.dataset.dataset.DS005385(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Resting-state EEG data before and after cognitive activity across the adult lifespan and a 5-year follow-up

Study:: ds005385 (OpenNeuro)
Author (year):: Wascher2024
Canonical:: —

Also importable as: DS005385, Wascher2024.

Modality: eeg; Experiment type: Resting-state; Subject type: Healthy. Subjects: 608; recordings: 3264; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005385 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005385 DOI: https://doi.org/10.18112/openneuro.ds005385.v1.0.3 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005385
>>> dataset = DS005385(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005397(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Affordances of stairs

Study:: ds005397 (OpenNeuro)
Author (year):: Hilton2024
Canonical:: —

Also importable as: DS005397, Hilton2024.

Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 26; recordings: 26; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005397 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005397 DOI: https://doi.org/10.18112/openneuro.ds005397.v1.0.4 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005397
>>> dataset = DS005397(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005398(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Open iEEG Dataset (Pediatric iEEG, Wayne State University and UCLA)

Study:: ds005398 (OpenNeuro)
Author (year):: Zhang2024_Open_Pediatric_Wayne
Canonical:: —

Also importable as: DS005398, Zhang2024_Open_Pediatric_Wayne.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 185; recordings: 185; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005398 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005398 DOI: https://doi.org/10.18112/openneuro.ds005398.v1.1.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005398
>>> dataset = DS005398(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005403(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Delayed Auditory Feedback EEG/EGG

Study:: ds005403 (OpenNeuro)
Author (year):: Veillette2024
Canonical:: Veillette2019

Also importable as: DS005403, Veillette2024, Veillette2019.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 32; recordings: 32; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005403 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005403 DOI: https://doi.org/10.18112/openneuro.ds005403.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005403
>>> dataset = DS005403(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Veillette2019']

class eegdash.dataset.dataset.DS005406(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG frequency tagging reveals the integration of dissimilar observed actions

Study:: ds005406 (OpenNeuro)
Author (year):: Formica2024
Canonical:: Formica2025

Also importable as: DS005406, Formica2024, Formica2025.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 29; recordings: 29; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005406 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005406 DOI: https://doi.org/10.18112/openneuro.ds005406.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005406
>>> dataset = DS005406(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Formica2025']

class eegdash.dataset.dataset.DS005407(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The effect of speech masking on the subcortical response to speech

Study:: ds005407 (OpenNeuro)
Author (year):: Polonenko2024_effect
Canonical:: —

Also importable as: DS005407, Polonenko2024_effect.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 25; recordings: 29; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005407 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005407 DOI: https://doi.org/10.18112/openneuro.ds005407.v1.0.1

Examples

>>> from eegdash.dataset import DS005407
>>> dataset = DS005407(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005408(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The effect of speech masking on the subcortical response to speech

Study:: ds005408 (OpenNeuro)
Author (year):: Polonenko2024_effect_speech
Canonical:: —

Also importable as: DS005408, Polonenko2024_effect_speech.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 25; recordings: 29; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005408 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005408 DOI: https://doi.org/10.18112/openneuro.ds005408.v1.0.0

Examples

>>> from eegdash.dataset import DS005408
>>> dataset = DS005408(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005410(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Semantic_conditioning

Study:: ds005410 (OpenNeuro)
Author (year):: Pavlov2024_Semantic_conditioning
Canonical:: —

Also importable as: DS005410, Pavlov2024_Semantic_conditioning.

Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 81; recordings: 81; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005410 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005410 DOI: https://doi.org/10.18112/openneuro.ds005410.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005410
>>> dataset = DS005410(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005411(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Free Recall of Word Lists with Repeated Items

Study:: ds005411 (OpenNeuro)
Author (year):: Herrema2024_Free
Canonical:: —

Also importable as: DS005411, Herrema2024_Free.

Modality: ieeg; Experiment type: Memory; Subject type: Epilepsy. Subjects: 47; recordings: 193; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005411 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005411 DOI: https://doi.org/10.18112/openneuro.ds005411.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005411
>>> dataset = DS005411(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005415(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Numbers

Study:: ds005415 (OpenNeuro)
Author (year):: Rockhill2024
Canonical:: —

Also importable as: DS005415, Rockhill2024.

Modality: ieeg; Experiment type: Perception; Subject type: Epilepsy. Subjects: 13; recordings: 13; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005415 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005415 DOI: https://doi.org/10.18112/openneuro.ds005415.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005415
>>> dataset = DS005415(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005416(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Fatigue Characterization of EEG under Mixed Reality Stereo Vision

Study:: ds005416 (OpenNeuro)
Author (year):: Wu2024
Canonical:: —

Also importable as: DS005416, Wu2024.

Modality: eeg; Experiment type: Resting-state; Subject type: Healthy. Subjects: 23; recordings: 23; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005416 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005416 DOI: https://doi.org/10.18112/openneuro.ds005416.v1.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005416
>>> dataset = DS005416(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005420(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Resting state EEG with closed eyes and open eyes in females from 60 to 80 years old

Study:: ds005420 (OpenNeuro)
Author (year):: Gama2024
Canonical:: Gama2019

Also importable as: DS005420, Gama2024, Gama2019.

Modality: eeg; Experiment type: Resting-state; Subject type: Healthy. Subjects: 37; recordings: 72; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005420 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005420 DOI: https://doi.org/10.18112/openneuro.ds005420.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005420
>>> dataset = DS005420(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Gama2019']

class eegdash.dataset.dataset.DS005429(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Auditory oddball comparison (Optimum-1, Learning-oddball, and the local–global paradigm)

Study:: ds005429 (OpenNeuro)
Author (year):: Rutiku2024
Canonical:: —

Also importable as: DS005429, Rutiku2024.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 15; recordings: 61; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005429 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005429 DOI: https://doi.org/10.18112/openneuro.ds005429.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005429
>>> dataset = DS005429(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005448(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

STReEF

Study:: ds005448 (OpenNeuro)
Author (year):: Jelsma2024
Canonical:: STReEF

Also importable as: DS005448, Jelsma2024, STReEF.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 13; recordings: 18; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005448 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005448 DOI: https://doi.org/10.18112/openneuro.ds005448.v1.0.0

Examples

>>> from eegdash.dataset import DS005448
>>> dataset = DS005448(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['STReEF']

class eegdash.dataset.dataset.DS005473(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

29 By BP

Study:: ds005473 (OpenNeuro)
Author (year):: Xiangyue2024_29_BP
Canonical:: Zhao2024

Also importable as: DS005473, Xiangyue2024_29_BP, Zhao2024.

Modality: eeg; Experiment type: Unknown; Subject type: Healthy. Subjects: 29; recordings: 58; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005473 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005473 DOI: https://doi.org/10.18112/openneuro.ds005473.v1.0.0

Examples

>>> from eegdash.dataset import DS005473
>>> dataset = DS005473(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Zhao2024']

class eegdash.dataset.dataset.DS005486(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

PREDICT

Study:: ds005486 (OpenNeuro)
Author (year):: Chowdhury2024
Canonical:: —

Also importable as: DS005486, Chowdhury2024.

Modality: eeg; Experiment type: Resting-state; Subject type: Unknown. Subjects: 159; recordings: 445; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005486 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005486 DOI: https://doi.org/10.18112/openneuro.ds005486.v1.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005486
>>> dataset = DS005486(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005489(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Free Recall with Open-Loop Stimulation at Encoding

Study:: ds005489 (OpenNeuro)
Author (year):: Herrema2024_Free_Recall
Canonical:: —

Also importable as: DS005489, Herrema2024_Free_Recall.

Modality: ieeg; Experiment type: Memory; Subject type: Unknown. Subjects: 37; recordings: 154; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005489 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005489 DOI: https://doi.org/10.18112/openneuro.ds005489.v1.0.3 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005489
>>> dataset = DS005489(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005491(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Categorized Free Recall with Open-Loop Stimulation at Encoding

Study:: ds005491 (OpenNeuro)
Author (year):: Herrema2024_Categorized
Canonical:: catFR_open_loop, RAM_catFR, catFR_stim

Also importable as: DS005491, Herrema2024_Categorized, catFR_open_loop, RAM_catFR, catFR_stim.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Unknown. Subjects: 19; recordings: 51; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005491 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005491 DOI: https://doi.org/10.18112/openneuro.ds005491.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005491
>>> dataset = DS005491(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['catFR_open_loop', 'RAM_catFR', 'catFR_stim']

class eegdash.dataset.dataset.DS005494(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Cued Recall of Paired Associates with Open-Loop Stimulation at Encoding or Retrieval

Study:: ds005494 (OpenNeuro)
Author (year):: Herrema2024_Cued
Canonical:: Herrema2024

Also importable as: DS005494, Herrema2024_Cued, Herrema2024.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Unknown. Subjects: 20; recordings: 51; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005494 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005494 DOI: https://doi.org/10.18112/openneuro.ds005494.v1.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005494
>>> dataset = DS005494(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Herrema2024']

class eegdash.dataset.dataset.DS005505(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 1

Study:: ds005505 (OpenNeuro)
Author (year):: Shirazi2024_R1
Canonical:: HBN_r1

Also importable as: DS005505, Shirazi2024_R1, HBN_r1.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 136; recordings: 1342; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005505 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005505 DOI: https://doi.org/10.18112/openneuro.ds005505.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005505
>>> dataset = DS005505(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r1']

class eegdash.dataset.dataset.DS005506(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 2

Study:: ds005506 (OpenNeuro)
Author (year):: Shirazi2024_R2
Canonical:: HBN_r2

Also importable as: DS005506, Shirazi2024_R2, HBN_r2.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 150; recordings: 1405; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005506 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005506 DOI: https://doi.org/10.18112/openneuro.ds005506.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005506
>>> dataset = DS005506(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r2']

class eegdash.dataset.dataset.DS005507(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 3

Study:: ds005507 (OpenNeuro)
Author (year):: Shirazi2024_R3
Canonical:: HBN_r3

Also importable as: DS005507, Shirazi2024_R3, HBN_r3.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 184; recordings: 1812; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005507 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005507 DOI: https://doi.org/10.18112/openneuro.ds005507.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005507
>>> dataset = DS005507(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r3']

class eegdash.dataset.dataset.DS005508(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 4

Study:: ds005508 (OpenNeuro)
Author (year):: Shirazi2024_R4
Canonical:: HBN_r4

Also importable as: DS005508, Shirazi2024_R4, HBN_r4.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 324; recordings: 3342; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005508 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005508 DOI: https://doi.org/10.18112/openneuro.ds005508.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005508
>>> dataset = DS005508(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r4']

class eegdash.dataset.dataset.DS005509(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 5

Study:: ds005509 (OpenNeuro)
Author (year):: Shirazi2024_R5
Canonical:: HBN_r5

Also importable as: DS005509, Shirazi2024_R5, HBN_r5.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 330; recordings: 3326; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005509 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005509 DOI: https://doi.org/10.18112/openneuro.ds005509.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005509
>>> dataset = DS005509(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r5']

class eegdash.dataset.dataset.DS005510(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 6

Study:: ds005510 (OpenNeuro)
Author (year):: Shirazi2024_R6
Canonical:: HBN_r6

Also importable as: DS005510, Shirazi2024_R6, HBN_r6.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 135; recordings: 1227; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005510 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005510 DOI: https://doi.org/10.18112/openneuro.ds005510.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005510
>>> dataset = DS005510(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r6']

class eegdash.dataset.dataset.DS005512(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 8

Study:: ds005512 (OpenNeuro)
Author (year):: Shirazi2024_R8
Canonical:: HBN_r8

Also importable as: DS005512, Shirazi2024_R8, HBN_r8.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 257; recordings: 2320; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005512 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005512 DOI: https://doi.org/10.18112/openneuro.ds005512.v1.0.1 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS005512
>>> dataset = DS005512(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r8']

class eegdash.dataset.dataset.DS005514(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 9

Study:: ds005514 (OpenNeuro)
Author (year):: Shirazi2024_R9
Canonical:: HBN_r9

Also importable as: DS005514, Shirazi2024_R9, HBN_r9.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 295; recordings: 2885; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005514 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005514 DOI: https://doi.org/10.18112/openneuro.ds005514.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005514
>>> dataset = DS005514(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r9']

class eegdash.dataset.dataset.DS005515(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 10

Study:: ds005515 (OpenNeuro)
Author (year):: Shirazi2024_R10
Canonical:: HBN_r10

Also importable as: DS005515, Shirazi2024_R10, HBN_r10.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 533; recordings: 2516; tasks: 8.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005515 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005515 DOI: https://doi.org/10.18112/openneuro.ds005515.v1.0.1

Examples

>>> from eegdash.dataset import DS005515
>>> dataset = DS005515(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r10']

class eegdash.dataset.dataset.DS005516(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 11

Study:: ds005516 (OpenNeuro)
Author (year):: Shirazi2024_R11
Canonical:: HBN_r11

Also importable as: DS005516, Shirazi2024_R11, HBN_r11.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 430; recordings: 3397; tasks: 8.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005516 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005516 DOI: https://doi.org/10.18112/openneuro.ds005516.v1.0.1

Examples

>>> from eegdash.dataset import DS005516
>>> dataset = DS005516(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r11']

class eegdash.dataset.dataset.DS005520(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Research data supporting ‘EEG recording during playing MOBA game’

Study:: ds005520 (OpenNeuro)
Author (year):: Li2024_Research_supporting_playing
Canonical:: —

Also importable as: DS005520, Li2024_Research_supporting_playing.

Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 23; recordings: 69; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005520 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005520 DOI: https://doi.org/10.18112/openneuro.ds005520.v1.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005520
>>> dataset = DS005520(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005522(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Spatial Navigation Memory of Object Locations

Study:: ds005522 (OpenNeuro)
Author (year):: Herrema2024_Spatial
Canonical:: —

Also importable as: DS005522, Herrema2024_Spatial.

Modality: ieeg; Experiment type: Memory; Subject type: Unknown. Subjects: 55; recordings: 176; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005522 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005522 DOI: https://doi.org/10.18112/openneuro.ds005522.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005522
>>> dataset = DS005522(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005523(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Spatial Memory of Object Locations with Open-Loop Stimulation at Encoding

Study:: ds005523 (OpenNeuro)
Author (year):: Herrema2024_Spatial_Memory
Canonical:: —

Also importable as: DS005523, Herrema2024_Spatial_Memory.

Modality: ieeg; Experiment type: Memory; Subject type: Surgery. Subjects: 21; recordings: 102; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005523 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005523 DOI: https://doi.org/10.18112/openneuro.ds005523.v1.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005523
>>> dataset = DS005523(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005530(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Depotentiation of emotional reactivity using TMR during REM sleep

Study:: ds005530 (OpenNeuro)
Author (year):: Greco2024
Canonical:: —

Also importable as: DS005530, Greco2024.

Modality: eeg; Experiment type: Sleep; Subject type: Healthy. Subjects: 17; recordings: 21; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005530 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005530 DOI: https://doi.org/10.18112/openneuro.ds005530.v1.0.9 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005530
>>> dataset = DS005530(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005540(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EmoEEG-MC: A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding

Study:: ds005540 (OpenNeuro)
Author (year):: Xin2024
Canonical:: —

Also importable as: DS005540, Xin2024.

Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 59; recordings: 103; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005540 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005540 DOI: https://doi.org/10.18112/openneuro.ds005540.v1.0.7 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005540
>>> dataset = DS005540(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005545(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Auditory naming

Study:: ds005545 (OpenNeuro)
Author (year):: Kanno2024
Canonical:: Kanno2025

Also importable as: DS005545, Kanno2024, Kanno2025.

Modality: ieeg; Experiment type: Other; Subject type: Surgery. Subjects: 106; recordings: 336; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005545 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005545 DOI: https://doi.org/10.18112/openneuro.ds005545.v1.0.3 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005545
>>> dataset = DS005545(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Kanno2025']

class eegdash.dataset.dataset.DS005555(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The Bitbrain Open Access Sleep (BOAS) dataset

Study:: ds005555 (OpenNeuro)
Author (year):: LopezLarraz2024
Canonical:: BOAS

Also importable as: DS005555, LopezLarraz2024, BOAS.

Modality: eeg; Experiment type: Sleep; Subject type: Healthy. Subjects: 128; recordings: 256; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005555 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005555 DOI: https://doi.org/10.18112/openneuro.ds005555.v1.1.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005555
>>> dataset = DS005555(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BOAS']

class eegdash.dataset.dataset.DS005557(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Free Recall with Closed-Loop Stimulation at Encoding (Encoding Classifier)

Study:: ds005557 (OpenNeuro)
Author (year):: Herrema2024_Classifier
Canonical:: —

Also importable as: DS005557, Herrema2024_Classifier.

Modality: ieeg; Experiment type: Memory; Subject type: Other. Subjects: 16; recordings: 58; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005557 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005557 DOI: https://doi.org/10.18112/openneuro.ds005557.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005557
>>> dataset = DS005557(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005558(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Categorized Free Recall with Closed-Loop Stimulation at Encoding (Encoding Classifier)

Study:: ds005558 (OpenNeuro)
Author (year):: Herrema2024_Categorized_Free
Canonical:: catFR_closed_loop

Also importable as: DS005558, Herrema2024_Categorized_Free, catFR_closed_loop.

Modality: ieeg; Experiment type: Memory; Subject type: Surgery. Subjects: 7; recordings: 22; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005558 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005558 DOI: https://doi.org/10.18112/openneuro.ds005558.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005558
>>> dataset = DS005558(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['catFR_closed_loop']

class eegdash.dataset.dataset.DS005565(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Neural associations between fingerspelling, print, and signs: An ERP priming study with deaf readers

Study:: ds005565 (OpenNeuro)
Author (year):: Lee2024_StudyWITH
Canonical:: —

Also importable as: DS005565, Lee2024_StudyWITH.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 24; recordings: 24; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005565 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005565 DOI: https://doi.org/10.18112/openneuro.ds005565.v1.0.3 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005565
>>> dataset = DS005565(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005571(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Expectation of Conflict Stimuli

Study:: ds005571 (OpenNeuro)
Author (year):: MartinezMolina2024
Canonical:: —

Also importable as: DS005571, MartinezMolina2024.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 24; recordings: 45; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005571 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005571 DOI: https://doi.org/10.18112/openneuro.ds005571.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005571
>>> dataset = DS005571(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005574(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The “Podcast” ECoG dataset

Study:: ds005574 (OpenNeuro)
Author (year):: Zada2024
Canonical:: Podcast

Also importable as: DS005574, Zada2024, Podcast.

Modality: ieeg; Experiment type: Other; Subject type: Unknown. Subjects: 9; recordings: 9; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005574 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005574 DOI: https://doi.org/10.18112/openneuro.ds005574.v1.0.2

Examples

>>> from eegdash.dataset import DS005574
>>> dataset = DS005574(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Podcast']

class eegdash.dataset.dataset.DS005586(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Electroencephalographic responses to the number of objects in partially occluded and uncovered scenes

Study:: ds005586 (OpenNeuro)
Author (year):: Baykan2024
Canonical:: —

Also importable as: DS005586, Baykan2024.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 23; recordings: 23; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005586 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005586 DOI: https://doi.org/10.18112/openneuro.ds005586.v2.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005586
>>> dataset = DS005586(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005594(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Alphabetic Decision Task (Arial Light Font)

Study:: ds005594 (OpenNeuro)
Author (year):: Taylor2024
Canonical:: —

Also importable as: DS005594, Taylor2024.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 16; recordings: 16; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005594 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005594 DOI: https://doi.org/10.18112/openneuro.ds005594.v1.0.3 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005594
>>> dataset = DS005594(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005620(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A repeated awakening study exploring the capacity of complexity measures to capture dreaming during propofol sedation

Study:: ds005620 (OpenNeuro)
Author (year):: Bajwa2024
Canonical:: —

Also importable as: DS005620, Bajwa2024.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. Subjects: 21; recordings: 202; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005620 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005620 DOI: https://doi.org/10.18112/openneuro.ds005620.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005620
>>> dataset = DS005620(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005624(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Color Change Detection Task

Study:: ds005624 (OpenNeuro)
Author (year):: DS5624_ColorChangeDetection
Canonical:: —

Also importable as: DS005624, DS5624_ColorChangeDetection.

Modality: ieeg; Experiment type: Memory; Subject type: Unknown. Subjects: 24; recordings: 35; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005624 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005624 DOI: https://doi.org/10.18112/openneuro.ds005624.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005624
>>> dataset = DS005624(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005628(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Dataset of Visual and Audiovisual Stimuli in Virtual Reality from the Edzna Archaeological Site

Study:: ds005628 (OpenNeuro)
Author (year):: RosadoAiza2024
Canonical:: —

Also importable as: DS005628, RosadoAiza2024.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 102; recordings: 306; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005628 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005628 DOI: https://doi.org/10.18112/openneuro.ds005628.v1.0.0

Examples

>>> from eegdash.dataset import DS005628
>>> dataset = DS005628(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005642(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

illusory-face-eeg

Study:: ds005642 (OpenNeuro)
Author (year):: Robinson2024_illusory
Canonical:: —

Also importable as: DS005642, Robinson2024_illusory.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 21; recordings: 21; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005642 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005642 DOI: https://doi.org/10.18112/openneuro.ds005642.v1.0.1

Examples

>>> from eegdash.dataset import DS005642
>>> dataset = DS005642(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005648(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Mapping object space dimensions: new insights from temporal dynamics

Study:: ds005648 (OpenNeuro)
Author (year):: Kidder2024
Canonical:: —

Also importable as: DS005648, Kidder2024.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 21; recordings: 21; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005648 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005648 DOI: https://doi.org/10.18112/openneuro.ds005648.v1.0.3

Examples

>>> from eegdash.dataset import DS005648
>>> dataset = DS005648(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005662(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A comprehensive EEG dataset for investigating visual touch perception

Study:: ds005662 (OpenNeuro)
Author (year):: Smit2024
Canonical:: —

Also importable as: DS005662, Smit2024.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 80; recordings: 80; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005662 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005662 DOI: https://doi.org/10.18112/openneuro.ds005662.v2.0.1

Examples

>>> from eegdash.dataset import DS005662
>>> dataset = DS005662(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005670(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

SEEG Resting State Recording

Study:: ds005670 (OpenNeuro)
Author (year):: Xu2024_SEEG_Resting_State
Canonical:: —

Also importable as: DS005670, Xu2024_SEEG_Resting_State.

Modality: ieeg; Experiment type: Resting-state; Subject type: Epilepsy. Subjects: 2; recordings: 2; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005670 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005670 DOI: https://doi.org/10.18112/openneuro.ds005670.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005670
>>> dataset = DS005670(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005672(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

PerceiveImagine

Study:: ds005672 (OpenNeuro)
Author (year):: Zhiyuan2024
Canonical:: —

Also importable as: DS005672, Zhiyuan2024.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 3; recordings: 3; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005672 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005672 DOI: https://doi.org/10.18112/openneuro.ds005672.v1.0.0 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS005672
>>> dataset = DS005672(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005688(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

visStim

Study:: ds005688 (OpenNeuro)
Author (year):: Tan2024
Canonical:: —

Also importable as: DS005688, Tan2024.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. Subjects: 20; recordings: 89; tasks: 5.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005688 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005688 DOI: https://doi.org/10.18112/openneuro.ds005688.v1.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005688
>>> dataset = DS005688(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005691(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

SpinalExpect_Invasive

Study:: ds005691 (OpenNeuro)
Author (year):: Stenner2024_SpinalExpect
Canonical:: —

Also importable as: DS005691, Stenner2024_SpinalExpect.

Modality: ieeg; Experiment type: Attention; Subject type: Other. Subjects: 8; recordings: 8; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005691 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005691 DOI: https://doi.org/10.18112/openneuro.ds005691.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005691
>>> dataset = DS005691(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005692(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

SpinalExpect_NonInvasive

Study:: ds005692 (OpenNeuro)
Author (year):: Stenner2024_SpinalExpect_NonInvasive
Canonical:: —

Also importable as: DS005692, Stenner2024_SpinalExpect_NonInvasive.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 30; recordings: 59; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005692 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005692 DOI: https://doi.org/10.18112/openneuro.ds005692.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005692
>>> dataset = DS005692(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005697(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

PerceiveImagine

Study:: ds005697 (OpenNeuro)
Author (year):: Li2024_PerceiveImagine
Canonical:: PerceiveImagine

Also importable as: DS005697, Li2024_PerceiveImagine, PerceiveImagine.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 51; recordings: 51; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005697 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005697 DOI: https://doi.org/10.18112/openneuro.ds005697.v1.0.2 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS005697
>>> dataset = DS005697(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['PerceiveImagine']

class eegdash.dataset.dataset.DS005752(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The NIMH Healthy Research Volunteer Dataset

Study:: ds005752 (OpenNeuro)
Author (year):: Nugent2024
Canonical:: —

Also importable as: DS005752, Nugent2024.

Modality: meg; Experiment type: Other; Subject type: Healthy. Subjects: 123; recordings: 1055; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005752 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005752 DOI: https://doi.org/10.18112/openneuro.ds005752.v2.1.0

Examples

>>> from eegdash.dataset import DS005752
>>> dataset = DS005752(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005776(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Electrical_Thermal_FingerTapping_2015

Study:: ds005776 (OpenNeuro)
Author (year):: Yucel2025_Electrical
Canonical:: Yucel2015

Also importable as: DS005776, Yucel2025_Electrical, Yucel2015.

Modality: fnirs; Experiment type: Motor; Subject type: Healthy. Subjects: 11; recordings: 46; tasks: 5.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005776 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005776 DOI: https://doi.org/10.18112/openneuro.ds005776.v1.0.1

Examples

>>> from eegdash.dataset import DS005776
>>> dataset = DS005776(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Yucel2015']

class eegdash.dataset.dataset.DS005777(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Electrical_Morphine_Placebo_2018

Study:: ds005777 (OpenNeuro)
Author (year):: Peng2025
Canonical:: Peng2018

Also importable as: DS005777, Peng2025, Peng2018.

Modality: fnirs; Experiment type: Perception; Subject type: Unknown. Subjects: 14; recordings: 113; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005777 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005777 DOI: https://doi.org/10.18112/openneuro.ds005777.v1.0.1

Examples

>>> from eegdash.dataset import DS005777
>>> dataset = DS005777(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Peng2018']

class eegdash.dataset.dataset.DS005779(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Real-time personalized brain state-dependent TMS in healthy adults

Study:: ds005779 (OpenNeuro)
Author (year):: Khatri2025
Canonical:: —

Also importable as: DS005779, Khatri2025.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. Subjects: 19; recordings: 250; tasks: 16.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005779 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005779 DOI: https://doi.org/10.18112/openneuro.ds005779.v1.0.1

Examples

>>> from eegdash.dataset import DS005779
>>> dataset = DS005779(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005795(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

MULTI-CLARID (Multimodal Category Learning and Resting-state Imaging Data)

Study:: ds005795 (OpenNeuro)
Author (year):: Stadler2025
Canonical:: —

Also importable as: DS005795, Stadler2025.

Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 34; recordings: 39; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005795 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005795 DOI: https://doi.org/10.18112/openneuro.ds005795.v1.0.0

Examples

>>> from eegdash.dataset import DS005795
>>> dataset = DS005795(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005810(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

NOD-MEG

Study:: ds005810 (OpenNeuro)
Author (year):: Zhang2025_MEG
Canonical:: NOD_MEG

Also importable as: DS005810, Zhang2025_MEG, NOD_MEG.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 31; recordings: 305; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005810 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005810 DOI: https://doi.org/10.18112/openneuro.ds005810.v2.0.0

Examples

>>> from eegdash.dataset import DS005810
>>> dataset = DS005810(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['NOD_MEG']

class eegdash.dataset.dataset.DS005811(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

NOD-EEG

Study:: ds005811 (OpenNeuro)
Author (year):: Zhang2025_EEG
Canonical:: NOD_EEG

Also importable as: DS005811, Zhang2025_EEG, NOD_EEG.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 19; recordings: 448; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005811 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005811 DOI: https://doi.org/10.18112/openneuro.ds005811.v1.0.9

Examples

>>> from eegdash.dataset import DS005811
>>> dataset = DS005811(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['NOD_EEG']

class eegdash.dataset.dataset.DS005815(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A Human EEG Dataset for Multisensory Perception and Mental Imagery

Study:: ds005815 (OpenNeuro)
Author (year):: Chang2025
Canonical:: —

Also importable as: DS005815, Chang2025.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 20; recordings: 103; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005815 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005815 DOI: https://doi.org/10.18112/openneuro.ds005815.v2.0.1

Examples

>>> from eegdash.dataset import DS005815
>>> dataset = DS005815(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005841(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG Experiment measuring ERPs in VR

Study:: ds005841 (OpenNeuro)
Author (year):: Karakashevska2025
Canonical:: —

Also importable as: DS005841, Karakashevska2025.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 48; recordings: 288; tasks: 6.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005841 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005841 DOI: https://doi.org/10.18112/openneuro.ds005841.v1.0.0

Examples

>>> from eegdash.dataset import DS005841
>>> dataset = DS005841(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005857(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

ltpDelayRepFRReadOnly

Study:: ds005857 (OpenNeuro)
Author (year):: Broitman2025
Canonical:: Broitman2019

Also importable as: DS005857, Broitman2025, Broitman2019.

Modality: eeg; Experiment type: Memory; Subject type: Unknown. Subjects: 29; recordings: 110; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005857 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005857 DOI: https://doi.org/10.18112/openneuro.ds005857.v1.0.0

Examples

>>> from eegdash.dataset import DS005857
>>> dataset = DS005857(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Broitman2019']

class eegdash.dataset.dataset.DS005863(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Cognitive Electrophysiology in Socioeconomic Context in Adulthood

Study:: ds005863 (OpenNeuro)
Author (year):: Isbell2025_Cognitive
Canonical:: —

Also importable as: DS005863, Isbell2025_Cognitive.

Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 127; recordings: 357; tasks: 4.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005863 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005863 DOI: https://doi.org/10.18112/openneuro.ds005863.v2.0.0

Examples

>>> from eegdash.dataset import DS005863
>>> dataset = DS005863(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005866(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Flankers-NEAR

Study:: ds005866 (OpenNeuro)
Author (year):: TerhuneCotter2025_NEAR
Canonical:: Flankers_NEAR

Also importable as: DS005866, TerhuneCotter2025_NEAR, Flankers_NEAR.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 60; recordings: 60; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005866 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005866 DOI: https://doi.org/10.18112/openneuro.ds005866.v1.0.1

Examples

>>> from eegdash.dataset import DS005866
>>> dataset = DS005866(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Flankers_NEAR']

class eegdash.dataset.dataset.DS005868(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Flankers-FAR

Study:: ds005868 (OpenNeuro)
Author (year):: TerhuneCotter2025_FAR
Canonical:: Flankers_FAR

Also importable as: DS005868, TerhuneCotter2025_FAR, Flankers_FAR.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 48; recordings: 48; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005868 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005868 DOI: https://doi.org/10.18112/openneuro.ds005868.v1.0.1

Examples

>>> from eegdash.dataset import DS005868
>>> dataset = DS005868(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Flankers_FAR']

class eegdash.dataset.dataset.DS005872(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEGEyeNet Dataset

Study:: ds005872 (OpenNeuro)
Author (year):: Plomecka2025
Canonical:: EEGEyeNet

Also importable as: DS005872, Plomecka2025, EEGEyeNet.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005872 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005872 DOI: https://doi.org/10.18112/openneuro.ds005872.v1.0.0

Examples

>>> from eegdash.dataset import DS005872
>>> dataset = DS005872(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['EEGEyeNet']

class eegdash.dataset.dataset.DS005873(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

SeizeIT2

Study:: ds005873 (OpenNeuro)
Author (year):: Bhagubai2025
Canonical:: SeizeIT2

Also importable as: DS005873, Bhagubai2025, SeizeIT2.

Modality: eeg, emg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 125; recordings: 5654; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005873 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005873 DOI: https://doi.org/10.18112/openneuro.ds005873.v1.1.0

Examples

>>> from eegdash.dataset import DS005873
>>> dataset = DS005873(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['SeizeIT2']

class eegdash.dataset.dataset.DS005876(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Song Familiarity

Study:: ds005876 (OpenNeuro)
Author (year):: Girard2025
Canonical:: —

Also importable as: DS005876, Girard2025.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 29; recordings: 29; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005876 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005876 DOI: https://doi.org/10.18112/openneuro.ds005876.v1.0.1

Examples

>>> from eegdash.dataset import DS005876
>>> dataset = DS005876(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005907(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG: RL Task (3-Armed Bandit) with alcohol cues in hazardous drinkers and ctls

Study:: ds005907 (OpenNeuro)
Author (year):: Campbell2025
Canonical:: —

Also importable as: DS005907, Campbell2025.

Modality: eeg; Experiment type: Learning; Subject type: Alcohol. Subjects: 53; recordings: 53; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005907 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005907 DOI: https://doi.org/10.18112/openneuro.ds005907.v1.0.0

Examples

>>> from eegdash.dataset import DS005907
>>> dataset = DS005907(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005929(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Motion-Yucel2014

Study:: ds005929 (OpenNeuro)
Author (year):: MotionYucel2014
Canonical:: Yucel2014, Motion_Yucel2014

Also importable as: DS005929, MotionYucel2014, Yucel2014, Motion_Yucel2014.

Modality: fnirs; Experiment type: Motor; Subject type: Healthy. Subjects: 7; recordings: 7; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005929 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005929 DOI: https://doi.org/10.18112/openneuro.ds005929.v1.0.1

Examples

>>> from eegdash.dataset import DS005929
>>> dataset = DS005929(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Yucel2014', 'Motion_Yucel2014']

class eegdash.dataset.dataset.DS005930(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BallSqueezingHD_Gao2023

Study:: ds005930 (OpenNeuro)
Author (year):: Gao2023
Canonical:: —

Also importable as: DS005930, Gao2023.

Modality: fnirs; Experiment type: Motor; Subject type: Unknown. Subjects: 12; recordings: 36; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005930 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005930 DOI: https://doi.org/10.18112/openneuro.ds005930.v1.0.1

Examples

>>> from eegdash.dataset import DS005930
>>> dataset = DS005930(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005931(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Visuomotor_task

Study:: ds005931 (OpenNeuro)
Author (year):: Ueda2025
Canonical:: —

Also importable as: DS005931, Ueda2025.

Modality: ieeg; Experiment type: Motor; Subject type: Epilepsy. Subjects: 8; recordings: 16; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005931 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005931 DOI: https://doi.org/10.18112/openneuro.ds005931.v1.0.0

Examples

>>> from eegdash.dataset import DS005931
>>> dataset = DS005931(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005932(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

PWIe

Study:: ds005932 (OpenNeuro)
Author (year):: Holcomb2025
Canonical:: PWIe

Also importable as: DS005932, Holcomb2025, PWIe.

Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 29; recordings: 29; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005932 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005932 DOI: https://doi.org/10.18112/openneuro.ds005932.v1.0.0

Examples

>>> from eegdash.dataset import DS005932
>>> dataset = DS005932(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['PWIe']

class eegdash.dataset.dataset.DS005935(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Mirror Neuron Study

Study:: ds005935 (OpenNeuro)
Author (year):: Li2025
Canonical:: —

Also importable as: DS005935, Li2025.

Modality: fnirs; Experiment type: Motor; Subject type: Unknown. Subjects: 21; recordings: 64; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005935 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005935 DOI: https://doi.org/10.18112/openneuro.ds005935.v1.0.0

Examples

>>> from eegdash.dataset import DS005935
>>> dataset = DS005935(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005946(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

ERC_CoG PROMENADE - WP2 - MetaImagery (Metaphor and Mental Imagery)

Study:: ds005946 (OpenNeuro)
Author (year):: Frau2025
Canonical:: PROMENADE

Also importable as: DS005946, Frau2025, PROMENADE.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 39; recordings: 39; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005946 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005946 DOI: https://doi.org/10.18112/openneuro.ds005946.v1.0.1

Examples

>>> from eegdash.dataset import DS005946
>>> dataset = DS005946(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['PROMENADE']

class eegdash.dataset.dataset.DS005953(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

iEEG_visual

Study:: ds005953 (OpenNeuro)
Author (year):: Winawer2025
Canonical:: —

Also importable as: DS005953, Winawer2025.

Modality: ieeg; Experiment type: Perception; Subject type: Surgery. Subjects: 2; recordings: 3; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005953 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005953 DOI: https://doi.org/10.18112/openneuro.ds005953.v1.0.0

Examples

>>> from eegdash.dataset import DS005953
>>> dataset = DS005953(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005960(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

General Info: inst-comp-eeg

Study:: ds005960 (OpenNeuro)
Author (year):: Pena2025
Canonical:: —

Also importable as: DS005960, Pena2025.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 41; recordings: 41; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005960 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005960 DOI: https://doi.org/10.18112/openneuro.ds005960.v1.0.0

Examples

>>> from eegdash.dataset import DS005960
>>> dataset = DS005960(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS005963(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

FRESH Motor Dataset

Study:: ds005963 (OpenNeuro)
Author (year):: Mesquita2025
Canonical:: Mesquita2019

Also importable as: DS005963, Mesquita2025, Mesquita2019.

Modality: fnirs; Experiment type: Motor; Subject type: Unknown. Subjects: 10; recordings: 40; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005963 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005963 DOI: https://doi.org/10.18112/openneuro.ds005963.v1.0.0

Examples

>>> from eegdash.dataset import DS005963
>>> dataset = DS005963(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Mesquita2019']

class eegdash.dataset.dataset.DS005964(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

FRESH Audio Dataset

Study:: ds005964 (OpenNeuro)
Author (year):: Luke2025
Canonical:: Luke2019

Also importable as: DS005964, Luke2025, Luke2019.

Modality: fnirs; Experiment type: Perception; Subject type: Unknown. Subjects: 17; recordings: 17; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005964 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005964 DOI: https://doi.org/10.18112/openneuro.ds005964.v1.0.0

Examples

>>> from eegdash.dataset import DS005964
>>> dataset = DS005964(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Luke2019']

class eegdash.dataset.dataset.DS006012(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A geometric shape regularity effect in the human brain: MEG dataset

Study:: ds006012 (OpenNeuro)
Author (year):: SableMeyer2025
Canonical:: —

Also importable as: DS006012, SableMeyer2025.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 21; recordings: 193; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006012 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006012 DOI: https://doi.org/10.18112/openneuro.ds006012.v1.0.1

Examples

>>> from eegdash.dataset import DS006012
>>> dataset = DS006012(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006018(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Cognitive Electrophysiology in Socioeconomic Context in Adulthood: An EEG dataset

Study:: ds006018 (OpenNeuro)
Author (year):: Isbell2025_Adulthood
Canonical:: —

Also importable as: DS006018, Isbell2025_Adulthood.

Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 127; recordings: 357; tasks: 4.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006018 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006018 DOI: https://doi.org/10.18112/openneuro.ds006018.v1.2.2

Examples

>>> from eegdash.dataset import DS006018
>>> dataset = DS006018(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006033(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Synchronous EEG and fMRI dataset on inner speech

Study:: ds006033 (OpenNeuro)
Author (year):: Liwicki2025
Canonical:: —

Also importable as: DS006033, Liwicki2025.

Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 3; recordings: 5; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006033 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006033 DOI: https://doi.org/10.18112/openneuro.ds006033.v1.0.1

Examples

>>> from eegdash.dataset import DS006033
>>> dataset = DS006033(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006035(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

somatomotor

Study:: ds006035 (OpenNeuro)
Author (year):: Lin2025
Canonical:: Lin2019

Also importable as: DS006035, Lin2025, Lin2019.

Modality: meg; Experiment type: Motor; Subject type: Healthy. Subjects: 5; recordings: 15; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006035 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006035 DOI: https://doi.org/10.18112/openneuro.ds006035.v1.0.0

Examples

>>> from eegdash.dataset import DS006035
>>> dataset = DS006035(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Lin2019']

class eegdash.dataset.dataset.DS006036(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A complementary dataset of open-eyes EEG recordings in a photo-stimulation setting from: Alzheimer’s disease, Frontotemporal dementia and Healthy subjects

Study:: ds006036 (OpenNeuro)
Author (year):: Ntetska2025
Canonical:: —

Also importable as: DS006036, Ntetska2025.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Dementia. Subjects: 88; recordings: 88; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006036 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006036 DOI: https://doi.org/10.18112/openneuro.ds006036.v1.0.6

Examples

>>> from eegdash.dataset import DS006036
>>> dataset = DS006036(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006040(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Sustained Attention Task (gradCPT) Dataset using simultaneous EEG-fMRI and DTI

Study:: ds006040 (OpenNeuro)
Author (year):: Cha2025
Canonical:: —

Also importable as: DS006040, Cha2025.

Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 28; recordings: 392; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006040 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006040 DOI: https://doi.org/10.18112/openneuro.ds006040.v1.0.2

Examples

>>> from eegdash.dataset import DS006040
>>> dataset = DS006040(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006065(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

TSS_iEEG

Study:: ds006065 (OpenNeuro)
Author (year):: Kragel2025
Canonical:: —

Also importable as: DS006065, Kragel2025.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Surgery. Subjects: 7; recordings: 45; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006065 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006065 DOI: https://doi.org/10.18112/openneuro.ds006065.v1.0.0

Examples

>>> from eegdash.dataset import DS006065
>>> dataset = DS006065(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006095(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Mind in Motion Older Adults Walking Over Uneven Terrain

Study:: ds006095 (OpenNeuro)
Author (year):: Liu2025_Mind_Motion_Older
Canonical:: —

Also importable as: DS006095, Liu2025_Mind_Motion_Older.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 71; recordings: 1182; tasks: 9.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006095 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006095 DOI: https://doi.org/10.18112/openneuro.ds006095.v1.0.0

Examples

>>> from eegdash.dataset import DS006095
>>> dataset = DS006095(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006104(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG dataset for speech decoding

Study:: ds006104 (OpenNeuro)
Author (year):: Moreira2025
Canonical:: —

Also importable as: DS006104, Moreira2025.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 24; recordings: 56; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006104 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006104 DOI: https://doi.org/10.18112/openneuro.ds006104.v1.0.1

Examples

>>> from eegdash.dataset import DS006104
>>> dataset = DS006104(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006107(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

iEEG_Neural_spatial_volatility

Study:: ds006107 (OpenNeuro)
Author (year):: Kuroda2025
Canonical:: Kuroda2024

Also importable as: DS006107, Kuroda2025, Kuroda2024.

Modality: ieeg; Experiment type: Sleep; Subject type: Unknown. Subjects: 166; recordings: 167; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006107 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006107 DOI: https://doi.org/10.18112/openneuro.ds006107.v1.0.0

Examples

>>> from eegdash.dataset import DS006107
>>> dataset = DS006107(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Kuroda2024']

class eegdash.dataset.dataset.DS006126(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

TDCS Modulation of Visual Cortex in Motor Imagery

Study:: ds006126 (OpenNeuro)
Author (year):: Mensah2025
Canonical:: —

Also importable as: DS006126, Mensah2025.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 5; recordings: 90; tasks: 6.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006126 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006126 DOI: https://doi.org/10.18112/openneuro.ds006126.v1.0.0

Examples

>>> from eegdash.dataset import DS006126
>>> dataset = DS006126(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006136(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

OWM-Dataset

Study:: ds006136 (OpenNeuro)
Author (year):: Omelyusik2025
Canonical:: Omelyusik2026

Also importable as: DS006136, Omelyusik2025, Omelyusik2026.

Modality: ieeg; Experiment type: Memory; Subject type: Epilepsy. Subjects: 13; recordings: 14; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006136 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006136 DOI: https://doi.org/10.18112/openneuro.ds006136.v1.0.1

Examples

>>> from eegdash.dataset import DS006136
>>> dataset = DS006136(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Omelyusik2026']

class eegdash.dataset.dataset.DS006142(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Essex EEG Movie Memory dataset

Study:: ds006142 (OpenNeuro)
Author (year):: MatranFernandez2025
Canonical:: —

Also importable as: DS006142, MatranFernandez2025.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 27; recordings: 27; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006142 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006142 DOI: https://doi.org/10.18112/openneuro.ds006142.v1.0.2

Examples

>>> from eegdash.dataset import DS006142
>>> dataset = DS006142(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006159(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Implicit Learning EEG (BioSemi)

Study:: ds006159 (OpenNeuro)
Author (year):: LeganesFonteneau2025
Canonical:: LeganesFonteneau2024

Also importable as: DS006159, LeganesFonteneau2025, LeganesFonteneau2024.

Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 61; recordings: 61; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006159 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006159 DOI: https://doi.org/10.18112/openneuro.ds006159.v1.0.0

Examples

>>> from eegdash.dataset import DS006159
>>> dataset = DS006159(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['LeganesFonteneau2024']

class eegdash.dataset.dataset.DS006171(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG data during three near-threshold visual detection tasks: a no-cue task, a noninformative cue task (50% validity), and an informative cue task (100% validity)

Study:: ds006171 (OpenNeuro)
Author (year):: Melcon2025
Canonical:: Melcon2024

Also importable as: DS006171, Melcon2025, Melcon2024.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 36; recordings: 104; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006171 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006171 DOI: https://doi.org/10.18112/openneuro.ds006171.v1.0.0

Examples

>>> from eegdash.dataset import DS006171
>>> dataset = DS006171(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Melcon2024']

class eegdash.dataset.dataset.DS006222(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

MultisensoryFlickerHealthyYoungAdults_AllSubjectsRawData

Study:: ds006222 (OpenNeuro)
Author (year):: Attokaren2025
Canonical:: —

Also importable as: DS006222, Attokaren2025.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 69; recordings: 70; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006222 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006222 DOI: https://doi.org/10.18112/openneuro.ds006222.v1.0.1

Examples

>>> from eegdash.dataset import DS006222
>>> dataset = DS006222(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006233(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Picture naming

Study:: ds006233 (OpenNeuro)
Author (year):: Kochi2025_Picture_naming
Canonical:: —

Also importable as: DS006233, Kochi2025_Picture_naming.

Modality: ieeg; Experiment type: Other; Subject type: Surgery. Subjects: 108; recordings: 347; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006233 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006233 DOI: https://doi.org/10.18112/openneuro.ds006233.v1.0.0

Examples

>>> from eegdash.dataset import DS006233
>>> dataset = DS006233(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006234(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Auditory naming

Study:: ds006234 (OpenNeuro)
Author (year):: Kochi2025_Auditory_naming
Canonical:: —

Also importable as: DS006234, Kochi2025_Auditory_naming.

Modality: ieeg; Experiment type: Other; Subject type: Surgery. Subjects: 119; recordings: 378; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006234 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006234 DOI: https://doi.org/10.18112/openneuro.ds006234.v1.0.0

Examples

>>> from eegdash.dataset import DS006234
>>> dataset = DS006234(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006253(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

MetaRDK

Study:: ds006253 (OpenNeuro)
Author (year):: Goueytes2024
Canonical:: MetaRDK

Also importable as: DS006253, Goueytes2024, MetaRDK.

Modality: ieeg; Experiment type: Decision-making; Subject type: Epilepsy. Subjects: 23; recordings: 201; tasks: 4.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006253 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006253 DOI: https://doi.org/10.18112/openneuro.ds006253.v1.0.3

Examples

>>> from eegdash.dataset import DS006253
>>> dataset = DS006253(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['MetaRDK']

class eegdash.dataset.dataset.DS006260(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Dataset of psychophysiological data from children with learning difficulties who strengthen reading and math skills through assistive technology

Study:: ds006260 (OpenNeuro)
Author (year):: CoronaGonzalez2025
Canonical:: —

Also importable as: DS006260, CoronaGonzalez2025.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 76; recordings: 366; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006260 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006260 DOI: https://doi.org/10.18112/openneuro.ds006260.v1.0.1

Examples

>>> from eegdash.dataset import DS006260
>>> dataset = DS006260(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006269(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Tethered EEG Recordings in Syngap1 rats

Study:: ds006269 (OpenNeuro)
Author (year):: Pritchard2025
Canonical:: —

Also importable as: DS006269, Pritchard2025.

Modality: eeg; Experiment type: Resting-state; Subject type: Other. Subjects: 24; recordings: 40; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006269 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006269 DOI: https://doi.org/10.18112/openneuro.ds006269.v1.0.0

Examples

>>> from eegdash.dataset import DS006269
>>> dataset = DS006269(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006317(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Chisco-2.0

Study:: ds006317 (OpenNeuro)
Author (year):: Zhang2025_Chisco_2_0
Canonical:: Chisco2_0, Chisco20, CHISCO20

Also importable as: DS006317, Zhang2025_Chisco_2_0, Chisco2_0, Chisco20, CHISCO20.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 2; recordings: 64; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006317 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006317 DOI: https://doi.org/10.18112/openneuro.ds006317.v1.1.0

Examples

>>> from eegdash.dataset import DS006317
>>> dataset = DS006317(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Chisco2_0', 'Chisco20', 'CHISCO20']

class eegdash.dataset.dataset.DS006334(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Neocortical and Hippocampal Theta Oscillations Track Audiovisual Integration and Replay of Speech Memories

Study:: ds006334 (OpenNeuro)
Author (year):: Biau2025
Canonical:: —

Also importable as: DS006334, Biau2025.

Modality: meg; Experiment type: Memory; Subject type: Healthy. Subjects: 30; recordings: 128; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006334 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006334 DOI: https://doi.org/10.18112/openneuro.ds006334.v1.0.0

Examples

>>> from eegdash.dataset import DS006334
>>> dataset = DS006334(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006366(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Mouse Sleep Staging Validation dataset (MSSV)

Study:: ds006366 (OpenNeuro)
Author (year):: Rose2025
Canonical:: MSSV

Also importable as: DS006366, Rose2025, MSSV.

Modality: eeg; Experiment type: Sleep; Subject type: Healthy. Subjects: 92; recordings: 148; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006366 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006366 DOI: https://doi.org/10.18112/openneuro.ds006366.v1.0.1

Examples

>>> from eegdash.dataset import DS006366
>>> dataset = DS006366(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['MSSV']

class eegdash.dataset.dataset.DS006367(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Memory Reactivation Levels Remain Unaffected by Anticipated Interference

Study:: ds006367 (OpenNeuro)
Author (year):: DS6367_Memory_Reactivation
Canonical:: —

Also importable as: DS006367, DS6367_Memory_Reactivation.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 52; recordings: 52; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006367 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006367 DOI: https://doi.org/10.18112/openneuro.ds006367.v1.0.1

Examples

>>> from eegdash.dataset import DS006367
>>> dataset = DS006367(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006370(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Memory Reactivation Levels Remain Unaffected by Anticipated Interference Experiment 2 Dataset

Study:: ds006370 (OpenNeuro)
Author (year):: DS6370_Memory_Reactivation
Canonical:: —

Also importable as: DS006370, DS6370_Memory_Reactivation.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 56; recordings: 56; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006370 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006370 DOI: https://doi.org/10.18112/openneuro.ds006370.v1.0.1

Examples

>>> from eegdash.dataset import DS006370
>>> dataset = DS006370(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006374(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Expectation effects on repetition suppression in nociception

Study:: ds006374 (OpenNeuro)
Author (year):: Pohle2025
Canonical:: Pohle2019

Also importable as: DS006374, Pohle2025, Pohle2019.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 36; recordings: 358; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006374 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006374 DOI: https://doi.org/10.18112/openneuro.ds006374.v1.0.0

Examples

>>> from eegdash.dataset import DS006374
>>> dataset = DS006374(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Pohle2019']

class eegdash.dataset.dataset.DS006377(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

InclusionStudy

Study:: ds006377 (OpenNeuro)
Author (year):: Yucel2025_InclusionStudy
Canonical:: —

Also importable as: DS006377, Yucel2025_InclusionStudy.

Modality: fnirs; Experiment type: Motor; Subject type: Unknown. Subjects: 115; recordings: 690; tasks: 6.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006377 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006377 DOI: https://doi.org/10.18112/openneuro.ds006377.v1.0.2

Examples

>>> from eegdash.dataset import DS006377
>>> dataset = DS006377(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006386(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

PhysioMotion_Artifact

Study:: ds006386 (OpenNeuro)
Author (year):: Yu2025
Canonical:: Yu2019

Also importable as: DS006386, Yu2025, Yu2019.

Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 30; recordings: 180; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006386 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006386 DOI: https://doi.org/10.18112/openneuro.ds006386.v1.0.1

Examples

>>> from eegdash.dataset import DS006386
>>> dataset = DS006386(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Yu2019']

class eegdash.dataset.dataset.DS006392(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

HED schema library for SCORE annotations example

Study:: ds006392 (OpenNeuro)
Author (year):: Attia2025
Canonical:: Hermes2024

Also importable as: DS006392, Attia2025, Hermes2024.

Modality: ieeg; Experiment type: Perception; Subject type: Unknown. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006392 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006392 DOI: https://doi.org/10.18112/openneuro.ds006392.v1.0.1

Examples

>>> from eegdash.dataset import DS006392
>>> dataset = DS006392(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Hermes2024']

class eegdash.dataset.dataset.DS006394(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Electrophysiological markers of surprise-induced failures of visual and auditory awareness

Study:: ds006394 (OpenNeuro)
Author (year):: Leong2025
Canonical:: —

Also importable as: DS006394, Leong2025.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 33; recordings: 60; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006394 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006394 DOI: https://doi.org/10.18112/openneuro.ds006394.v1.0.3

Examples

>>> from eegdash.dataset import DS006394
>>> dataset = DS006394(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006434(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The auditory brainstem response to natural speech is not affected by selective attention

Study:: ds006434 (OpenNeuro)
Author (year):: Stoll2025
Canonical:: —

Also importable as: DS006434, Stoll2025.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 66; recordings: 118; tasks: 5.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006434 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006434 DOI: https://doi.org/10.18112/openneuro.ds006434.v1.2.0

Examples

>>> from eegdash.dataset import DS006434
>>> dataset = DS006434(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006437(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

LIGHT Hypnotherapy

Study:: ds006437 (OpenNeuro)
Author (year):: DS6437_LIGHT_Hypnotherapy
Canonical:: —

Also importable as: DS006437, DS6437_LIGHT_Hypnotherapy.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. Subjects: 9; recordings: 63; tasks: 5.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006437 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006437 DOI: https://doi.org/10.18112/openneuro.ds006437.v1.1.0

Examples

>>> from eegdash.dataset import DS006437
>>> dataset = DS006437(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006446(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Cueing the future to reduce temporal discounting

Study:: ds006446 (OpenNeuro)
Author (year):: Kinley2025
Canonical:: Kinley2019

Also importable as: DS006446, Kinley2025, Kinley2019.

Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 29; recordings: 29; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006446 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006446 DOI: https://doi.org/10.18112/openneuro.ds006446.v1.0.0

Examples

>>> from eegdash.dataset import DS006446
>>> dataset = DS006446(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Kinley2019']

class eegdash.dataset.dataset.DS006459(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

High-DensityvSparsefNIRS_WordColorStroop_Sparse_Anderson_2025

Study:: ds006459 (OpenNeuro)
Author (year):: Anderson2025_Sparse
Canonical:: —

Also importable as: DS006459, Anderson2025_Sparse.

Modality: fnirs; Experiment type: Attention; Subject type: Healthy. Subjects: 17; recordings: 17; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006459 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006459 DOI: https://doi.org/10.18112/openneuro.ds006459.v1.0.0

Examples

>>> from eegdash.dataset import DS006459
>>> dataset = DS006459(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006460(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

High-DensityvSparsefNIRS_WordColorStroop_HD_Anderson_2025

Study:: ds006460 (OpenNeuro)
Author (year):: Anderson2025_HD
Canonical:: —

Also importable as: DS006460, Anderson2025_HD.

Modality: fnirs; Experiment type: Attention; Subject type: Healthy. Subjects: 17; recordings: 17; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006460 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006460 DOI: https://doi.org/10.18112/openneuro.ds006460.v1.0.0

Examples

>>> from eegdash.dataset import DS006460
>>> dataset = DS006460(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006465(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

3M-CPSEED：An EEG-based Dataset for Chinese Pinyin Production in Overt, Silent-intended, and Imagined Speech

Study:: ds006465 (OpenNeuro)
Author (year):: Ma2025
Canonical:: CPSEED_3M, CPSEED

Also importable as: DS006465, Ma2025, CPSEED_3M, CPSEED.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 20; recordings: 80; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006465 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006465 DOI: https://doi.org/10.18112/openneuro.ds006465.v2.0.0

Examples

>>> from eegdash.dataset import DS006465
>>> dataset = DS006465(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['CPSEED_3M', 'CPSEED']

class eegdash.dataset.dataset.DS006466(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

HeartBEAM: Older Adult Resting State and Auditory Oddball Task EEG Data

Study:: ds006466 (OpenNeuro)
Author (year):: Kim2025_HeartBEAM_Older_Adult
Canonical:: HeartBEAM

Also importable as: DS006466, Kim2025_HeartBEAM_Older_Adult, HeartBEAM.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 66; recordings: 1257; tasks: 6.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006466 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006466 DOI: https://doi.org/10.18112/openneuro.ds006466.v1.0.1

Examples

>>> from eegdash.dataset import DS006466
>>> dataset = DS006466(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HeartBEAM']

class eegdash.dataset.dataset.DS006468(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

MEG-SCANS - A comprehensive magnetoencephalography speech dataset with Stories, Chirps And Noisy Sentences.

Study:: ds006468 (OpenNeuro)
Author (year):: Habersetzer2025
Canonical:: MEG_SCANS

Also importable as: DS006468, Habersetzer2025, MEG_SCANS.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 24; recordings: 189; tasks: 4.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006468 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006468 DOI: https://doi.org/10.18112/openneuro.ds006468.v1.1.2

Examples

>>> from eegdash.dataset import DS006468
>>> dataset = DS006468(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['MEG_SCANS']

class eegdash.dataset.dataset.DS006480(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Young Adult Resting State and Auditory Oddball Task EEG Data

Study:: ds006480 (OpenNeuro)
Author (year):: Kim2025_Young_Adult_Resting
Canonical:: —

Also importable as: DS006480, Kim2025_Young_Adult_Resting.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 68; recordings: 68; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006480 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006480 DOI: https://doi.org/10.18112/openneuro.ds006480.v1.0.1

Examples

>>> from eegdash.dataset import DS006480
>>> dataset = DS006480(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006502(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Skill learning and consolidation in healthy humans

Study:: ds006502 (OpenNeuro)
Author (year):: Bonstrup2025
Canonical:: —

Also importable as: DS006502, Bonstrup2025.

Modality: meg; Experiment type: Learning; Subject type: Healthy. Subjects: 31; recordings: 380; tasks: 4.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006502 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006502 DOI: https://doi.org/10.18112/openneuro.ds006502.v1.0.0

Examples

>>> from eegdash.dataset import DS006502
>>> dataset = DS006502(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006519(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Dataset of intracranial EEG during cortical stimulations evoking negative motor responses

Study:: ds006519 (OpenNeuro)
Author (year):: Barborica2025
Canonical:: —

Also importable as: DS006519, Barborica2025.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 21; recordings: 35; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006519 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006519 DOI: https://doi.org/10.18112/openneuro.ds006519.v1.0.0

Examples

>>> from eegdash.dataset import DS006519
>>> dataset = DS006519(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006525(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Resting EEG

Study:: ds006525 (OpenNeuro)
Author (year):: Neuroimaging2025
Canonical:: —

Also importable as: DS006525, Neuroimaging2025.

Modality: eeg; Experiment type: Resting-state; Subject type: Unknown. Subjects: 34; recordings: 34; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006525 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006525 DOI: https://doi.org/10.18112/openneuro.ds006525.v1.0.0

Examples

>>> from eegdash.dataset import DS006525
>>> dataset = DS006525(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006545(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Reliability-Dubois2024

Study:: ds006545 (OpenNeuro)
Author (year):: ReliabilityDubois2024
Canonical:: Dubois2024

Also importable as: DS006545, ReliabilityDubois2024, Dubois2024.

Modality: fnirs; Experiment type: Unknown; Subject type: Unknown. Subjects: 49; recordings: 98; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006545 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006545 DOI: https://doi.org/10.18112/openneuro.ds006545.v1.0.0

Examples

>>> from eegdash.dataset import DS006545
>>> dataset = DS006545(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Dubois2024']

class eegdash.dataset.dataset.DS006547(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Visual EEG Study (BrainVision → BIDS)

Study:: ds006547 (OpenNeuro)
Author (year):: Ghaffari2025
Canonical:: Ghaffari2024

Also importable as: DS006547, Ghaffari2025, Ghaffari2024.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 31; recordings: 31; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006547 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006547 DOI: https://doi.org/10.18112/openneuro.ds006547.v1.0.0

Examples

>>> from eegdash.dataset import DS006547
>>> dataset = DS006547(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Ghaffari2024']

class eegdash.dataset.dataset.DS006554(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Social Observation EEG raw data

Study:: ds006554 (OpenNeuro)
Author (year):: Su2025
Canonical:: —

Also importable as: DS006554, Su2025.

Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 47; recordings: 47; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006554 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006554 DOI: https://doi.org/10.18112/openneuro.ds006554.v1.0.0

Examples

>>> from eegdash.dataset import DS006554
>>> dataset = DS006554(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006563(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Dimension-based attention modulates early visual processing

Study:: ds006563 (OpenNeuro)
Author (year):: Gramann2025
Canonical:: —

Also importable as: DS006563, Gramann2025.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 12; recordings: 12; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006563 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006563 DOI: https://doi.org/10.18112/openneuro.ds006563.v1.0.0

Examples

>>> from eegdash.dataset import DS006563
>>> dataset = DS006563(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006576(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The role of REM sleep in neural differentiation of memories in the hippocampus

Study:: ds006576 (OpenNeuro)
Author (year):: McDevitt2025
Canonical:: —

Also importable as: DS006576, McDevitt2025.

Modality: eeg; Experiment type: Sleep; Subject type: Healthy. Subjects: 57; recordings: 57; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006576 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006576 DOI: https://doi.org/10.18112/openneuro.ds006576.v1.0.3

Examples

>>> from eegdash.dataset import DS006576
>>> dataset = DS006576(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006593(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

cBCI Matrix Multimodal Dataset

Study:: ds006593 (OpenNeuro)
Author (year):: Celik2025
Canonical:: —

Also importable as: DS006593, Celik2025.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 21; recordings: 21; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006593 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006593 DOI: https://doi.org/10.18112/openneuro.ds006593.v1.0.0

Examples

>>> from eegdash.dataset import DS006593
>>> dataset = DS006593(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006629(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

SINGSING

Study:: ds006629 (OpenNeuro)
Author (year):: Chanoine2025
Canonical:: SINGSING

Also importable as: DS006629, Chanoine2025, SINGSING.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 19; recordings: 38; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006629 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006629 DOI: https://doi.org/10.18112/openneuro.ds006629.v1.0.1

Examples

>>> from eegdash.dataset import DS006629
>>> dataset = DS006629(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['SINGSING']

class eegdash.dataset.dataset.DS006647(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Poetry Assessment EEG Dataset 2

Study:: ds006647 (OpenNeuro)
Author (year):: Chaudhuri2025_D2
Canonical:: —

Also importable as: DS006647, Chaudhuri2025_D2.

Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 4; recordings: 4; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006647 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006647 DOI: https://doi.org/10.18112/openneuro.ds006647.v1.0.1

Examples

>>> from eegdash.dataset import DS006647
>>> dataset = DS006647(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006648(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Poetry Assessment EEG Dataset 1

Study:: ds006648 (OpenNeuro)
Author (year):: Chaudhuri2025_D1
Canonical:: —

Also importable as: DS006648, Chaudhuri2025_D1.

Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 47; recordings: 47; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006648 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006648 DOI: https://doi.org/10.18112/openneuro.ds006648.v1.0.0

Examples

>>> from eegdash.dataset import DS006648
>>> dataset = DS006648(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006673(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

ball_squeeze_Carlton_2025

Study:: ds006673 (OpenNeuro)
Author (year):: Carlton2025
Canonical:: —

Also importable as: DS006673, Carlton2025.

Modality: fnirs; Experiment type: Motor; Subject type: Healthy. Subjects: 17; recordings: 67; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006673 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006673 DOI: https://doi.org/10.18112/openneuro.ds006673.v1.0.2

Examples

>>> from eegdash.dataset import DS006673
>>> dataset = DS006673(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006695(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Validation of Sleep Staging with Forehead EEG Patch

Study:: ds006695 (OpenNeuro)
Author (year):: Onton2025
Canonical:: Onton2024

Also importable as: DS006695, Onton2025, Onton2024.

Modality: eeg; Experiment type: Sleep; Subject type: Healthy. Subjects: 19; recordings: 19; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006695 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006695 DOI: https://doi.org/10.18112/openneuro.ds006695.v1.0.2

Examples

>>> from eegdash.dataset import DS006695
>>> dataset = DS006695(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Onton2024']

class eegdash.dataset.dataset.DS006720(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Alpha power indexes working memory load for durations

Study:: ds006720 (OpenNeuro)
Author (year):: Herbst2025
Canonical:: —

Also importable as: DS006720, Herbst2025.

Modality: meg; Experiment type: Memory; Subject type: Healthy. Subjects: 24; recordings: 246; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006720 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006720 DOI: https://doi.org/10.18112/openneuro.ds006720.v1.0.0

Examples

>>> from eegdash.dataset import DS006720
>>> dataset = DS006720(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006735(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Chimeric music reveals an interaction of pitch and time in electrophysiological signatures of music encoding

Study:: ds006735 (OpenNeuro)
Author (year):: Shan2025
Canonical:: —

Also importable as: DS006735, Shan2025.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 27; recordings: 27; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006735 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006735 DOI: https://doi.org/10.18112/openneuro.ds006735.v2.0.0

Examples

>>> from eegdash.dataset import DS006735
>>> dataset = DS006735(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006761(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Neural decoding of competitive decision-making in Rock-Paper-Scissors

Study:: ds006761 (OpenNeuro)
Author (year):: Moerel2025_Neural
Canonical:: —

Also importable as: DS006761, Moerel2025_Neural.

Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 31; recordings: 31; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006761 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006761 DOI: https://doi.org/10.18112/openneuro.ds006761.v1.0.0

Examples

>>> from eegdash.dataset import DS006761
>>> dataset = DS006761(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006768(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Multiple Object Monitoring (EEG)

Study:: ds006768 (OpenNeuro)
Author (year):: Lowe2025
Canonical:: —

Also importable as: DS006768, Lowe2025.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 30; recordings: 210; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006768 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006768 DOI: https://doi.org/10.18112/openneuro.ds006768.v1.1.0

Examples

>>> from eegdash.dataset import DS006768
>>> dataset = DS006768(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006801(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Resting-state EEG before and after different study methods

Study:: ds006801 (OpenNeuro)
Author (year):: Alves2025
Canonical:: —

Also importable as: DS006801, Alves2025.

Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 21; recordings: 42; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006801 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006801 DOI: https://doi.org/10.18112/openneuro.ds006801.v1.0.0

Examples

>>> from eegdash.dataset import DS006801
>>> dataset = DS006801(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006802(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Collaborative rule learning promotes interbrain information alignment

Study:: ds006802 (OpenNeuro)
Author (year):: Moerel2025_Collaborative
Canonical:: —

Also importable as: DS006802, Moerel2025_Collaborative.

Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 24; recordings: 24; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006802 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006802 DOI: https://doi.org/10.18112/openneuro.ds006802.v1.0.0

Examples

>>> from eegdash.dataset import DS006802
>>> dataset = DS006802(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006803(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

NeuroTechs Dataset for Stem Skills

Study:: ds006803 (OpenNeuro)
Author (year):: PechCanul2025
Canonical:: —

Also importable as: DS006803, PechCanul2025.

Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 63; recordings: 126; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006803 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006803 DOI: https://doi.org/10.18112/openneuro.ds006803.v1.1.1

Examples

>>> from eegdash.dataset import DS006803
>>> dataset = DS006803(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006817(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Visual Attribute-Specific Contextual Trajectory Paradigm 2.0

Study:: ds006817 (OpenNeuro)
Author (year):: Lowe2025
Canonical:: VisualContextTrajectory_v2

Also importable as: DS006817, Lowe2025, VisualContextTrajectory_v2.

Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 34; recordings: 34; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006817 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006817 DOI: https://doi.org/10.18112/openneuro.ds006817.v1.0.0

Examples

>>> from eegdash.dataset import DS006817
>>> dataset = DS006817(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['VisualContextTrajectory_v2', 'Lowe2025']

class eegdash.dataset.dataset.DS006839(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG recordings during sham neurofeedback in virtual reality

Study:: ds006839 (OpenNeuro)
Author (year):: Gonzales2025
Canonical:: —

Also importable as: DS006839, Gonzales2025.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 36; recordings: 144; tasks: 4.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006839 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006839 DOI: https://doi.org/10.18112/openneuro.ds006839.v1.0.0

Examples

>>> from eegdash.dataset import DS006839
>>> dataset = DS006839(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006840(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

IACKD: Intention Action Conflict EEG-Hand Kinematics Dataset

Study:: ds006840 (OpenNeuro)
Author (year):: Cai2025
Canonical:: IACKD

Also importable as: DS006840, Cai2025, IACKD.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 15; recordings: 128; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006840 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006840 DOI: https://doi.org/10.18112/openneuro.ds006840.v1.0.0

Examples

>>> from eegdash.dataset import DS006840
>>> dataset = DS006840(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['IACKD']

class eegdash.dataset.dataset.DS006848(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

AlphaDirection1: EEG, ECG, PPG in the resting state and working memory for sequentially and simultaneously presented digits

Study:: ds006848 (OpenNeuro)
Author (year):: Kosachenko2025
Canonical:: —

Also importable as: DS006848, Kosachenko2025.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 30; recordings: 52; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006848 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006848 DOI: https://doi.org/10.18112/openneuro.ds006848.v1.0.0

Examples

>>> from eegdash.dataset import DS006848
>>> dataset = DS006848(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006850(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Urban Appraisal: Physiological Recording during Rating of Different Urban Environments

Study:: ds006850 (OpenNeuro)
Author (year):: Zaehme2025
Canonical:: —

Also importable as: DS006850, Zaehme2025.

Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 63; recordings: 126; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006850 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006850 DOI: https://doi.org/10.18112/openneuro.ds006850.v1.0.0

Examples

>>> from eegdash.dataset import DS006850
>>> dataset = DS006850(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006861(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Targeted Neuromodulation of the Left Dorsolateral Prefrontal Cortex Alleviates Altered Affective Response Evaluation in Lonely Individuals

Study:: ds006861 (OpenNeuro)
Author (year):: Maka2025_Targeted
Canonical:: —

Also importable as: DS006861, Maka2025_Targeted.

Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 120; recordings: 239; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006861 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006861 DOI: https://doi.org/10.18112/openneuro.ds006861.v1.0.2

Examples

>>> from eegdash.dataset import DS006861
>>> dataset = DS006861(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006866(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Discrepancy between self-report and neurophysiological markers of socio-affective responses in lonely individuals

Study:: ds006866 (OpenNeuro)
Author (year):: Maka2025_Discrepancy
Canonical:: —

Also importable as: DS006866, Maka2025_Discrepancy.

Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 148; recordings: 148; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006866 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006866 DOI: https://doi.org/10.18112/openneuro.ds006866.v1.0.0

Examples

>>> from eegdash.dataset import DS006866
>>> dataset = DS006866(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006890(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Longitudinal Multitask Wireless ECoG Data from Two Fully Implanted Macaca fuscata

Study:: ds006890 (OpenNeuro)
Author (year):: Yang2025_Longitudinal
Canonical:: —

Also importable as: DS006890, Yang2025_Longitudinal.

Modality: ieeg; Experiment type: Motor; Subject type: Healthy. Subjects: 2; recordings: 870; tasks: 5.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006890 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006890 DOI: https://doi.org/10.18112/openneuro.ds006890.v1.0.0

Examples

>>> from eegdash.dataset import DS006890
>>> dataset = DS006890(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006902(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Profound neuronal differences during Exercise-Induced Hypoalgesia between athletes and non-athletes revealed by functional near-infrared spectroscopy

Study:: ds006902 (OpenNeuro)
Author (year):: Geisler2025
Canonical:: —

Also importable as: DS006902, Geisler2025.

Modality: fnirs; Experiment type: Perception; Subject type: Healthy. Subjects: 42; recordings: 42; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006902 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006902 DOI: https://doi.org/10.18112/openneuro.ds006902.v1.1.1

Examples

>>> from eegdash.dataset import DS006902
>>> dataset = DS006902(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006903(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

ball_squeeze_2025

Study:: ds006903 (OpenNeuro)
Author (year):: here2025
Canonical:: —

Also importable as: DS006903, here2025.

Modality: fnirs; Experiment type: Motor; Subject type: Healthy. Subjects: 17; recordings: 67; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006903 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006903 DOI: https://doi.org/10.18112/openneuro.ds006903.v1.0.0

Examples

>>> from eegdash.dataset import DS006903
>>> dataset = DS006903(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006910(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Auditory Naming EC

Study:: ds006910 (OpenNeuro)
Author (year):: Kochi2025_Auditory_Naming_EC
Canonical:: —

Also importable as: DS006910, Kochi2025_Auditory_Naming_EC.

Modality: ieeg; Experiment type: Other; Subject type: Unknown. Subjects: 121; recordings: 384; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006910 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006910 DOI: https://doi.org/10.18112/openneuro.ds006910.v1.0.1

Examples

>>> from eegdash.dataset import DS006910
>>> dataset = DS006910(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006914(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Visual Naming EC

Study:: ds006914 (OpenNeuro)
Author (year):: Kochi2025_Visual_Naming_EC
Canonical:: —

Also importable as: DS006914, Kochi2025_Visual_Naming_EC.

Modality: ieeg; Experiment type: Other; Subject type: Epilepsy. Subjects: 110; recordings: 353; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006914 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006914 DOI: https://doi.org/10.18112/openneuro.ds006914.v1.0.3

Examples

>>> from eegdash.dataset import DS006914
>>> dataset = DS006914(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006921(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

High Density Resting State EEG of Phantom Limb Pain and Controls

Study:: ds006921 (OpenNeuro)
Author (year):: Ramne2025
Canonical:: —

Also importable as: DS006921, Ramne2025.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Other. Subjects: 38; recordings: 152; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006921 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006921 DOI: https://doi.org/10.18112/openneuro.ds006921.v1.1.1

Examples

>>> from eegdash.dataset import DS006921
>>> dataset = DS006921(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006923(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Dataset of Electroencephalograms of Juvenile Offenders

Study:: ds006923 (OpenNeuro)
Author (year):: Polo2025
Canonical:: —

Also importable as: DS006923, Polo2025.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Other. Subjects: 140; recordings: 280; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006923 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006923 DOI: https://doi.org/10.18112/openneuro.ds006923.v1.0.0

Examples

>>> from eegdash.dataset import DS006923
>>> dataset = DS006923(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006940(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Dataset: EEG-Controlled Exoskeleton for Walking and Standing - A Longitudinal Study of Healthy Individuals

Study:: ds006940 (OpenNeuro)
Author (year):: Sarkar2025_StudyOF
Canonical:: —

Also importable as: DS006940, Sarkar2025_StudyOF.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 7; recordings: 935; tasks: 15.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006940 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006940 DOI: https://doi.org/10.18112/openneuro.ds006940.v1.0.0

Examples

>>> from eegdash.dataset import DS006940
>>> dataset = DS006940(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006945(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Dataset: T1-Weighted Structural MRI and fMRI of Participants Viewing Self-Avatar Exoskeleton Walking (11 SWS Cycles)

Study:: ds006945 (OpenNeuro)
Author (year):: Sarkar2025_T1_Weighted_Structural
Canonical:: —

Also importable as: DS006945, Sarkar2025_T1_Weighted_Structural.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 5; recordings: 14; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006945 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006945 DOI: https://doi.org/10.18112/openneuro.ds006945.v1.2.1

Examples

>>> from eegdash.dataset import DS006945
>>> dataset = DS006945(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006963(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Motor Control Processes Moderate Visual Working Memory Gating Dataset

Study:: ds006963 (OpenNeuro)
Author (year):: Ozdemir2025
Canonical:: —

Also importable as: DS006963, Ozdemir2025.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 32; recordings: 32; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006963 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006963 DOI: https://doi.org/10.18112/openneuro.ds006963.v1.0.0

Examples

>>> from eegdash.dataset import DS006963
>>> dataset = DS006963(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS006979(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Examining Perceptual Grouping on Stages of Processing in Visual Working Memory: An ERP Study

Study:: ds006979 (OpenNeuro)
Author (year):: Ramzaoui2025
Canonical:: Ramzaoui2024

Also importable as: DS006979, Ramzaoui2025, Ramzaoui2024.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 53; recordings: 56; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006979 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006979 DOI: https://doi.org/10.18112/openneuro.ds006979.v1.0.1

Examples

>>> from eegdash.dataset import DS006979
>>> dataset = DS006979(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Ramzaoui2024']

class eegdash.dataset.dataset.DS007006(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

VR-Compassion Cultivation Training

Study:: ds007006 (OpenNeuro)
Author (year):: Wu2025
Canonical:: —

Also importable as: DS007006, Wu2025.

Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 10; recordings: 50; tasks: 5.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007006 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007006 DOI: https://doi.org/10.18112/openneuro.ds007006.v1.0.0

Examples

>>> from eegdash.dataset import DS007006
>>> dataset = DS007006(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS007020(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG Mortality Dataset in Parkinson’s Disease

Study:: ds007020 (OpenNeuro)
Author (year):: Jamshidi2025
Canonical:: —

Also importable as: DS007020, Jamshidi2025.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Parkinson's. Subjects: 94; recordings: 94; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007020 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007020 DOI: https://doi.org/10.18112/openneuro.ds007020.v1.0.0

Examples

>>> from eegdash.dataset import DS007020
>>> dataset = DS007020(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS007028(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Auditory Cortex Macaque Monkey DISC Data

Study:: ds007028 (OpenNeuro)
Author (year):: Kajikawa2025
Canonical:: Kajikawa2000

Also importable as: DS007028, Kajikawa2025, Kajikawa2000.

Modality: eeg; Experiment type: Perception; Subject type: Other. Subjects: 3; recordings: 3; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007028 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007028 DOI: https://doi.org/10.18112/openneuro.ds007028.v1.0.0

Examples

>>> from eegdash.dataset import DS007028
>>> dataset = DS007028(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Kajikawa2000']

class eegdash.dataset.dataset.DS007052(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

PURSUE N400 Word Processing

Study:: ds007052 (OpenNeuro)
Author (year):: Couperus2025_N400
Canonical:: Couperus2021_N400

Also importable as: DS007052, Couperus2025_N400, Couperus2021_N400.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 288; recordings: 288; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007052 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007052 DOI: https://doi.org/10.18112/openneuro.ds007052.v1.1.2

Examples

>>> from eegdash.dataset import DS007052
>>> dataset = DS007052(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Couperus2021_N400']

class eegdash.dataset.dataset.DS007056(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

PURSUE P300 Visual Oddball

Study:: ds007056 (OpenNeuro)
Author (year):: Couperus2025_P300
Canonical:: Couperus2021_P300

Also importable as: DS007056, Couperus2025_P300, Couperus2021_P300.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 286; recordings: 286; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007056 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007056 DOI: https://doi.org/10.18112/openneuro.ds007056.v1.1.1

Examples

>>> from eegdash.dataset import DS007056
>>> dataset = DS007056(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Couperus2021_P300']

class eegdash.dataset.dataset.DS007069(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

PURSUE MMN Auditory Oddball

Study:: ds007069 (OpenNeuro)
Author (year):: Couperus2025_MMN
Canonical:: Couperus2021_MMN

Also importable as: DS007069, Couperus2025_MMN, Couperus2021_MMN.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 281; recordings: 281; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007069 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007069 DOI: https://doi.org/10.18112/openneuro.ds007069.v1.0.0

Examples

>>> from eegdash.dataset import DS007069
>>> dataset = DS007069(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Couperus2021_MMN']

class eegdash.dataset.dataset.DS007081(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Passive but accessible: Studied information is not actively stored in working memory, yet attended regardless of anticipated load

Study:: ds007081 (OpenNeuro)
Author (year):: Ylmaz2025
Canonical:: —

Also importable as: DS007081, Ylmaz2025.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 41; recordings: 41; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007081 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007081 DOI: https://doi.org/10.18112/openneuro.ds007081.v1.0.0

Examples

>>> from eegdash.dataset import DS007081
>>> dataset = DS007081(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS007095(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

RNS_Epilepsy-iBIDS

Study:: ds007095 (OpenNeuro)
Author (year):: Feng2025
Canonical:: —

Also importable as: DS007095, Feng2025.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 8; recordings: 6019; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007095 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007095 DOI: https://doi.org/10.18112/openneuro.ds007095.v1.0.0

Examples

>>> from eegdash.dataset import DS007095
>>> dataset = DS007095(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS007096(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

PURSUE N170 Face Perception

Study:: ds007096 (OpenNeuro)
Author (year):: Couperus2025_PURSUE_N170_Face
Canonical:: Couperus2017

Also importable as: DS007096, Couperus2025_PURSUE_N170_Face, Couperus2017.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 292; recordings: 292; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007096 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007096 DOI: https://doi.org/10.18112/openneuro.ds007096.v1.0.0

Examples

>>> from eegdash.dataset import DS007096
>>> dataset = DS007096(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Couperus2017']

class eegdash.dataset.dataset.DS007118(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

iEEG_comprehensive_HFA_model_part1

Study:: ds007118 (OpenNeuro)
Author (year):: Hatano2025_part1
Canonical:: Hatano

Also importable as: DS007118, Hatano2025_part1, Hatano.

Modality: ieeg; Experiment type: Sleep; Subject type: Unknown. Subjects: 65; recordings: 82; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007118 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007118 DOI: https://doi.org/10.18112/openneuro.ds007118.v1.0.0

Examples

>>> from eegdash.dataset import DS007118
>>> dataset = DS007118(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Hatano']

class eegdash.dataset.dataset.DS007119(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

iEEG_comprehensive_HFA_model_part3

Study:: ds007119 (OpenNeuro)
Author (year):: Hatano2025_part3
Canonical:: —

Also importable as: DS007119, Hatano2025_part3.

Modality: ieeg; Experiment type: Sleep; Subject type: Unknown. Subjects: 103; recordings: 106; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007119 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007119 DOI: https://doi.org/10.18112/openneuro.ds007119.v1.0.0

Examples

>>> from eegdash.dataset import DS007119
>>> dataset = DS007119(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS007120(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

iEEG_comprehensive_HFA_model_part2

Study:: ds007120 (OpenNeuro)
Author (year):: Hatano2025_part2
Canonical:: —

Also importable as: DS007120, Hatano2025_part2.

Modality: ieeg; Experiment type: Sleep; Subject type: Epilepsy. Subjects: 65; recordings: 70; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007120 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007120 DOI: https://doi.org/10.18112/openneuro.ds007120.v1.0.0

Examples

>>> from eegdash.dataset import DS007120
>>> dataset = DS007120(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS007137(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

PURSUE N2pc Visual Search

Study:: ds007137 (OpenNeuro)
Author (year):: Couperus2025_N2PC
Canonical:: Couperus2021_N2pc

Also importable as: DS007137, Couperus2025_N2PC, Couperus2021_N2pc.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 294; recordings: 294; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007137 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007137 DOI: https://doi.org/10.18112/openneuro.ds007137.v1.0.0

Examples

>>> from eegdash.dataset import DS007137
>>> dataset = DS007137(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Couperus2021_N2pc']

class eegdash.dataset.dataset.DS007139(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

PURSUE LRP/ERN Flanker

Study:: ds007139 (OpenNeuro)
Author (year):: Couperus2025_LRP
Canonical:: Couperus2021_LRP

Also importable as: DS007139, Couperus2025_LRP, Couperus2021_LRP.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 292; recordings: 292; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007139 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007139 DOI: https://doi.org/10.18112/openneuro.ds007139.v1.0.0

Examples

>>> from eegdash.dataset import DS007139
>>> dataset = DS007139(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Couperus2021_LRP']

class eegdash.dataset.dataset.DS007162(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Adaptive recruitment of cortex-wide recurrence for visual object recognition (EEG)

Study:: ds007162 (OpenNeuro)
Author (year):: DS7162_VisualRecognition
Canonical:: —

Also importable as: DS007162, DS7162_VisualRecognition.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 34; recordings: 69; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007162 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007162 DOI: https://doi.org/10.18112/openneuro.ds007162.v1.0.0

Examples

>>> from eegdash.dataset import DS007162
>>> dataset = DS007162(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS007169(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Multimodal Cognitive Workload n-back Task, 4 Difficulties

Study:: ds007169 (OpenNeuro)
Author (year):: Barras2026_Multimodal
Canonical:: Barras2021

Also importable as: DS007169, Barras2026_Multimodal, Barras2021.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 18; recordings: 18; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007169 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007169 DOI: https://doi.org/10.18112/openneuro.ds007169.v1.0.5

Examples

>>> from eegdash.dataset import DS007169
>>> dataset = DS007169(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Barras2021']

class eegdash.dataset.dataset.DS007172(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG-Asymmetries Dataset

Study:: ds007172 (OpenNeuro)
Author (year):: Reinke2026
Canonical:: EEGAsymmetries

Also importable as: DS007172, Reinke2026, EEGAsymmetries.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 100; recordings: 501; tasks: 6.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007172 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007172 DOI: https://doi.org/10.18112/openneuro.ds007172.v1.0.0

Examples

>>> from eegdash.dataset import DS007172
>>> dataset = DS007172(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['EEGAsymmetries']

class eegdash.dataset.dataset.DS007175(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

FFR-active-listening

Study:: ds007175 (OpenNeuro)
Author (year):: DS7175_FFR_ActiveListening
Canonical:: —

Also importable as: DS007175, DS7175_FFR_ActiveListening.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 41; recordings: 41; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007175 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007175 DOI: https://doi.org/10.18112/openneuro.ds007175.v1.0.1

Examples

>>> from eegdash.dataset import DS007175
>>> dataset = DS007175(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS007176(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Longitudinal EEG Test-Retest Reliability in Healthy Individuals

Study:: ds007176 (OpenNeuro)
Author (year):: Isaza2026_Longitudinal
Canonical:: —

Also importable as: DS007176, Isaza2026_Longitudinal.

Modality: eeg; Experiment type: Resting-state; Subject type: Healthy. Subjects: 45; recordings: 300; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007176 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007176 DOI: https://doi.org/10.18112/openneuro.ds007176.v1.0.1

Examples

>>> from eegdash.dataset import DS007176
>>> dataset = DS007176(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS007180(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Exo-EEG Experiment

Study:: ds007180 (OpenNeuro)
Author (year):: FuentesGuerra2026
Canonical:: FuentesGuerra2024

Also importable as: DS007180, FuentesGuerra2026, FuentesGuerra2024.

Modality: eeg; Experiment type: Unknown; Subject type: Healthy. Subjects: 25; recordings: 25; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007180 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007180 DOI: https://doi.org/10.18112/openneuro.ds007180.v1.0.0

Examples

>>> from eegdash.dataset import DS007180
>>> dataset = DS007180(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['FuentesGuerra2024']

class eegdash.dataset.dataset.DS007181(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Structural MRI, Resting-state fMRI, and PSG/EEG Dataset of Zoster-associated Neuralgia

Study:: ds007181 (OpenNeuro)
Author (year):: Li2026
Canonical:: —

Also importable as: DS007181, Li2026.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Other. Subjects: 59; recordings: 59; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007181 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007181 DOI: https://doi.org/10.18112/openneuro.ds007181.v1.0.1

Examples

>>> from eegdash.dataset import DS007181
>>> dataset = DS007181(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS007216(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A multi-session simultaneous EEG-fMRI dataset with online experience sampling

Study:: ds007216 (OpenNeuro)
Author (year):: Kucyi2026
Canonical:: Kucyi2024

Also importable as: DS007216, Kucyi2026, Kucyi2024.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 24; recordings: 187; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007216 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007216 DOI: https://doi.org/10.18112/openneuro.ds007216.v1.0.0

Examples

>>> from eegdash.dataset import DS007216
>>> dataset = DS007216(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Kucyi2024']

class eegdash.dataset.dataset.DS007221(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Cross-Environment Multi-Paradigm Motor Imagery EEG Dataset

Study:: ds007221 (OpenNeuro)
Author (year):: Xinwei2026
Canonical:: —

Also importable as: DS007221, Xinwei2026.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 84; recordings: 1265; tasks: 4.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007221 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007221 DOI: https://doi.org/10.18112/openneuro.ds007221.v1.0.1

Examples

>>> from eegdash.dataset import DS007221
>>> dataset = DS007221(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS007262(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Cognitive Workload 8-level arithmetic

Study:: ds007262 (OpenNeuro)
Author (year):: Barras2026_Cognitive
Canonical:: Barras2025

Also importable as: DS007262, Barras2026_Cognitive, Barras2025.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 18; recordings: 18; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007262 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007262 DOI: https://doi.org/10.18112/openneuro.ds007262.v1.0.6

Examples

>>> from eegdash.dataset import DS007262
>>> dataset = DS007262(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Barras2025']

class eegdash.dataset.dataset.DS007314(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

tACS for Patients with Post-Stroke Anomia

Study:: ds007314 (OpenNeuro)
Author (year):: Martzoukou2026_tACS
Canonical:: Martzoukou2024_Post

Also importable as: DS007314, Martzoukou2026_tACS, Martzoukou2024_Post.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Other. Subjects: 2; recordings: 14; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007314 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007314 DOI: https://doi.org/10.18112/openneuro.ds007314.v1.0.0

Examples

>>> from eegdash.dataset import DS007314
>>> dataset = DS007314(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Martzoukou2024_Post']

class eegdash.dataset.dataset.DS007315(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

tACS for Patients with Post-Stroke Anomia

Study:: ds007315 (OpenNeuro)
Author (year):: Martzoukou2026_tACS_Patients
Canonical:: Martzoukou2024_Post_A

Also importable as: DS007315, Martzoukou2026_tACS_Patients, Martzoukou2024_Post_A.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Other. Subjects: 2; recordings: 14; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007315 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007315 DOI: https://doi.org/10.18112/openneuro.ds007315.v1.0.1

Examples

>>> from eegdash.dataset import DS007315
>>> dataset = DS007315(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Martzoukou2024_Post_A']

class eegdash.dataset.dataset.DS007322(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Personalized smartphone notifications bias auditory salience across processing stages

Study:: ds007322 (OpenNeuro)
Author (year):: Mishra2026
Canonical:: Mishra2024

Also importable as: DS007322, Mishra2026, Mishra2024.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 57; recordings: 57; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007322 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007322 DOI: https://doi.org/10.18112/openneuro.ds007322.v1.0.1

Examples

>>> from eegdash.dataset import DS007322
>>> dataset = DS007322(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Mishra2024']

class eegdash.dataset.dataset.DS007338(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEGEyeNet Dataset

Study:: ds007338 (OpenNeuro)
Author (year):: Plomecka2026
Canonical:: EEGEyeNet_v2, EEGEYENET

Also importable as: DS007338, Plomecka2026, EEGEyeNet_v2, EEGEYENET.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007338 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007338 DOI: https://doi.org/10.18112/openneuro.ds007338.v1.0.0

Examples

>>> from eegdash.dataset import DS007338
>>> dataset = DS007338(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['EEGEyeNet_v2', 'EEGEYENET']

class eegdash.dataset.dataset.DS007347(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Sterotactic Focused Ultrasound Mesencephalotomy for the Treatment of Head and Neck Cancer Pain

Study:: ds007347 (OpenNeuro)
Author (year):: Elias2026
Canonical:: —

Also importable as: DS007347, Elias2026.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Cancer. Subjects: 5; recordings: 10; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007347 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007347 DOI: https://doi.org/10.18112/openneuro.ds007347.v1.0.0

Examples

>>> from eegdash.dataset import DS007347
>>> dataset = DS007347(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS007353(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

HAD-MEEG

Study:: ds007353 (OpenNeuro)
Author (year):: Zhang2026
Canonical:: HAD_MEEG, HADMEEG

Also importable as: DS007353, Zhang2026, HAD_MEEG, HADMEEG.

Modality: eeg, meg; Experiment type: Perception; Subject type: Healthy. Subjects: 32; recordings: 473; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007353 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007353 DOI: https://doi.org/10.18112/openneuro.ds007353.v1.0.0

Examples

>>> from eegdash.dataset import DS007353
>>> dataset = DS007353(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HAD_MEEG', 'HADMEEG']

class eegdash.dataset.dataset.DS007358(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A subset of large-scale EEG dataset (India + Tanzania)

Study:: ds007358 (OpenNeuro)
Author (year):: Vianney2026
Canonical:: Vianney2025

Also importable as: DS007358, Vianney2026, Vianney2025.

Modality: eeg; Experiment type: Resting-state; Subject type: Healthy. Subjects: 2000; recordings: 6000; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007358 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007358 DOI: https://doi.org/10.18112/openneuro.ds007358.v1.0.0

Examples

>>> from eegdash.dataset import DS007358
>>> dataset = DS007358(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Vianney2025']

class eegdash.dataset.dataset.DS007406(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG dataset on consumer responses to extreme versus traditional marketing videos

Study:: ds007406 (OpenNeuro)
Author (year):: Edit2026
Canonical:: Edit2024

Also importable as: DS007406, Edit2026, Edit2024.

Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 10; recordings: 10; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007406 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007406 DOI: https://doi.org/10.18112/openneuro.ds007406.v1.0.0

Examples

>>> from eegdash.dataset import DS007406
>>> dataset = DS007406(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Edit2024']

class eegdash.dataset.dataset.DS007420(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A Light Weight Multi-Distance fNIRS Dataset for Ball-Squeezing Task and Purposeful Motion Artifact Creation Task

Study:: ds007420 (OpenNeuro)
Author (year):: Gao2026_Light_Weight_Multi
Canonical:: Gao2024

Also importable as: DS007420, Gao2026_Light_Weight_Multi, Gao2024.

Modality: fnirs; Experiment type: Motor; Subject type: Healthy. Subjects: 12; recordings: 60; tasks: 4.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007420 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007420 DOI: https://doi.org/10.18112/openneuro.ds007420.v1.0.2

Examples

>>> from eegdash.dataset import DS007420
>>> dataset = DS007420(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Gao2024']

class eegdash.dataset.dataset.DS007427(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Comprehensive methodology for sample enrichment in EEG biomarker studies for Alzheimer’s risk classification

Study:: ds007427 (OpenNeuro)
Author (year):: Isaza2026_Comprehensive
Canonical:: HenaoIsaza2026

Also importable as: DS007427, Isaza2026_Comprehensive, HenaoIsaza2026.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Dementia. Subjects: 44; recordings: 44; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007427 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007427 DOI: https://doi.org/10.18112/openneuro.ds007427.v1.0.1

Examples

>>> from eegdash.dataset import DS007427
>>> dataset = DS007427(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HenaoIsaza2026']

class eegdash.dataset.dataset.DS007431(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Diffuse predictions stabilize and reshape neural code during memory encoding

Study:: ds007431 (OpenNeuro)
Author (year):: Ataseven2026
Canonical:: Ataseven2024

Also importable as: DS007431, Ataseven2026, Ataseven2024.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 47; recordings: 47; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007431 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007431 DOI: https://doi.org/10.18112/openneuro.ds007431.v1.0.0

Examples

>>> from eegdash.dataset import DS007431
>>> dataset = DS007431(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Ataseven2024']

class eegdash.dataset.dataset.DS007445(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Thalamocortical ictal iEEG dataset

Study:: ds007445 (OpenNeuro)
Author (year):: Panchavati2026
Canonical:: —

Also importable as: DS007445, Panchavati2026.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 19; recordings: 66; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007445 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007445 DOI: https://doi.org/10.18112/openneuro.ds007445.v1.0.2

Examples

>>> from eegdash.dataset import DS007445
>>> dataset = DS007445(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS007454(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A common neural mechanism underlies experiences of passage of time

Study:: ds007454 (OpenNeuro)
Author (year):: DS7454_TimePerception
Canonical:: —

Also importable as: DS007454, DS7454_TimePerception.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 42; recordings: 42; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007454 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007454 DOI: https://doi.org/10.18112/openneuro.ds007454.v1.0.1

Examples

>>> from eegdash.dataset import DS007454
>>> dataset = DS007454(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS007463(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Very-High-Density Diffuse Optical Tomography System Validation Dataset

Study:: ds007463 (OpenNeuro)
Author (year):: Fogarty2026_Very
Canonical:: Fogarty2025

Also importable as: DS007463, Fogarty2026_Very, Fogarty2025.

Modality: fnirs; Experiment type: Perception; Subject type: Healthy. Subjects: 8; recordings: 88; tasks: 14.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007463 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007463 DOI: https://doi.org/10.18112/openneuro.ds007463.v1.1.1

Examples

>>> from eegdash.dataset import DS007463
>>> dataset = DS007463(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Fogarty2025']

class eegdash.dataset.dataset.DS007471(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Joint agency EEG dataset

Study:: ds007471 (OpenNeuro)
Author (year):: Zhou2026
Canonical:: Zhou2024

Also importable as: DS007471, Zhou2026, Zhou2024.

Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 31; recordings: 31; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007471 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007471 DOI: https://doi.org/10.18112/openneuro.ds007471.v1.0.0

Examples

>>> from eegdash.dataset import DS007471
>>> dataset = DS007471(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Zhou2024']

class eegdash.dataset.dataset.DS007473(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

High-Density Diffuse Optical Tomography Audiovisual Movie Viewing Dataset

Study:: ds007473 (OpenNeuro)
Author (year):: Fogarty2026_High
Canonical:: Tripathy2024

Also importable as: DS007473, Fogarty2026_High, Tripathy2024.

Modality: fnirs; Experiment type: Perception; Subject type: Healthy. Subjects: 5; recordings: 189; tasks: 19.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007473 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007473 DOI: https://doi.org/10.18112/openneuro.ds007473.v1.0.0

Examples

>>> from eegdash.dataset import DS007473
>>> dataset = DS007473(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Tripathy2024']

class eegdash.dataset.dataset.DS007477(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

TimeSeries BIDS converted

Study:: ds007477 (OpenNeuro)
Author (year):: Niu2026
Canonical:: —

Also importable as: DS007477, Niu2026.

Modality: fnirs; Experiment type: Unknown; Subject type: Unknown. Subjects: 18; recordings: 36; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007477 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007477 DOI: https://doi.org/10.18112/openneuro.ds007477.v1.0.1

Examples

>>> from eegdash.dataset import DS007477
>>> dataset = DS007477(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS007521(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The effect of hunger and state preferences on the neural processing of food images

Study:: ds007521 (OpenNeuro)
Author (year):: Moerel2026
Canonical:: Moerel2025

Also importable as: DS007521, Moerel2026, Moerel2025.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 23; recordings: 46; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007521 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007521 DOI: https://doi.org/10.18112/openneuro.ds007521.v1.0.1

Examples

>>> from eegdash.dataset import DS007521
>>> dataset = DS007521(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Moerel2025']

class eegdash.dataset.dataset.DS007523(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

LPP MEG Listen

Study:: ds007523 (OpenNeuro)
Author (year):: Bel2026
Canonical:: Dascoli2025

Also importable as: DS007523, Bel2026, Dascoli2025.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 58; recordings: 579; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007523 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007523 DOI: https://doi.org/10.18112/openneuro.ds007523.v1.0.0

Examples

>>> from eegdash.dataset import DS007523
>>> dataset = DS007523(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Dascoli2025']

class eegdash.dataset.dataset.DS007524(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

LittlePrince_MEG_French_Read_Pallier2025

Study:: ds007524 (OpenNeuro)
Author (year):: Pallier2025
Canonical:: LittlePrince

Also importable as: DS007524, Pallier2025, LittlePrince.

Modality: meg; Experiment type: Other; Subject type: Healthy. Subjects: 50; recordings: 500; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007524 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007524 DOI: https://doi.org/10.18112/openneuro.ds007524.v1.0.1

Examples

>>> from eegdash.dataset import DS007524
>>> dataset = DS007524(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['LittlePrince']

class eegdash.dataset.dataset.DS007526(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

PD-EEG: Resting-State & Walking EEG in Parkinson’s Disease

Study:: ds007526 (OpenNeuro)
Author (year):: Katzir2026
Canonical:: PD_EEG, PDEEG

Also importable as: DS007526, Katzir2026, PD_EEG, PDEEG.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Parkinson's. Subjects: 144; recordings: 277; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007526 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007526 DOI: https://doi.org/10.18112/openneuro.ds007526.v1.0.0

Examples

>>> from eegdash.dataset import DS007526
>>> dataset = DS007526(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['PD_EEG', 'PDEEG']

class eegdash.dataset.dataset.DS007554(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Multimodal dataset from the CMx7-MM Experiment

Study:: ds007554 (OpenNeuro)
Author (year):: Ajra2026
Canonical:: —

Also importable as: DS007554, Ajra2026.

Modality: eeg, fnirs; Experiment type: Other; Subject type: Healthy. Subjects: 30; recordings: 1034; tasks: 7.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007554 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007554 DOI: https://doi.org/10.18112/openneuro.ds007554.v1.0.0

Examples

>>> from eegdash.dataset import DS007554
>>> dataset = DS007554(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS007558(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG Pre/Post Intervention Dataset

Study:: ds007558 (OpenNeuro)
Author (year):: Qi2026
Canonical:: —

Also importable as: DS007558, Qi2026.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Unknown. Subjects: 67; recordings: 121; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007558 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007558 DOI: https://doi.org/10.18112/openneuro.ds007558.v1.0.0

Examples

>>> from eegdash.dataset import DS007558
>>> dataset = DS007558(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.DS007591(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Delineating neural contributions to EEG-based speech decoding

Study:: ds007591 (OpenNeuro)
Author (year):: Sato2026_Delineating
Canonical:: Sato2025

Also importable as: DS007591, Sato2026_Delineating, Sato2025.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 3; recordings: 21; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007591 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007591 DOI: https://doi.org/10.18112/openneuro.ds007591.v1.0.1

Examples

>>> from eegdash.dataset import DS007591
>>> dataset = DS007591(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Sato2025']

class eegdash.dataset.dataset.DS007602(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG-Speech Brain Decoding Dataset

Study:: ds007602 (OpenNeuro)
Author (year):: Sato2026_Speech
Canonical:: Sato2024

Also importable as: DS007602, Sato2026_Speech, Sato2024.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 3; recordings: 113; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007602 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007602 DOI: https://doi.org/10.18112/openneuro.ds007602.v1.0.1

Examples

>>> from eegdash.dataset import DS007602
>>> dataset = DS007602(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Sato2024']

class eegdash.dataset.dataset.DS007609(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Resting-State EEG and Trait Anxiety

Study:: ds007609 (OpenNeuro)
Author (year):: Shalamberidze2026
Canonical:: Shalamberidze2025

Also importable as: DS007609, Shalamberidze2026, Shalamberidze2025.

Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 51; recordings: 51; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007609 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007609 DOI: https://doi.org/10.18112/openneuro.ds007609.v1.0.0

Examples

>>> from eegdash.dataset import DS007609
>>> dataset = DS007609(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Shalamberidze2025']

class eegdash.dataset.dataset.DS007615(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

LDAEP and resting-state EEG in healthy women

Study:: ds007615 (OpenNeuro)
Author (year):: Normannseth2026
Canonical:: —

Also importable as: DS007615, Normannseth2026.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 69; recordings: 192; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007615 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007615 DOI: https://doi.org/10.18112/openneuro.ds007615.v1.0.0

Examples

>>> from eegdash.dataset import DS007615
>>> dataset = DS007615(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Normannseth2026']

eegdash.dataset.dataset.Dascoli2025[source]: alias of DS007523

eegdash.dataset.dataset.Delorme[source]: alias of DS003061

eegdash.dataset.dataset.Dubois2024[source]: alias of DS006545

class eegdash.dataset.dataset.EEG2025R1(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 1 (BDF Converted)

Study:: EEG2025r1 (NeMAR)
Author (year):: Shirazi2024_R1_bdf
Canonical:: HBN_r1_bdf

Also importable as: EEG2025R1, Shirazi2024_R1_bdf, HBN_r1_bdf.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 136; recordings: 1342; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r1 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r1 DOI: https://doi.org/10.18112/openneuro.ds005505.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R1
>>> dataset = EEG2025R1(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r1_bdf']

class eegdash.dataset.dataset.EEG2025R10(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 10 (BDF Converted)

Study:: EEG2025r10 (NeMAR)
Author (year):: Shirazi2025_R10_bdf
Canonical:: HBN_r10_bdf

Also importable as: EEG2025R10, Shirazi2025_R10_bdf, HBN_r10_bdf.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 533; recordings: 2516; tasks: 8.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r10 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r10

Examples

>>> from eegdash.dataset import EEG2025R10
>>> dataset = EEG2025R10(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r10_bdf']

class eegdash.dataset.dataset.EEG2025R10MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 10 (BDF Converted)

Study:: EEG2025r10mini (NeMAR)
Author (year):: Shirazi2025_R10_bdf_mini
Canonical:: HBN_r10_bdf_mini

Also importable as: EEG2025R10MINI, Shirazi2025_R10_bdf_mini, HBN_r10_bdf_mini.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 20; recordings: 220; tasks: 8.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r10mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r10mini

Examples

>>> from eegdash.dataset import EEG2025R10MINI
>>> dataset = EEG2025R10MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r10_bdf_mini']

class eegdash.dataset.dataset.EEG2025R11(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 11 (BDF Converted)

Study:: EEG2025r11 (NeMAR)
Author (year):: Shirazi2025_R11_bdf
Canonical:: HBN_r11_bdf

Also importable as: EEG2025R11, Shirazi2025_R11_bdf, HBN_r11_bdf.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 430; recordings: 3397; tasks: 8.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r11 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r11

Examples

>>> from eegdash.dataset import EEG2025R11
>>> dataset = EEG2025R11(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r11_bdf']

class eegdash.dataset.dataset.EEG2025R11MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 11 (BDF Converted)

Study:: EEG2025r11mini (NeMAR)
Author (year):: Shirazi2025_R11_bdf_mini
Canonical:: HBN_r11_bdf_mini

Also importable as: EEG2025R11MINI, Shirazi2025_R11_bdf_mini, HBN_r11_bdf_mini.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 20; recordings: 220; tasks: 8.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r11mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r11mini

Examples

>>> from eegdash.dataset import EEG2025R11MINI
>>> dataset = EEG2025R11MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r11_bdf_mini']

class eegdash.dataset.dataset.EEG2025R1MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 1 (BDF Converted)

Study:: EEG2025r1mini (NeMAR)
Author (year):: Shirazi2024_R1_bdf_mini
Canonical:: HBN_r1_bdf_mini

Also importable as: EEG2025R1MINI, Shirazi2024_R1_bdf_mini, HBN_r1_bdf_mini.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 20; recordings: 239; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r1mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r1mini DOI: https://doi.org/10.18112/openneuro.ds005505.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R1MINI
>>> dataset = EEG2025R1MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r1_bdf_mini']

class eegdash.dataset.dataset.EEG2025R2(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 2 (BDF Converted)

Study:: EEG2025r2 (NeMAR)
Author (year):: Shirazi2024_R2_bdf
Canonical:: HBN_r2_bdf

Also importable as: EEG2025R2, Shirazi2024_R2_bdf, HBN_r2_bdf.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 150; recordings: 1405; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r2 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r2 DOI: https://doi.org/10.18112/openneuro.ds005506.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R2
>>> dataset = EEG2025R2(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r2_bdf']

class eegdash.dataset.dataset.EEG2025R2MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 2 (BDF Converted)

Study:: EEG2025r2mini (NeMAR)
Author (year):: Shirazi2024_R2_bdf_mini
Canonical:: HBN_r2_bdf_mini

Also importable as: EEG2025R2MINI, Shirazi2024_R2_bdf_mini, HBN_r2_bdf_mini.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 20; recordings: 240; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r2mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r2mini DOI: https://doi.org/10.18112/openneuro.ds005506.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R2MINI
>>> dataset = EEG2025R2MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r2_bdf_mini']

class eegdash.dataset.dataset.EEG2025R3(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 3 (BDF Converted)

Study:: EEG2025r3 (NeMAR)
Author (year):: Shirazi2024_R3_bdf
Canonical:: HBN_r3_bdf

Also importable as: EEG2025R3, Shirazi2024_R3_bdf, HBN_r3_bdf.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 184; recordings: 1812; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r3 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r3 DOI: https://doi.org/10.18112/openneuro.ds005507.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R3
>>> dataset = EEG2025R3(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r3_bdf']

class eegdash.dataset.dataset.EEG2025R3MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 3 (BDF Converted)

Study:: EEG2025r3mini (NeMAR)
Author (year):: Shirazi2024_R3_bdf_mini
Canonical:: HBN_r3_bdf_mini

Also importable as: EEG2025R3MINI, Shirazi2024_R3_bdf_mini, HBN_r3_bdf_mini.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 20; recordings: 240; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r3mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r3mini DOI: https://doi.org/10.18112/openneuro.ds005507.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R3MINI
>>> dataset = EEG2025R3MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r3_bdf_mini']

class eegdash.dataset.dataset.EEG2025R4(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 4 (BDF Converted)

Study:: EEG2025r4 (NeMAR)
Author (year):: Shirazi2024_R4_bdf
Canonical:: HBN_r4_bdf

Also importable as: EEG2025R4, Shirazi2024_R4_bdf, HBN_r4_bdf.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 324; recordings: 3342; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r4 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r4 DOI: https://doi.org/10.18112/openneuro.ds005508.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R4
>>> dataset = EEG2025R4(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r4_bdf']

class eegdash.dataset.dataset.EEG2025R4MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 4 (BDF Converted)

Study:: EEG2025r4mini (NeMAR)
Author (year):: Shirazi2024_R4_bdf_mini
Canonical:: HBN_r4_bdf_mini

Also importable as: EEG2025R4MINI, Shirazi2024_R4_bdf_mini, HBN_r4_bdf_mini.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 20; recordings: 240; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r4mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r4mini DOI: https://doi.org/10.18112/openneuro.ds005508.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R4MINI
>>> dataset = EEG2025R4MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r4_bdf_mini']

class eegdash.dataset.dataset.EEG2025R5(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 5 (BDF Converted)

Study:: EEG2025r5 (NeMAR)
Author (year):: Shirazi2024_R5_bdf
Canonical:: HBN_r5_bdf

Also importable as: EEG2025R5, Shirazi2024_R5_bdf, HBN_r5_bdf.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 330; recordings: 3326; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r5 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r5 DOI: https://doi.org/10.18112/openneuro.ds005509.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R5
>>> dataset = EEG2025R5(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r5_bdf']

class eegdash.dataset.dataset.EEG2025R5MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 5 (BDF Converted)

Study:: EEG2025r5mini (NeMAR)
Author (year):: Shirazi2024_R5_bdf_mini
Canonical:: HBN_r5_bdf_mini

Also importable as: EEG2025R5MINI, Shirazi2024_R5_bdf_mini, HBN_r5_bdf_mini.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 20; recordings: 240; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r5mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r5mini DOI: https://doi.org/10.18112/openneuro.ds005509.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R5MINI
>>> dataset = EEG2025R5MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r5_bdf_mini']

class eegdash.dataset.dataset.EEG2025R6(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 6 (BDF Converted)

Study:: EEG2025r6 (NeMAR)
Author (year):: Shirazi2024_R6_bdf
Canonical:: HBN_r6_bdf

Also importable as: EEG2025R6, Shirazi2024_R6_bdf, HBN_r6_bdf.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 135; recordings: 1227; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r6 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r6 DOI: https://doi.org/10.18112/openneuro.ds005510.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R6
>>> dataset = EEG2025R6(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r6_bdf']

class eegdash.dataset.dataset.EEG2025R6MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 6 (BDF Converted)

Study:: EEG2025r6mini (NeMAR)
Author (year):: Shirazi2024_R6_bdf_mini
Canonical:: HBN_r6_bdf_mini

Also importable as: EEG2025R6MINI, Shirazi2024_R6_bdf_mini, HBN_r6_bdf_mini.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 20; recordings: 237; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r6mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r6mini DOI: https://doi.org/10.18112/openneuro.ds005510.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R6MINI
>>> dataset = EEG2025R6MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r6_bdf_mini']

class eegdash.dataset.dataset.EEG2025R7(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 7 (BDF Converted)

Study:: EEG2025r7 (NeMAR)
Author (year):: Shirazi2024_R7_bdf
Canonical:: HBN_r7_bdf

Also importable as: EEG2025R7, Shirazi2024_R7_bdf, HBN_r7_bdf.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 381; recordings: 3100; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r7 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r7 DOI: https://doi.org/10.18112/openneuro.ds005511.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R7
>>> dataset = EEG2025R7(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r7_bdf']

class eegdash.dataset.dataset.EEG2025R7MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 7 (BDF Converted)

Study:: EEG2025r7mini (NeMAR)
Author (year):: Shirazi2024_R7_bdf_mini
Canonical:: HBN_r7_bdf_mini

Also importable as: EEG2025R7MINI, Shirazi2024_R7_bdf_mini, HBN_r7_bdf_mini.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 20; recordings: 239; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r7mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r7mini DOI: https://doi.org/10.18112/openneuro.ds005511.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R7MINI
>>> dataset = EEG2025R7MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r7_bdf_mini']

class eegdash.dataset.dataset.EEG2025R8(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 8 (BDF Converted)

Study:: EEG2025r8 (NeMAR)
Author (year):: Shirazi2024_R8_bdf
Canonical:: HBN_r8_bdf

Also importable as: EEG2025R8, Shirazi2024_R8_bdf, HBN_r8_bdf.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 257; recordings: 2320; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r8 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r8 DOI: https://doi.org/10.18112/openneuro.ds005512.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R8
>>> dataset = EEG2025R8(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r8_bdf']

class eegdash.dataset.dataset.EEG2025R8MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 8 (BDF Converted)

Study:: EEG2025r8mini (NeMAR)
Author (year):: Shirazi2024_R8_bdf_mini
Canonical:: HBN_r8_bdf_mini

Also importable as: EEG2025R8MINI, Shirazi2024_R8_bdf_mini, HBN_r8_bdf_mini.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 20; recordings: 238; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r8mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r8mini DOI: https://doi.org/10.18112/openneuro.ds005512.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R8MINI
>>> dataset = EEG2025R8MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r8_bdf_mini']

class eegdash.dataset.dataset.EEG2025R9(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 9 (BDF Converted)

Study:: EEG2025r9 (NeMAR)
Author (year):: Shirazi2024_R9_bdf
Canonical:: HBN_r9_bdf

Also importable as: EEG2025R9, Shirazi2024_R9_bdf, HBN_r9_bdf.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 295; recordings: 2885; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r9 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r9 DOI: https://doi.org/10.18112/openneuro.ds005514.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R9
>>> dataset = EEG2025R9(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r9_bdf']

class eegdash.dataset.dataset.EEG2025R9MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network (HBN) EEG - Release 9 (BDF Converted)

Study:: EEG2025r9mini (NeMAR)
Author (year):: Shirazi2024_R9_bdf_mini
Canonical:: HBN_r9_bdf_mini

Also importable as: EEG2025R9MINI, Shirazi2024_R9_bdf_mini, HBN_r9_bdf_mini.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 20; recordings: 237; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r9mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r9mini DOI: https://doi.org/10.18112/openneuro.ds005514.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R9MINI
>>> dataset = EEG2025R9MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HBN_r9_bdf_mini']

eegdash.dataset.dataset.EEGAsymmetries[source]: alias of DS007172

class eegdash.dataset.dataset.EEGChallengeDataset(release: str, cache_dir: str, mini: bool = True, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A dataset helper for the EEG 2025 Challenge.

This class simplifies access to the EEG 2025 Challenge datasets. It is a specialized version of EEGDashDataset that is pre-configured for the challenge’s data releases. It automatically maps a release name (e.g., “R1”) to the corresponding OpenNeuro dataset and handles the selection of subject subsets (e.g., “mini” release).

Parameters:

release (str) – The name of the challenge release to load. Must be one of the keys in RELEASE_TO_OPENNEURO_DATASET_MAP (e.g., “R1”, “R2”, …, “R11”).
cache_dir (str) – The local directory where the dataset will be downloaded and cached.
mini (bool, default True) – If True, the dataset is restricted to the official “mini” subset of subjects for the specified release. If False, all subjects for the release are included.
query (dict, optional) – An additional MongoDB-style query to apply as a filter. This query is combined with the release and subject filters using a logical AND. The query must not contain the dataset key, as this is determined by the release parameter.
s3_bucket (str, optional) – The base S3 bucket URI where the challenge data is stored. Defaults to the official challenge bucket.
**kwargs – Additional keyword arguments that are passed directly to the EEGDashDataset constructor.

Raises:

ValueError – If the specified release is unknown, or if the query argument contains a dataset key. Also raised if mini is True and a requested subject is not part of the official mini-release subset.

See also

EEGDashDataset: The base class for creating datasets from queries.

class eegdash.dataset.dataset.EEGDashDataset(cache_dir: str | Path, query: dict[str, Any] = None, description_fields: list[str] | None = None, s3_bucket: str | None = None, records: list[dict] | None = None, download: bool = True, n_jobs: int = -1, eeg_dash_instance: Any = None, database: str | None = None, auth_token: str | None = None, on_error: str = 'raise', **kwargs)[source]

Bases: BaseConcatDataset

Create a new EEGDashDataset from a given query or local BIDS dataset directory and dataset name. An EEGDashDataset is pooled collection of EEGDashBaseDataset instances (individual recordings) and is a subclass of braindecode’s BaseConcatDataset.

Examples

Basic usage with dataset and subject filtering:

>>> from eegdash import EEGDashDataset
>>> dataset = EEGDashDataset(
...     cache_dir="./data",
...     dataset="ds002718",
...     subject="012"
... )
>>> print(f"Number of recordings: {len(dataset)}")

Filter by multiple subjects and specific task:

>>> subjects = ["012", "013", "014"]
>>> dataset = EEGDashDataset(
...     cache_dir="./data",
...     dataset="ds002718",
...     subject=subjects,
...     task="RestingState"
... )

Load and inspect EEG data from recordings:

>>> if len(dataset) > 0:
...     recording = dataset[0]
...     raw = recording.load()
...     print(f"Sampling rate: {raw.info['sfreq']} Hz")
...     print(f"Number of channels: {len(raw.ch_names)}")
...     print(f"Duration: {raw.times[-1]:.1f} seconds")

Advanced filtering with raw MongoDB queries:

>>> from eegdash import EEGDashDataset
>>> query = {
...     "dataset": "ds002718",
...     "subject": {"$in": ["012", "013"]},
...     "task": "RestingState"
... }
>>> dataset = EEGDashDataset(cache_dir="./data", query=query)

Working with dataset collections and braindecode integration:

>>> # EEGDashDataset is a braindecode BaseConcatDataset
>>> for i, recording in enumerate(dataset):
...     if i >= 2:  # limit output
...         break
...     print(f"Recording {i}: {recording.description}")
...     raw = recording.load()
...     print(f"  Channels: {len(raw.ch_names)}, Duration: {raw.times[-1]:.1f}s")

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Raw MongoDB query to filter records. If provided, it is merged with keyword filtering arguments (see **kwargs) using logical AND. You must provide at least a dataset (either in query or as a keyword argument). Only fields in ALLOWED_QUERY_FIELDS are considered for filtering.
dataset (str) – Dataset identifier (e.g., "ds002718"). Required if query does not already specify a dataset.
task (str | list[str]) – Task name(s) to filter by (e.g., "RestingState").
subject (str | list[str]) – Subject identifier(s) to filter by (e.g., "NDARCA153NKE").
session (str | list[str]) – Session identifier(s) to filter by (e.g., "1").
run (str | list[str]) – Run identifier(s) to filter by (e.g., "1").
description_fields (list[str]) – Fields to extract from each record and include in dataset descriptions (e.g., “subject”, “session”, “run”, “task”).
s3_bucket (str | None) – Optional S3 bucket URI (e.g., “s3://mybucket”) to use instead of the default OpenNeuro bucket when downloading data files.
records (list[dict] | None) – Pre-fetched metadata records. If provided, the dataset is constructed directly from these records and no MongoDB query is performed.
download (bool, default True) – If False, load from local BIDS files only. Local data are expected under cache_dir / dataset; no DB or S3 access is attempted.
n_jobs (int) – Number of parallel jobs to use where applicable (-1 uses all cores).
eeg_dash_instance (EEGDash | None) – Optional existing EEGDash client to reuse for DB queries. If None, a new client is created on demand, not used in the case of no download.
database (str | None) – Database name to use (e.g., “eegdash”, “eegdash_staging”). If None, uses the default database.
auth_token (str | None) – Authentication token for accessing protected databases. Required for staging or admin operations.
on_error (str, default "raise") –
How to handle DataIntegrityError when accessing .raw on individual recordings:
- "raise" (default): propagate the exception.
- "warn": log the error as a warning and set .raw to None.
- "skip": silently set .raw to None.
Use drop_bad() after iteration to remove skipped recordings.
**kwargs (dict) –
Additional keyword arguments serving two purposes:
- Filtering: any keys present in ALLOWED_QUERY_FIELDS are treated as query filters (e.g., dataset, subject, task, …).
- Dataset options: remaining keys are forwarded to EEGDashRaw.

property cumulative_sizes: list[int]

Recompute cumulative sizes from current dataset lengths.

Overrides the cached version from BaseConcatDataset because individual dataset lengths can change after lazy raw loading (estimated ntimes from JSON metadata may differ from actual n_times in the raw file).

download_all(n_jobs: int | None = None) → None[source]

Download missing remote files in parallel.

Parameters:: n_jobs (int | None) – Number of parallel workers to use. If None, defaults to self.n_jobs.

drop_bad() → list[dict][source]

Remove skipped datasets and return their records.

Call after accessing .raw on all datasets (e.g. after iteration or preprocessing) to clean up the dataset list.

Returns:: Records that were removed because loading failed.
Return type:: list of dict

drop_short(min_samples: int) → list[dict][source]

Remove recordings shorter than min_samples and return their records.

This is useful when downstream processing (e.g., fixed-length windowing) requires a minimum number of samples per recording. Recordings whose .raw is None (failed to load) are also dropped.

Parameters:: min_samples (int) – Minimum number of time-domain samples a recording must have to be kept.
Returns:: Records that were removed.
Return type:: list of dict

save(path, overwrite=False)[source]

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

eegdash.dataset.dataset.EEGEYENET[source]: alias of DS007338

eegdash.dataset.dataset.EEGEyeNet[source]: alias of DS005872

eegdash.dataset.dataset.EEGEyeNet_v2[source]: alias of DS007338

eegdash.dataset.dataset.EEGMotorMovementImagery[source]: alias of DS004362

eegdash.dataset.dataset.EESM17[source]: alias of DS004348

eegdash.dataset.dataset.EESM19[source]: alias of DS005185

eegdash.dataset.dataset.EESM23[source]: alias of DS005178

eegdash.dataset.dataset.EPFLP300[source]: alias of NM000231

eegdash.dataset.dataset.EPFLP300Dataset[source]: alias of NM000231

eegdash.dataset.dataset.EPFL_P300[source]: alias of NM000231

eegdash.dataset.dataset.ERDetect[source]: alias of DS004774

eegdash.dataset.dataset.ERPCORE[source]: alias of NM000132

eegdash.dataset.dataset.ERP_CORE[source]: alias of NM000132

eegdash.dataset.dataset.ER_Detect[source]: alias of DS004774

eegdash.dataset.dataset.Edit2024[source]: alias of DS007406

eegdash.dataset.dataset.EldBETA[source]: alias of NM000130

eegdash.dataset.dataset.Ester2022[source]: alias of DS004519

eegdash.dataset.dataset.Ester2024_E1[source]: alias of DS004521

eegdash.dataset.dataset.Ester2024_E2[source]: alias of DS004520

eegdash.dataset.dataset.FACED[source]: alias of NM000112

eegdash.dataset.dataset.FLUX[source]: alias of DS004346

eegdash.dataset.dataset.FRL_DiscreteGestures[source]: alias of NM000105

eegdash.dataset.dataset.FRL_Handwriting[source]: alias of NM000106

eegdash.dataset.dataset.FRL_WristControl[source]: alias of NM000107

eegdash.dataset.dataset.FernandezRodriguez2023[source]: alias of NM000240

eegdash.dataset.dataset.Ferron2019[source]: alias of DS004541

eegdash.dataset.dataset.Flankers_FAR[source]: alias of DS005868

eegdash.dataset.dataset.Flankers_NEAR[source]: alias of DS005866

eegdash.dataset.dataset.Fogarty2025[source]: alias of DS007463

eegdash.dataset.dataset.Formica2025[source]: alias of DS005406

eegdash.dataset.dataset.ForrestGump_MEG[source]: alias of DS003633

eegdash.dataset.dataset.FuentesGuerra2024[source]: alias of DS007180

eegdash.dataset.dataset.Gama2019[source]: alias of DS005420

eegdash.dataset.dataset.Gao2024[source]: alias of DS007420

eegdash.dataset.dataset.Gao2026[source]: alias of NM000242

eegdash.dataset.dataset.Ghaffari2024[source]: alias of DS006547

eegdash.dataset.dataset.GuttmannFlury2025_ME[source]: alias of NM000227

eegdash.dataset.dataset.GuttmannFlury2025_MIME[source]: alias of NM000235

eegdash.dataset.dataset.HADMEEG[source]: alias of DS007353

eegdash.dataset.dataset.HAD_MEEG[source]: alias of DS007353

eegdash.dataset.dataset.HBN_EEG_NC[source]: alias of NM000103

eegdash.dataset.dataset.HBN_NoCommercial[source]: alias of NM000103

eegdash.dataset.dataset.HBN_r1[source]: alias of DS005505

eegdash.dataset.dataset.HBN_r10[source]: alias of DS005515

eegdash.dataset.dataset.HBN_r10_bdf[source]: alias of EEG2025R10

eegdash.dataset.dataset.HBN_r10_bdf_mini[source]: alias of EEG2025R10MINI

eegdash.dataset.dataset.HBN_r11[source]: alias of DS005516

eegdash.dataset.dataset.HBN_r11_bdf[source]: alias of EEG2025R11

eegdash.dataset.dataset.HBN_r11_bdf_mini[source]: alias of EEG2025R11MINI

eegdash.dataset.dataset.HBN_r1_bdf[source]: alias of EEG2025R1

eegdash.dataset.dataset.HBN_r1_bdf_mini[source]: alias of EEG2025R1MINI

eegdash.dataset.dataset.HBN_r2[source]: alias of DS005506

eegdash.dataset.dataset.HBN_r2_bdf[source]: alias of EEG2025R2

eegdash.dataset.dataset.HBN_r2_bdf_mini[source]: alias of EEG2025R2MINI

eegdash.dataset.dataset.HBN_r3[source]: alias of DS005507

eegdash.dataset.dataset.HBN_r3_bdf[source]: alias of EEG2025R3

eegdash.dataset.dataset.HBN_r3_bdf_mini[source]: alias of EEG2025R3MINI

eegdash.dataset.dataset.HBN_r4[source]: alias of DS005508

eegdash.dataset.dataset.HBN_r4_bdf[source]: alias of EEG2025R4

eegdash.dataset.dataset.HBN_r4_bdf_mini[source]: alias of EEG2025R4MINI

eegdash.dataset.dataset.HBN_r5[source]: alias of DS005509

eegdash.dataset.dataset.HBN_r5_bdf[source]: alias of EEG2025R5

eegdash.dataset.dataset.HBN_r5_bdf_mini[source]: alias of EEG2025R5MINI

eegdash.dataset.dataset.HBN_r6[source]: alias of DS005510

eegdash.dataset.dataset.HBN_r6_bdf[source]: alias of EEG2025R6

eegdash.dataset.dataset.HBN_r6_bdf_mini[source]: alias of EEG2025R6MINI

eegdash.dataset.dataset.HBN_r7_bdf[source]: alias of EEG2025R7

eegdash.dataset.dataset.HBN_r7_bdf_mini[source]: alias of EEG2025R7MINI

eegdash.dataset.dataset.HBN_r8[source]: alias of DS005512

eegdash.dataset.dataset.HBN_r8_bdf[source]: alias of EEG2025R8

eegdash.dataset.dataset.HBN_r8_bdf_mini[source]: alias of EEG2025R8MINI

eegdash.dataset.dataset.HBN_r9[source]: alias of DS005514

eegdash.dataset.dataset.HBN_r9_bdf[source]: alias of EEG2025R9

eegdash.dataset.dataset.HBN_r9_bdf_mini[source]: alias of EEG2025R9MINI

eegdash.dataset.dataset.HEFMIICH[source]: alias of NM000347

eegdash.dataset.dataset.HEFMI_ICH[source]: alias of NM000347

eegdash.dataset.dataset.HID[source]: alias of DS004851

eegdash.dataset.dataset.HUPiEEG[source]: alias of DS004100

eegdash.dataset.dataset.Hatano[source]: alias of DS007118

eegdash.dataset.dataset.Haupt2025[source]: alias of DS004951

eegdash.dataset.dataset.HealthyBrainNetwork[source]: alias of NM000103

eegdash.dataset.dataset.HeartBEAM[source]: alias of DS006466

eegdash.dataset.dataset.HenaoIsaza2026[source]: alias of DS007427

eegdash.dataset.dataset.Hermann2021[source]: alias of DS003352

eegdash.dataset.dataset.Hermes2024[source]: alias of DS006392

eegdash.dataset.dataset.Herrema2024[source]: alias of DS005494

eegdash.dataset.dataset.Hinss2021[source]: alias of NM000206

eegdash.dataset.dataset.Hinss2021_v2[source]: alias of NM000343

eegdash.dataset.dataset.Huang2022[source]: alias of DS004457

eegdash.dataset.dataset.Huebner2017[source]: alias of NM000199

eegdash.dataset.dataset.Huebner2018[source]: alias of NM000195

eegdash.dataset.dataset.HySER[source]: alias of NM000108

eegdash.dataset.dataset.Hyser[source]: alias of NM000108

eegdash.dataset.dataset.IACKD[source]: alias of DS006840

eegdash.dataset.dataset.Jao2020[source]: alias of NM000249

eegdash.dataset.dataset.Johnson2024[source]: alias of DS004850

eegdash.dataset.dataset.Johnson2025[source]: alias of DS004852

eegdash.dataset.dataset.Kajikawa2000[source]: alias of DS007028

eegdash.dataset.dataset.Kalenkovich2019[source]: alias of DS003703

eegdash.dataset.dataset.Kanno2025[source]: alias of DS005545

eegdash.dataset.dataset.Kekecs2024[source]: alias of DS004572

eegdash.dataset.dataset.Kidder2024[source]: alias of DS004278

eegdash.dataset.dataset.Kim2025[source]: alias of NM000127

eegdash.dataset.dataset.Kinley2019[source]: alias of DS006446

eegdash.dataset.dataset.Kitazawa2025[source]: alias of DS005007

eegdash.dataset.dataset.Kucyi2024[source]: alias of DS007216

eegdash.dataset.dataset.Kuroda2024[source]: alias of DS006107

eegdash.dataset.dataset.LEMON[source]: alias of NM000179

eegdash.dataset.dataset.LPP[source]: alias of DS005345

eegdash.dataset.dataset.LeganesFonteneau2024[source]: alias of DS006159

eegdash.dataset.dataset.Lin2019[source]: alias of DS006035

eegdash.dataset.dataset.LittlePrince[source]: alias of DS007524

eegdash.dataset.dataset.Liu2022EldBETA[source]: alias of NM000130

eegdash.dataset.dataset.Lowe2025[source]: alias of DS006817

eegdash.dataset.dataset.Luke2019[source]: alias of DS005964

eegdash.dataset.dataset.MAMEM2[source]: alias of NM000120

eegdash.dataset.dataset.MAMEM2_SSVEP[source]: alias of NM000120

eegdash.dataset.dataset.MAMEM3[source]: alias of NM000121

eegdash.dataset.dataset.MASC_MEG[source]: alias of NM000229

eegdash.dataset.dataset.MAVIS[source]: alias of DS004010

eegdash.dataset.dataset.MEGMEM[source]: alias of DS003694

eegdash.dataset.dataset.MEG_MASC[source]: alias of NM000229

eegdash.dataset.dataset.MEG_SCANS[source]: alias of DS006468

eegdash.dataset.dataset.MNESomato[source]: alias of DS003104

eegdash.dataset.dataset.MNESomatoData[source]: alias of DS003104

eegdash.dataset.dataset.MNE_Sample_Data[source]: alias of DS000248

eegdash.dataset.dataset.MSSV[source]: alias of DS006366

eegdash.dataset.dataset.MUSING[source]: alias of DS003774

eegdash.dataset.dataset.Maestu2021[source]: alias of DS003483

eegdash.dataset.dataset.Martzoukou2024_Post[source]: alias of DS007314

eegdash.dataset.dataset.Martzoukou2024_Post_A[source]: alias of DS007315

eegdash.dataset.dataset.Melcon2024[source]: alias of DS006171

eegdash.dataset.dataset.Mendola2020[source]: alias of DS002001

eegdash.dataset.dataset.Mesquita2019[source]: alias of DS005963

eegdash.dataset.dataset.MetaRDK[source]: alias of DS006253

eegdash.dataset.dataset.Mheich2020[source]: alias of DS002791

eegdash.dataset.dataset.Mheich2024[source]: alias of DS002833

eegdash.dataset.dataset.Miller2021[source]: alias of DS003708

eegdash.dataset.dataset.Mishra2024[source]: alias of DS007322

eegdash.dataset.dataset.Mivalt2024[source]: alias of DS004624

eegdash.dataset.dataset.Moerel2023[source]: alias of DS004995

eegdash.dataset.dataset.Moerel2025[source]: alias of DS007521

eegdash.dataset.dataset.Moradi2024[source]: alias of DS004598

eegdash.dataset.dataset.Motion_Yucel2014[source]: alias of DS005929

class eegdash.dataset.dataset.NM000103(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Healthy Brain Network EEG - Not for Commercial Use

Study:: nm000103 (NeMAR)
Author (year):: Shirazi2017
Canonical:: HealthyBrainNetwork, HBN_EEG_NC, HBN_NoCommercial

Also importable as: NM000103, Shirazi2017, HealthyBrainNetwork, HBN_EEG_NC, HBN_NoCommercial.

Modality: eeg. Subjects: 447; recordings: 3522; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000103 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000103 DOI: https://doi.org/10.82901/nemar.nm000103

Examples

>>> from eegdash.dataset import NM000103
>>> dataset = NM000103(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HealthyBrainNetwork', 'HBN_EEG_NC', 'HBN_NoCommercial']

class eegdash.dataset.dataset.NM000104(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface Electromyography

Study:: nm000104 (NeMAR)
Author (year):: Sivakumar2024
Canonical:: emg2qwerty

Also importable as: NM000104, Sivakumar2024, emg2qwerty.

Modality: emg. Subjects: 108; recordings: 1136; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000104 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000104 DOI: https://doi.org/10.82901/nemar.nm000104

Examples

>>> from eegdash.dataset import NM000104
>>> dataset = NM000104(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['emg2qwerty']

class eegdash.dataset.dataset.NM000105(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

FRL Discrete Gestures: Hand Gesture Recognition from Surface Electromyography

Study:: nm000105 (NeMAR)
Author (year):: Kaifosh2025
Canonical:: FRL_DiscreteGestures

Also importable as: NM000105, Kaifosh2025, FRL_DiscreteGestures.

Modality: emg. Subjects: 100; recordings: 100; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000105 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000105 DOI: https://doi.org/10.82901/nemar.nm000105

Examples

>>> from eegdash.dataset import NM000105
>>> dataset = NM000105(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['FRL_DiscreteGestures']

class eegdash.dataset.dataset.NM000106(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

FRL Handwriting: Handwriting Decoding from Surface Electromyography

Study:: nm000106 (NeMAR)
Author (year):: Kaifosh2025_106
Canonical:: FRL_Handwriting

Also importable as: NM000106, Kaifosh2025_106, FRL_Handwriting.

Modality: emg. Subjects: 100; recordings: 807; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000106 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000106 DOI: https://doi.org/10.82901/nemar.nm000106

Examples

>>> from eegdash.dataset import NM000106
>>> dataset = NM000106(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['FRL_Handwriting']

class eegdash.dataset.dataset.NM000107(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

FRL Wrist Control: Wrist Movement Decoding from Surface Electromyography

Study:: nm000107 (NeMAR)
Author (year):: Kaifosh2025_107
Canonical:: FRL_WristControl

Also importable as: NM000107, Kaifosh2025_107, FRL_WristControl.

Modality: emg. Subjects: 100; recordings: 182; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000107 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000107 DOI: https://doi.org/10.82901/nemar.nm000107

Examples

>>> from eegdash.dataset import NM000107
>>> dataset = NM000107(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['FRL_WristControl']

class eegdash.dataset.dataset.NM000108(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

HySER: High-Density Surface Electromyogram Recordings

Study:: nm000108 (NeMAR)
Author (year):: Jiang2021
Canonical:: HySER, Hyser

Also importable as: NM000108, Jiang2021, HySER, Hyser.

Modality: emg. Subjects: 20; recordings: 1514; tasks: 38.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000108 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000108 DOI: https://doi.org/10.82901/nemar.nm000108

Examples

>>> from eegdash.dataset import NM000108
>>> dataset = NM000108(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HySER', 'Hyser']

class eegdash.dataset.dataset.NM000109(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG During Mental Arithmetic Tasks

Study:: nm000109 (NeMAR)
Author (year):: Zyma2019
Canonical:: —

Also importable as: NM000109, Zyma2019.

Modality: eeg. Subjects: 36; recordings: 72; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000109 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000109 DOI: https://doi.org/10.82901/nemar.nm000109

Examples

>>> from eegdash.dataset import NM000109
>>> dataset = NM000109(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000110(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

CHB-MIT

Study:: nm000110 (NeMAR)
Author (year):: Connolly2010
Canonical:: CHBMIT, CHB_MIT

Also importable as: NM000110, Connolly2010, CHBMIT, CHB_MIT.

Modality: eeg. Subjects: 24; recordings: 686; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000110 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000110 DOI: https://doi.org/10.82901/nemar.nm000110

Examples

>>> from eegdash.dataset import NM000110
>>> dataset = NM000110(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['CHBMIT', 'CHB_MIT']

class eegdash.dataset.dataset.NM000112(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

FACED - Finer-grained Affective Computing EEG Dataset

Study:: nm000112 (NeMAR)
Author (year):: Liu2024_112
Canonical:: FACED

Also importable as: NM000112, Liu2024_112, FACED.

Modality: eeg. Subjects: 123; recordings: 123; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000112 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000112 DOI: https://doi.org/10.82901/nemar.nm000112

Examples

>>> from eegdash.dataset import NM000112
>>> dataset = NM000112(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['FACED']

class eegdash.dataset.dataset.NM000113(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

2020 BCI competition, track 3

Study:: nm000113 (NeMAR)
Author (year):: Lee2020
Canonical:: —

Also importable as: NM000113, Lee2020.

Modality: eeg. Subjects: 15; recordings: 45; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000113 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000113 DOI: https://doi.org/10.82901/nemar.nm000113

Examples

>>> from eegdash.dataset import NM000113
>>> dataset = NM000113(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000114(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

MDD Patients and Healthy Controls EEG Data

Study:: nm000114 (NeMAR)
Author (year):: Mumtaz2017
Canonical:: —

Also importable as: NM000114, Mumtaz2017.

Modality: eeg. Subjects: 64; recordings: 181; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000114 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000114 DOI: https://doi.org/10.82901/nemar.nm000114

Examples

>>> from eegdash.dataset import NM000114
>>> dataset = NM000114(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000115(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Zhou2016

Study:: nm000115 (NeMAR)
Author (year):: Zhou2016
Canonical:: —

Also importable as: NM000115, Zhou2016.

Modality: eeg. Subjects: 4; recordings: 24; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000115 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000115 DOI: https://doi.org/10.82901/nemar.nm000115

Examples

>>> from eegdash.dataset import NM000115
>>> dataset = NM000115(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000118(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Nakanishi2015 – SSVEP Nakanishi 2015 dataset

Study:: nm000118 (NeMAR)
Author (year):: Nakanishi2015
Canonical:: —

Also importable as: NM000118, Nakanishi2015.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 9; recordings: 9; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000118 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000118

Examples

>>> from eegdash.dataset import NM000118
>>> dataset = NM000118(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000119(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Oikonomou2016 – SSVEP MAMEM 1 dataset

Study:: nm000119 (NeMAR)
Author (year):: Oikonomou2016_MAMEM1
Canonical:: Oikonomou2016

Also importable as: NM000119, Oikonomou2016_MAMEM1, Oikonomou2016.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 11; recordings: 47; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000119 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000119

Examples

>>> from eegdash.dataset import NM000119
>>> dataset = NM000119(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Oikonomou2016']

class eegdash.dataset.dataset.NM000120(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Oikonomou2016 – SSVEP MAMEM 2 dataset

Study:: nm000120 (NeMAR)
Author (year):: Oikonomou2016_MAMEM2
Canonical:: MAMEM2, SSVEPMAMEM2, MAMEM2_SSVEP

Also importable as: NM000120, Oikonomou2016_MAMEM2, MAMEM2, SSVEPMAMEM2, MAMEM2_SSVEP.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 11; recordings: 55; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000120 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000120

Examples

>>> from eegdash.dataset import NM000120
>>> dataset = NM000120(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['MAMEM2', 'SSVEPMAMEM2', 'MAMEM2_SSVEP']

class eegdash.dataset.dataset.NM000121(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Oikonomou2016 – SSVEP MAMEM 3 dataset

Study:: nm000121 (NeMAR)
Author (year):: Oikonomou2016_MAMEM3
Canonical:: MAMEM3, SSVEP_MAMEM3

Also importable as: NM000121, Oikonomou2016_MAMEM3, MAMEM3, SSVEP_MAMEM3.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 11; recordings: 110; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000121 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000121

Examples

>>> from eegdash.dataset import NM000121
>>> dataset = NM000121(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['MAMEM3', 'SSVEP_MAMEM3']

class eegdash.dataset.dataset.NM000122(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Chen2017 – Single-flicker online SSVEP BCI dataset

Study:: nm000122 (NeMAR)
Author (year):: Chen2017
Canonical:: —

Also importable as: NM000122, Chen2017.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 12; recordings: 12; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000122 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000122

Examples

>>> from eegdash.dataset import NM000122
>>> dataset = NM000122(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000123(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Kalunga2016 – SSVEP Exo dataset

Study:: nm000123 (NeMAR)
Author (year):: Kalunga2016
Canonical:: —

Also importable as: NM000123, Kalunga2016.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 12; recordings: 30; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000123 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000123

Examples

>>> from eegdash.dataset import NM000123
>>> dataset = NM000123(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000124(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Han2024 – SSVEP fatigue dataset with two frequency paradigms

Study:: nm000124 (NeMAR)
Author (year):: Han2024
Canonical:: —

Also importable as: NM000124, Han2024.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 24; recordings: 48; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000124 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000124

Examples

>>> from eegdash.dataset import NM000124
>>> dataset = NM000124(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000125(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Lee2021 – SSVEP paradigm of the Mobile BCI dataset

Study:: nm000125 (NeMAR)
Author (year):: Lee2021_SSVEP
Canonical:: —

Also importable as: NM000125, Lee2021_SSVEP.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 23; recordings: 85; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000125 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000125

Examples

>>> from eegdash.dataset import NM000125
>>> dataset = NM000125(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000126(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Wang2016 – SSVEP Wang 2016 dataset

Study:: nm000126 (NeMAR)
Author (year):: Wang2016
Canonical:: —

Also importable as: NM000126, Wang2016.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 34; recordings: 34; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000126 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000126

Examples

>>> from eegdash.dataset import NM000126
>>> dataset = NM000126(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000127(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Kim2025 – 40-class beta-range SSVEP speller dataset

Study:: nm000127 (NeMAR)
Author (year):: Kim2025_SSVEP
Canonical:: Kim2025

Also importable as: NM000127, Kim2025_SSVEP, Kim2025.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 40; recordings: 240; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000127 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000127

Examples

>>> from eegdash.dataset import NM000127
>>> dataset = NM000127(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Kim2025']

class eegdash.dataset.dataset.NM000128(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Dong2023 – 59-subject 40-class SSVEP dataset

Study:: nm000128 (NeMAR)
Author (year):: Dong2023
Canonical:: —

Also importable as: NM000128, Dong2023.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 59; recordings: 59; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000128 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000128

Examples

>>> from eegdash.dataset import NM000128
>>> dataset = NM000128(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000129(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Liu2020 – BETA SSVEP benchmark dataset

Study:: nm000129 (NeMAR)
Author (year):: Liu2020
Canonical:: BetaSSVEP, BETA_SSVEP, BETA

Also importable as: NM000129, Liu2020, BetaSSVEP, BETA_SSVEP, BETA.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 70; recordings: 70; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000129 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000129

Examples

>>> from eegdash.dataset import NM000129
>>> dataset = NM000129(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BetaSSVEP', 'BETA_SSVEP', 'BETA']

class eegdash.dataset.dataset.NM000130(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Liu2022 – eldBETA SSVEP benchmark dataset for elderly population

Study:: nm000130 (NeMAR)
Author (year):: Liu2022
Canonical:: EldBETA, eldBETA, Liu2022EldBETA

Also importable as: NM000130, Liu2022, EldBETA, eldBETA, Liu2022EldBETA.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 100; recordings: 700; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000130 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000130

Examples

>>> from eegdash.dataset import NM000130
>>> dataset = NM000130(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['EldBETA', 'eldBETA', 'Liu2022EldBETA']

class eegdash.dataset.dataset.NM000131(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Wang2021 – Combined SSVEP dataset with single stimulus location for two inputs

Study:: nm000131 (NeMAR)
Author (year):: Wang2021
Canonical:: —

Also importable as: NM000131, Wang2021.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 8; recordings: 22; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000131 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000131

Examples

>>> from eegdash.dataset import NM000131
>>> dataset = NM000131(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000132(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

ERP CORE

Study:: nm000132 (NeMAR)
Author (year):: Kappenman2021
Canonical:: ERPCORE, ERP_CORE

Also importable as: NM000132, Kappenman2021, ERPCORE, ERP_CORE.

Modality: eeg. Subjects: 40; recordings: 240; tasks: 6.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000132 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000132 DOI: https://doi.org/10.82901/nemar.nm000132

Examples

>>> from eegdash.dataset import NM000132
>>> dataset = NM000132(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['ERPCORE', 'ERP_CORE']

class eegdash.dataset.dataset.NM000133(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Alljoined1

Study:: nm000133 (NeMAR)
Author (year):: Xu2024
Canonical:: Alljoined1, Alljoined

Also importable as: NM000133, Xu2024, Alljoined1, Alljoined.

Modality: eeg. Subjects: 8; recordings: 13; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000133 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000133 DOI: https://doi.org/10.82901/nemar.nm000133

Examples

>>> from eegdash.dataset import NM000133
>>> dataset = NM000133(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Alljoined1', 'Alljoined']

class eegdash.dataset.dataset.NM000134(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Alljoined-1.6M

Study:: nm000134 (NeMAR)
Author (year):: Xu2025
Canonical:: Alljoined16M, Alljoined_16M, Alljoined1p6M

Also importable as: NM000134, Xu2025, Alljoined16M, Alljoined_16M, Alljoined1p6M.

Modality: eeg. Subjects: 20; recordings: 1525; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000134 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000134 DOI: https://doi.org/10.82901/nemar.nm000134

Examples

>>> from eegdash.dataset import NM000134
>>> dataset = NM000134(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Alljoined16M', 'Alljoined_16M', 'Alljoined1p6M']

class eegdash.dataset.dataset.NM000135(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BNCI 2014-004 Motor Imagery dataset

Study:: nm000135 (NeMAR)
Author (year):: Leeb2014
Canonical:: BNCI2014004

Also importable as: NM000135, Leeb2014, BNCI2014004.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 1; recordings: 5; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000135 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000135

Examples

>>> from eegdash.dataset import NM000135
>>> dataset = NM000135(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BNCI2014004']

class eegdash.dataset.dataset.NM000136(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

GuttmannFlury2025-P300

Study:: nm000136 (NeMAR)
Author (year):: GuttmannFlury2025
Canonical:: —

Also importable as: NM000136, GuttmannFlury2025.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 31; recordings: 63; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000136 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000136 DOI: https://doi.org/10.1038/s41597-025-04861-9

Examples

>>> from eegdash.dataset import NM000136
>>> dataset = NM000136(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000137(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Classical motor imagery dataset with left hand, right hand, and rest

Study:: nm000137 (NeMAR)
Author (year):: Kaya2018
Canonical:: —

Also importable as: NM000137, Kaya2018.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 7; recordings: 17; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000137 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000137

Examples

>>> from eegdash.dataset import NM000137
>>> dataset = NM000137(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000138(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Alex Motor Imagery dataset

Study:: nm000138 (NeMAR)
Author (year):: Barachant2012
Canonical:: AlexMI, AlexMotorImagery, AlexandreMotorImagery

Also importable as: NM000138, Barachant2012, AlexMI, AlexMotorImagery, AlexandreMotorImagery.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 8; recordings: 8; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000138 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000138

Examples

>>> from eegdash.dataset import NM000138
>>> dataset = NM000138(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['AlexMI', 'AlexMotorImagery', 'AlexandreMotorImagery']

class eegdash.dataset.dataset.NM000139(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BNCI 2014-001 Motor Imagery dataset

Study:: nm000139 (NeMAR)
Author (year):: Tangermann2014
Canonical:: BNCI2014001, BCICIV1, BCICompIV1

Also importable as: NM000139, Tangermann2014, BNCI2014001, BCICIV1, BCICompIV1.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 9; recordings: 108; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000139 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000139

Examples

>>> from eegdash.dataset import NM000139
>>> dataset = NM000139(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BNCI2014001', 'BCICIV1', 'BCICompIV1']

class eegdash.dataset.dataset.NM000140(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BNCI 2015-001 Motor Imagery dataset

Study:: nm000140 (NeMAR)
Author (year):: Faller2015
Canonical:: BNCI2015, BNCI2015001

Also importable as: NM000140, Faller2015, BNCI2015, BNCI2015001.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 12; recordings: 28; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000140 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000140

Examples

>>> from eegdash.dataset import NM000140
>>> dataset = NM000140(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BNCI2015', 'BNCI2015001']

class eegdash.dataset.dataset.NM000141(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Motor execution dataset from Wairagkar et al 2018

Study:: nm000141 (NeMAR)
Author (year):: Wairagkar2018
Canonical:: —

Also importable as: NM000141, Wairagkar2018.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 14; recordings: 14; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000141 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000141

Examples

>>> from eegdash.dataset import NM000141
>>> dataset = NM000141(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000142(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Ear-EEG motor execution dataset from Wu et al 2020

Study:: nm000142 (NeMAR)
Author (year):: Wu2020
Canonical:: —

Also importable as: NM000142, Wu2020.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 6; recordings: 13; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000142 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000142

Examples

>>> from eegdash.dataset import NM000142
>>> dataset = NM000142(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000143(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BNCI2003_IVa Motor Imagery dataset

Study:: nm000143 (NeMAR)
Author (year):: BNCI2003
Canonical:: BCICIII_IVa, BCICompIII_IVa, BNCI2003_IVa

Also importable as: NM000143, BNCI2003, BCICIII_IVa, BCICompIII_IVa, BNCI2003_IVa.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 5; recordings: 5; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000143 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000143

Examples

>>> from eegdash.dataset import NM000143
>>> dataset = NM000143(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BCICIII_IVa', 'BCICompIII_IVa', 'BNCI2003_IVa']

class eegdash.dataset.dataset.NM000144(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BNCI 2015-004 Mental tasks dataset

Study:: nm000144 (NeMAR)
Author (year):: Scherer2015
Canonical:: —

Also importable as: NM000144, Scherer2015.

Modality: eeg; Experiment type: Motor; Subject type: Other. Subjects: 9; recordings: 18; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000144 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000144

Examples

>>> from eegdash.dataset import NM000144
>>> dataset = NM000144(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000145(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Munich Motor Imagery dataset

Study:: nm000145 (NeMAR)
Author (year):: GrosseWentrup2009
Canonical:: —

Also importable as: NM000145, GrosseWentrup2009.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 10; recordings: 10; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000145 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000145

Examples

>>> from eegdash.dataset import NM000145
>>> dataset = NM000145(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000146(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Motor Imagery dataset from Weibo et al 2014

Study:: nm000146 (NeMAR)
Author (year):: Yi2014
Canonical:: Weibo2014

Also importable as: NM000146, Yi2014, Weibo2014.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 10; recordings: 10; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000146 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000146

Examples

>>> from eegdash.dataset import NM000146
>>> dataset = NM000146(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Weibo2014']

class eegdash.dataset.dataset.NM000147(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

RomaniBF2025ERP

Study:: nm000147 (NeMAR)
Author (year):: RomaniBF2025
Canonical:: Romani2025

Also importable as: NM000147, RomaniBF2025, Romani2025.

Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 22; recordings: 120; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000147 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000147 DOI: https://doi.org/10.48550/arXiv.2510.10169

Examples

>>> from eegdash.dataset import NM000147
>>> dataset = NM000147(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Romani2025']

class eegdash.dataset.dataset.NM000148(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Motor imagery BCI dataset with pupillometry augmentation

Study:: nm000148 (NeMAR)
Author (year):: Rozado2015
Canonical:: —

Also importable as: NM000148, Rozado2015.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 30; recordings: 60; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000148 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000148

Examples

>>> from eegdash.dataset import NM000148
>>> dataset = NM000148(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000149(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BNCI 2019-001 Motor Imagery dataset for Spinal Cord Injury patients

Study:: nm000149 (NeMAR)
Author (year):: Ofner2019
Canonical:: —

Also importable as: NM000149, Ofner2019.

Modality: eeg; Experiment type: Motor; Subject type: Other. Subjects: 10; recordings: 90; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000149 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000149

Examples

>>> from eegdash.dataset import NM000149
>>> dataset = NM000149(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000150(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Liu2025 - NEMAR Dataset

Study:: nm000150 (NeMAR)
Author (year):: Liu2025_NEMAR
Canonical:: —

Also importable as: NM000150, Liu2025_NEMAR.

Modality: eeg. Subjects: 0; recordings: 0; tasks: 0.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000150 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000150

Examples

>>> from eegdash.dataset import NM000150
>>> dataset = NM000150(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000151(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Motor imagery dataset for three imaginary states of the same upper extremity

Study:: nm000151 (NeMAR)
Author (year):: Tavakolan2017
Canonical:: —

Also importable as: NM000151, Tavakolan2017.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 12; recordings: 46; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000151 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000151

Examples

>>> from eegdash.dataset import NM000151
>>> dataset = NM000151(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000152(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Upper-limb elbow-centered motor imagery dataset (10 classes)

Study:: nm000152 (NeMAR)
Author (year):: Zhang2017
Canonical:: —

Also importable as: NM000152, Zhang2017.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 12; recordings: 180; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000152 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000152

Examples

>>> from eegdash.dataset import NM000152
>>> dataset = NM000152(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000155(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

MUniverse Caillet et al 2023

Study:: nm000155 (NeMAR)
Author (year):: Caillet2023
Canonical:: —

Also importable as: NM000155, Caillet2023.

Modality: emg. Subjects: 6; recordings: 11; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000155 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000155 DOI: https://doi.org/https://doi.org/10.7910/DVN/F9GWIW

Examples

>>> from eegdash.dataset import NM000155
>>> dataset = NM000155(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000157(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Mainsah2025-B

Study:: nm000157 (NeMAR)
Author (year):: Mainsah2025
Canonical:: —

Also importable as: NM000157, Mainsah2025.

Modality: eeg. Subjects: 19; recordings: 544; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000157 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000157 DOI: https://doi.org/10.13026/0byy-ry86

Examples

>>> from eegdash.dataset import NM000157
>>> dataset = NM000157(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000158(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Dataset [1]_ from the study on motor imagery [2]_

Study:: nm000158 (NeMAR)
Author (year):: Liu2024
Canonical:: —

Also importable as: NM000158, Liu2024.

Modality: eeg; Experiment type: Motor; Subject type: Other. Subjects: 50; recordings: 50; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000158 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000158

Examples

>>> from eegdash.dataset import NM000158
>>> dataset = NM000158(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000159(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

MUniverse Avrillon et al 2024

Study:: nm000159 (NeMAR)
Author (year):: Avrillon2024
Canonical:: —

Also importable as: NM000159, Avrillon2024.

Modality: emg. Subjects: 16; recordings: 124; tasks: 8.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000159 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000159 DOI: https://doi.org/https://doi.org/10.7910/DVN/L9OQY7

Examples

>>> from eegdash.dataset import NM000159
>>> dataset = NM000159(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000160(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Multi-joint upper-limb MI dataset from Yi et al. 2025

Study:: nm000160 (NeMAR)
Author (year):: Yi2025
Canonical:: —

Also importable as: NM000160, Yi2025.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 18; recordings: 141; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000160 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000160

Examples

>>> from eegdash.dataset import NM000160
>>> dataset = NM000160(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000161(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BNCI 2024-001 Handwritten Character Classification dataset

Study:: nm000161 (NeMAR)
Author (year):: Crell2024
Canonical:: —

Also importable as: NM000161, Crell2024.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 20; recordings: 40; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000161 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000161

Examples

>>> from eegdash.dataset import NM000161
>>> dataset = NM000161(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000162(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BNCI 2025-001 Motor Kinematics Reaching dataset

Study:: nm000162 (NeMAR)
Author (year):: Srisrisawang2025
Canonical:: BNCI2025

Also importable as: NM000162, Srisrisawang2025, BNCI2025.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 20; recordings: 20; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000162 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000162

Examples

>>> from eegdash.dataset import NM000162
>>> dataset = NM000162(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BNCI2025']

class eegdash.dataset.dataset.NM000163(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

c-VEP and Burst-VEP dataset from Castillos et al. (2023)

Study:: nm000163 (NeMAR)
Author (year):: Castillos2023_VEP
Canonical:: —

Also importable as: NM000163, Castillos2023_VEP.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 12; recordings: 12; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000163 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000163

Examples

>>> from eegdash.dataset import NM000163
>>> dataset = NM000163(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000165(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

MUniverse Grison et al 2025

Study:: nm000165 (NeMAR)
Author (year):: Grison2025
Canonical:: —

Also importable as: NM000165, Grison2025.

Modality: emg. Subjects: 1; recordings: 10; tasks: 10.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000165 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000165 DOI: https://doi.org/https://doi.org/10.7910/DVN/ID1WNQ

Examples

>>> from eegdash.dataset import NM000165
>>> dataset = NM000165(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000166(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

M3CV: Multi-subject, Multi-session, Multi-task EEG Database

Study:: nm000166 (NeMAR)
Author (year):: Huang2018
Canonical:: —

Also importable as: NM000166, Huang2018.

Modality: eeg. Subjects: 95; recordings: 2469; tasks: 13.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000166 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000166 DOI: https://doi.org/10.1016/j.neuroimage.2022.119666

Examples

>>> from eegdash.dataset import NM000166
>>> dataset = NM000166(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000167(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Motor imagery dataset from Ma et al. 2020

Study:: nm000167 (NeMAR)
Author (year):: Ma2020
Canonical:: —

Also importable as: NM000167, Ma2020.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 25; recordings: 375; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000167 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000167

Examples

>>> from eegdash.dataset import NM000167
>>> dataset = NM000167(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000168(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BNCI 2015-013 Error-Related Potentials dataset

Study:: nm000168 (NeMAR)
Author (year):: Chavarriaga2015
Canonical:: Chavarriaga2010

Also importable as: NM000168, Chavarriaga2015, Chavarriaga2010.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 6; recordings: 120; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000168 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000168

Examples

>>> from eegdash.dataset import NM000168
>>> dataset = NM000168(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Chavarriaga2010']

class eegdash.dataset.dataset.NM000169(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BNCI 2014-008 P300 dataset (ALS patients)

Study:: nm000169 (NeMAR)
Author (year):: Riccio2014
Canonical:: BNCI2014008

Also importable as: NM000169, Riccio2014, BNCI2014008.

Modality: eeg; Experiment type: Attention; Subject type: Other. Subjects: 8; recordings: 8; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000169 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000169

Examples

>>> from eegdash.dataset import NM000169
>>> dataset = NM000169(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BNCI2014008']

class eegdash.dataset.dataset.NM000170(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BNCI 2025-002 Continuous 2D Trajectory Decoding dataset

Study:: nm000170 (NeMAR)
Author (year):: Pulferer2025
Canonical:: —

Also importable as: NM000170, Pulferer2025.

Modality: eeg; Experiment type: Motor; Subject type: Other. Subjects: 10; recordings: 90; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000170 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000170

Examples

>>> from eegdash.dataset import NM000170
>>> dataset = NM000170(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000171(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BNCI 2014-002 Motor Imagery dataset

Study:: nm000171 (NeMAR)
Author (year):: Steyrl2014
Canonical:: BNCI2014002

Also importable as: NM000171, Steyrl2014, BNCI2014002.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 14; recordings: 112; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000171 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000171

Examples

>>> from eegdash.dataset import NM000171
>>> dataset = NM000171(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BNCI2014002']

class eegdash.dataset.dataset.NM000172(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

High-gamma dataset described in Schirrmeister et al. 2017

Study:: nm000172 (NeMAR)
Author (year):: Schirrmeister2017
Canonical:: —

Also importable as: NM000172, Schirrmeister2017.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 14; recordings: 28; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000172 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000172

Examples

>>> from eegdash.dataset import NM000172
>>> dataset = NM000172(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000173(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Motor Imagery ataset from Ofner et al 2017

Study:: nm000173 (NeMAR)
Author (year):: Ofner2017
Canonical:: —

Also importable as: NM000173, Ofner2017.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 15; recordings: 300; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000173 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000173

Examples

>>> from eegdash.dataset import NM000173
>>> dataset = NM000173(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000175(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

fNIRS Finger Tapping

Study:: nm000175 (NeMAR)
Author (year):: Luke2024
Canonical:: —

Also importable as: NM000175, Luke2024.

Modality: fnirs. Subjects: 5; recordings: 5; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000175 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000175

Examples

>>> from eegdash.dataset import NM000175
>>> dataset = NM000175(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000176(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BigP3BCI Study K — 9x8 adaptive/checkerboard, 2 sessions (5 healthy subjects)

Study:: nm000176 (NeMAR)
Author (year):: Mainsah2025_BigP3BCI
Canonical:: BigP3BCI_StudyK, BigP3BCI_K

Also importable as: NM000176, Mainsah2025_BigP3BCI, BigP3BCI_StudyK, BigP3BCI_K.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 5; recordings: 128; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000176 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000176

Examples

>>> from eegdash.dataset import NM000176
>>> dataset = NM000176(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BigP3BCI_StudyK', 'BigP3BCI_K']

class eegdash.dataset.dataset.NM000179(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

LEMON: MPI Leipzig Mind-Brain-Body EEG (Resting State)

Study:: nm000179 (NeMAR)
Author (year):: Babayan2018
Canonical:: LEMON

Also importable as: NM000179, Babayan2018, LEMON.

Modality: eeg. Subjects: 215; recordings: 215; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000179 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000179 DOI: https://doi.org/10.1038/sdata.2018.308

Examples

>>> from eegdash.dataset import NM000179
>>> dataset = NM000179(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['LEMON']

class eegdash.dataset.dataset.NM000180(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Brennan2019: EEG during Alice in Wonderland Listening

Study:: nm000180 (NeMAR)
Author (year):: Brennan2019
Canonical:: —

Also importable as: NM000180, Brennan2019.

Modality: eeg. Subjects: 45; recordings: 45; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000180 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000180 DOI: https://doi.org/10.1371/journal.pone.0207741

Examples

>>> from eegdash.dataset import NM000180
>>> dataset = NM000180(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000181(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

NMT: Neurodiagnostic Montage Template Scalp EEG

Study:: nm000181 (NeMAR)
Author (year):: Khan2019
Canonical:: —

Also importable as: NM000181, Khan2019.

Modality: eeg. Subjects: 2417; recordings: 2417; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000181 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000181 DOI: https://doi.org/10.5281/zenodo.10909103

Examples

>>> from eegdash.dataset import NM000181
>>> dataset = NM000181(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000185(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Sleep-EDF Expanded: Whole-Night PSG Recordings

Study:: nm000185 (NeMAR)
Author (year):: Kemp2000
Canonical:: SleepEDF, SleepEDFExpanded

Also importable as: NM000185, Kemp2000, SleepEDF, SleepEDFExpanded.

Modality: eeg. Subjects: 100; recordings: 197; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000185 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000185 DOI: https://doi.org/10.13026/C2X676

Examples

>>> from eegdash.dataset import NM000185
>>> dataset = NM000185(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['SleepEDF', 'SleepEDFExpanded']

class eegdash.dataset.dataset.NM000186(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BigP3BCI Study E — 6x6 checkerboard (8 healthy subjects)

Study:: nm000186 (NeMAR)
Author (year):: Mainsah2025_BigP3BCI_E
Canonical:: BigP3BCI_StudyE, BigP3BCI_E

Also importable as: NM000186, Mainsah2025_BigP3BCI_E, BigP3BCI_StudyE, BigP3BCI_E.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 8; recordings: 88; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000186 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000186

Examples

>>> from eegdash.dataset import NM000186
>>> dataset = NM000186(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BigP3BCI_StudyE', 'BigP3BCI_E']

class eegdash.dataset.dataset.NM000187(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BigP3BCI Study N — 9x8 dry/wet electrode comparison (8 ALS subjects)

Study:: nm000187 (NeMAR)
Author (year):: Mainsah2025_BigP3BCI_N
Canonical:: BigP3BCI_StudyN

Also importable as: NM000187, Mainsah2025_BigP3BCI_N, BigP3BCI_StudyN.

Modality: eeg; Experiment type: Attention; Subject type: Other. Subjects: 8; recordings: 160; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000187 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000187

Examples

>>> from eegdash.dataset import NM000187
>>> dataset = NM000187(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BigP3BCI_StudyN']

class eegdash.dataset.dataset.NM000188(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BNCI 2014-009 P300 dataset

Study:: nm000188 (NeMAR)
Author (year):: Arico2014
Canonical:: BNCI2014_009_P300

Also importable as: NM000188, Arico2014, BNCI2014_009_P300.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 10; recordings: 30; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000188 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000188

Examples

>>> from eegdash.dataset import NM000188
>>> dataset = NM000188(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BNCI2014_009_P300']

class eegdash.dataset.dataset.NM000189(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BNCI 2015-003 P300 dataset

Study:: nm000189 (NeMAR)
Author (year):: Schreuder2015_P300
Canonical:: BNCI2015_P300, BNCI2015_003_P300, BNCI2015_003_AMUSE

Also importable as: NM000189, Schreuder2015_P300, BNCI2015_P300, BNCI2015_003_P300, BNCI2015_003_AMUSE.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 10; recordings: 20; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000189 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000189

Examples

>>> from eegdash.dataset import NM000189
>>> dataset = NM000189(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BNCI2015_P300', 'BNCI2015_003_P300', 'BNCI2015_003_AMUSE']

class eegdash.dataset.dataset.NM000190(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BNCI 2015-012 PASS2D P300 dataset

Study:: nm000190 (NeMAR)
Author (year):: Hohne2015
Canonical:: —

Also importable as: NM000190, Hohne2015.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 10; recordings: 20; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000190 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000190

Examples

>>> from eegdash.dataset import NM000190
>>> dataset = NM000190(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000191(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BigP3BCI Study F — 6x6 multi-paradigm, 3 sessions (10 healthy subjects)

Study:: nm000191 (NeMAR)
Author (year):: Mainsah2025_BigP3BCI_F
Canonical:: BigP3BCI_StudyF, BigP3BCI_F

Also importable as: NM000191, Mainsah2025_BigP3BCI_F, BigP3BCI_StudyF, BigP3BCI_F.

Modality: eeg; Experiment type: Attention; Subject type: Other. Subjects: 10; recordings: 270; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000191 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000191

Examples

>>> from eegdash.dataset import NM000191
>>> dataset = NM000191(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BigP3BCI_StudyF', 'BigP3BCI_F']

class eegdash.dataset.dataset.NM000192(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BNCI 2015-006 Music BCI dataset

Study:: nm000192 (NeMAR)
Author (year):: Treder2015_BNCI_006_Music
Canonical:: BNCI2015_BNCI_006_Music, BNCI_2015_006_Music, BNCI2015_006_MusicBCI

Also importable as: NM000192, Treder2015_BNCI_006_Music, BNCI2015_BNCI_006_Music, BNCI_2015_006_Music, BNCI2015_006_MusicBCI.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 11; recordings: 11; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000192 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000192

Examples

>>> from eegdash.dataset import NM000192
>>> dataset = NM000192(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BNCI2015_BNCI_006_Music', 'BNCI_2015_006_Music', 'BNCI2015_006_MusicBCI']

class eegdash.dataset.dataset.NM000193(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Class for Kojima2024A dataset management. P300 dataset

Study:: nm000193 (NeMAR)
Author (year):: Kojima2024A_P300
Canonical:: —

Also importable as: NM000193, Kojima2024A_P300.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 11; recordings: 66; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000193 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000193

Examples

>>> from eegdash.dataset import NM000193
>>> dataset = NM000193(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000194(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BNCI 2015-010 RSVP P300 dataset

Study:: nm000194 (NeMAR)
Author (year):: Acqualagna2015
Canonical:: —

Also importable as: NM000194, Acqualagna2015.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 12; recordings: 24; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000194 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000194

Examples

>>> from eegdash.dataset import NM000194
>>> dataset = NM000194(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000195(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Mixture of LLP and EM for a visual matrix speller (ERP) dataset from

Study:: nm000195 (NeMAR)
Author (year):: Hubner2018
Canonical:: Huebner2018

Also importable as: NM000195, Hubner2018, Huebner2018.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 12; recordings: 360; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000195 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000195

Examples

>>> from eegdash.dataset import NM000195
>>> dataset = NM000195(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Huebner2018']

class eegdash.dataset.dataset.NM000196(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

c-VEP dataset from Thielen et al. (2015)

Study:: nm000196 (NeMAR)
Author (year):: Thielen2015
Canonical:: —

Also importable as: NM000196, Thielen2015.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 12; recordings: 36; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000196 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000196

Examples

>>> from eegdash.dataset import NM000196
>>> dataset = NM000196(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000197(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BigP3BCI Study M — 9x8 adaptive/checkerboard (21 ALS subjects)

Study:: nm000197 (NeMAR)
Author (year):: Mainsah2025_BigP3BCI_M
Canonical:: BigP3BCI_StudyM, BigP3BCI_M

Also importable as: NM000197, Mainsah2025_BigP3BCI_M, BigP3BCI_StudyM, BigP3BCI_M.

Modality: eeg; Experiment type: Attention; Subject type: Other. Subjects: 21; recordings: 420; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000197 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000197

Examples

>>> from eegdash.dataset import NM000197
>>> dataset = NM000197(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BigP3BCI_StudyM', 'BigP3BCI_M']

class eegdash.dataset.dataset.NM000198(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BNCI 2015-008 Center Speller P300 dataset

Study:: nm000198 (NeMAR)
Author (year):: Treder2015_P300
Canonical:: BNCI2015_008_P300, BNCI2015_008_CenterSpeller

Also importable as: NM000198, Treder2015_P300, BNCI2015_008_P300, BNCI2015_008_CenterSpeller.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 13; recordings: 26; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000198 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000198

Examples

>>> from eegdash.dataset import NM000198
>>> dataset = NM000198(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BNCI2015_008_P300', 'BNCI2015_008_CenterSpeller']

class eegdash.dataset.dataset.NM000199(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Learning from label proportions for a visual matrix speller (ERP)

Study:: nm000199 (NeMAR)
Author (year):: Hubner2017
Canonical:: Huebner2017

Also importable as: NM000199, Hubner2017, Huebner2017.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 13; recordings: 342; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000199 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000199

Examples

>>> from eegdash.dataset import NM000199
>>> dataset = NM000199(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Huebner2017']

class eegdash.dataset.dataset.NM000200(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BigP3BCI Study I — 9x8 checkerboard/performance-based (13 healthy subjects)

Study:: nm000200 (NeMAR)
Author (year):: Mainsah2025_BigP3BCI_I
Canonical:: BigP3BCI_StudyI, BigP3BCI_I

Also importable as: NM000200, Mainsah2025_BigP3BCI_I, BigP3BCI_StudyI, BigP3BCI_I.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 13; recordings: 265; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000200 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000200

Examples

>>> from eegdash.dataset import NM000200
>>> dataset = NM000200(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BigP3BCI_StudyI', 'BigP3BCI_I']

class eegdash.dataset.dataset.NM000201(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

ERP paradigm of the Mobile BCI dataset

Study:: nm000201 (NeMAR)
Author (year):: Lee2021_ERP
Canonical:: —

Also importable as: NM000201, Lee2021_ERP.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 24; recordings: 113; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000201 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000201

Examples

>>> from eegdash.dataset import NM000201
>>> dataset = NM000201(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000204(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Bluetooth speaker experiment (14 subjects, 6 classes, 31 EEG ch)

Study:: nm000204 (NeMAR)
Author (year):: Lee2024_Bluetooth_speaker_14
Canonical:: —

Also importable as: NM000204, Lee2024_Bluetooth_speaker_14.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 14; recordings: 420; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000204 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000204

Examples

>>> from eegdash.dataset import NM000204
>>> dataset = NM000204(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000205(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

RSVP collaborative BCI dataset from Zheng et al 2020

Study:: nm000205 (NeMAR)
Author (year):: Zheng2020
Canonical:: —

Also importable as: NM000205, Zheng2020.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 14; recordings: 84; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000205 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000205

Examples

>>> from eegdash.dataset import NM000205
>>> dataset = NM000205(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000206(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Neuroergonomic 2021 dataset

Study:: nm000206 (NeMAR)
Author (year):: Hinss2021_Neuroergonomic
Canonical:: Hinss2021

Also importable as: NM000206, Hinss2021_Neuroergonomic, Hinss2021.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 15; recordings: 30; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000206 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000206

Examples

>>> from eegdash.dataset import NM000206
>>> dataset = NM000206(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Hinss2021']

class eegdash.dataset.dataset.NM000207(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Class for Kojima2024B dataset management. P300 dataset

Study:: nm000207 (NeMAR)
Author (year):: Kojima2024B_P300
Canonical:: —

Also importable as: NM000207, Kojima2024B_P300.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 15; recordings: 180; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000207 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000207

Examples

>>> from eegdash.dataset import NM000207
>>> dataset = NM000207(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000208(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Door lock control experiment (15 subjects, 4 classes, 31 EEG ch)

Study:: nm000208 (NeMAR)
Author (year):: Lee2024_Door_lock_control
Canonical:: —

Also importable as: NM000208, Lee2024_Door_lock_control.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 14; recordings: 434; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000208 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000208

Examples

>>> from eegdash.dataset import NM000208
>>> dataset = NM000208(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000209(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Motor imagery + spatial attention dataset from Forenzo & He 2023

Study:: nm000209 (NeMAR)
Author (year):: Forenzo2023
Canonical:: —

Also importable as: NM000209, Forenzo2023.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 25; recordings: 150; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000209 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000209

Examples

>>> from eegdash.dataset import NM000209
>>> dataset = NM000209(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000210(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BCIAUT-P300 dataset for autism from Simoes et al 2020

Study:: nm000210 (NeMAR)
Author (year):: Simoes2020
Canonical:: BCIAUTP300, BCIAUT_P300, BCIAUT

Also importable as: NM000210, Simoes2020, BCIAUTP300, BCIAUT_P300, BCIAUT.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 15; recordings: 210; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000210 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000210

Examples

>>> from eegdash.dataset import NM000210
>>> dataset = NM000210(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BCIAUTP300', 'BCIAUT_P300', 'BCIAUT']

class eegdash.dataset.dataset.NM000211(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

RSVP ERP dataset for authentication from Zhang et al 2025

Study:: nm000211 (NeMAR)
Author (year):: Zhang2025_RSVP
Canonical:: Zhang2025

Also importable as: NM000211, Zhang2025_RSVP, Zhang2025.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 15; recordings: 240; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000211 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000211

Examples

>>> from eegdash.dataset import NM000211
>>> dataset = NM000211(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Zhang2025']

class eegdash.dataset.dataset.NM000212(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BNCI 2015-007 Motion VEP (mVEP) Speller dataset

Study:: nm000212 (NeMAR)
Author (year):: Schaeff2015
Canonical:: —

Also importable as: NM000212, Schaeff2015.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 16; recordings: 32; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000212 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000212

Examples

>>> from eegdash.dataset import NM000212
>>> dataset = NM000212(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000213(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Television control experiment (30 subjects, 4 classes, 31 EEG ch)

Study:: nm000213 (NeMAR)
Author (year):: Lee2024_Television_control_30
Canonical:: —

Also importable as: NM000213, Lee2024_Television_control_30.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 30; recordings: 2300; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000213 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000213

Examples

>>> from eegdash.dataset import NM000213
>>> dataset = NM000213(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000214(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

c-VEP dataset from Thielen et al. (2021)

Study:: nm000214 (NeMAR)
Author (year):: Thielen2021
Canonical:: —

Also importable as: NM000214, Thielen2021.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 30; recordings: 150; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000214 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000214

Examples

>>> from eegdash.dataset import NM000214
>>> dataset = NM000214(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000215(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

P300 dataset BI2014b from a “Brain Invaders” experiment

Study:: nm000215 (NeMAR)
Author (year):: Korczowski2014_P300
Canonical:: BrainInvaders2014b, BI2014b, BrainInvadersBI2014b

Also importable as: NM000215, Korczowski2014_P300, BrainInvaders2014b, BI2014b, BrainInvadersBI2014b.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 38; recordings: 38; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000215 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000215

Examples

>>> from eegdash.dataset import NM000215
>>> dataset = NM000215(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BrainInvaders2014b', 'BI2014b', 'BrainInvadersBI2014b']

class eegdash.dataset.dataset.NM000216(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

P300 dataset BI2015a from a “Brain Invaders” experiment

Study:: nm000216 (NeMAR)
Author (year):: Korczowski2015_P300
Canonical:: BrainInvaders2015a, BI2015a

Also importable as: NM000216, Korczowski2015_P300, BrainInvaders2015a, BI2015a.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 43; recordings: 129; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000216 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000216

Examples

>>> from eegdash.dataset import NM000216
>>> dataset = NM000216(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BrainInvaders2015a', 'BI2015a']

class eegdash.dataset.dataset.NM000217(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

P300 dataset BI2015b from a “Brain Invaders” experiment

Study:: nm000217 (NeMAR)
Author (year):: Korczowski2015_P300_BI2015b
Canonical:: BrainInvaders2015b, BI2015b

Also importable as: NM000217, Korczowski2015_P300_BI2015b, BrainInvaders2015b, BI2015b.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 44; recordings: 176; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000217 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000217

Examples

>>> from eegdash.dataset import NM000217
>>> dataset = NM000217(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BrainInvaders2015b', 'BI2015b']

class eegdash.dataset.dataset.NM000218(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BigP3BCI Study H — 9x8 checkerboard with gaze conditions (16 healthy subjects)

Study:: nm000218 (NeMAR)
Author (year):: Mainsah2025_BigP3BCI_H
Canonical:: BigP3BCI_StudyH, BigP3BCI_H

Also importable as: NM000218, Mainsah2025_BigP3BCI_H, BigP3BCI_StudyH, BigP3BCI_H.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 16; recordings: 372; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000218 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000218

Examples

>>> from eegdash.dataset import NM000218
>>> dataset = NM000218(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BigP3BCI_StudyH', 'BigP3BCI_H']

class eegdash.dataset.dataset.NM000219(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BNCI 2020-002 Attention Shift (Covert Spatial Attention) dataset

Study:: nm000219 (NeMAR)
Author (year):: Reichert2020
Canonical:: BNCI2020, BNCI2020_002_AttentionShift, BNCI2020_002_CovertSpatialAttention

Also importable as: NM000219, Reichert2020, BNCI2020, BNCI2020_002_AttentionShift, BNCI2020_002_CovertSpatialAttention.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 18; recordings: 18; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000219 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000219

Examples

>>> from eegdash.dataset import NM000219
>>> dataset = NM000219(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BNCI2020', 'BNCI2020_002_AttentionShift', 'BNCI2020_002_CovertSpatialAttention']

class eegdash.dataset.dataset.NM000221(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Alphawaves dataset

Study:: nm000221 (NeMAR)
Author (year):: Cattan2017
Canonical:: Alphawaves, Rodrigues2017, AlphaWaves

Also importable as: NM000221, Cattan2017, Alphawaves, Rodrigues2017, AlphaWaves.

Modality: eeg; Experiment type: Resting-state; Subject type: Healthy. Subjects: 19; recordings: 19; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000221 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000221

Examples

>>> from eegdash.dataset import NM000221
>>> dataset = NM000221(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Alphawaves', 'Rodrigues2017', 'AlphaWaves']

class eegdash.dataset.dataset.NM000222(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Air conditioner control experiment (10 subjects, 4 classes, 25 EEG ch)

Study:: nm000222 (NeMAR)
Author (year):: Lee2024_Air_conditioner_control
Canonical:: —

Also importable as: NM000222, Lee2024_Air_conditioner_control.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 10; recordings: 305; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000222 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000222

Examples

>>> from eegdash.dataset import NM000222
>>> dataset = NM000222(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000223(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Electric light control experiment (15 subjects, 4 classes, 31 EEG ch)

Study:: nm000223 (NeMAR)
Author (year):: Lee2024_Electric_light_control
Canonical:: —

Also importable as: NM000223, Lee2024_Electric_light_control.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 15; recordings: 465; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000223 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000223

Examples

>>> from eegdash.dataset import NM000223
>>> dataset = NM000223(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000225(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

PhysioNet 2018 Challenge: Sleep Arousal Detection PSG (Training)

Study:: nm000225 (NeMAR)
Author (year):: Ghassemi2018
Canonical:: —

Also importable as: NM000225, Ghassemi2018.

Modality: eeg. Subjects: 1983; recordings: 1983; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000225 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000225 DOI: https://doi.org/10.13026/6phb-r450

Examples

>>> from eegdash.dataset import NM000225
>>> dataset = NM000225(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000226(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Zhou2016

Study:: nm000226 (NeMAR)
Author (year):: Zhou2016_226
Canonical:: Zhou2016_NEMAR

Also importable as: NM000226, Zhou2016_226, Zhou2016_NEMAR.

Modality: eeg. Subjects: 4; recordings: 24; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000226 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000226 DOI: https://doi.org/10.82901/nemar.nm000115

Examples

>>> from eegdash.dataset import NM000226
>>> dataset = NM000226(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Zhou2016_NEMAR']

class eegdash.dataset.dataset.NM000227(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Eye-BCI Motor Execution dataset from Guttmann-Flury et al 2025

Study:: nm000227 (NeMAR)
Author (year):: GuttmannFlury2025_Eye
Canonical:: GuttmannFlury2025_ME

Also importable as: NM000227, GuttmannFlury2025_Eye, GuttmannFlury2025_ME.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 31; recordings: 63; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000227 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000227

Examples

>>> from eegdash.dataset import NM000227
>>> dataset = NM000227(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['GuttmannFlury2025_ME']

class eegdash.dataset.dataset.NM000228(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Nieuwland et al. 2018: Multi-site N400 Replication Study

Study:: nm000228 (NeMAR)
Author (year):: Nieuwland2018
Canonical:: —

Also importable as: NM000228, Nieuwland2018.

Modality: eeg. Subjects: 356; recordings: 397; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000228 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000228 DOI: https://doi.org/10.7554/eLife.33468

Examples

>>> from eegdash.dataset import NM000228
>>> dataset = NM000228(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000229(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Gwilliams et al. 2023 — Introducing MEG-MASC: a high-quality magneto-encephalography dataset for evaluating natural speech processing

Study:: nm000229 (NeMAR)
Author (year):: Gwilliams2023
Canonical:: MASC_MEG, MEG_MASC

Also importable as: NM000229, Gwilliams2023, MASC_MEG, MEG_MASC.

Modality: eeg. Subjects: 29; recordings: 1360; tasks: 79.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000229 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000229 DOI: https://doi.org/10.1038/s41597-023-02752-5

Examples

>>> from eegdash.dataset import NM000229
>>> dataset = NM000229(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['MASC_MEG', 'MEG_MASC']

class eegdash.dataset.dataset.NM000230(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Lower-limb MI dataset for knee pain patients from Zuo et al. 2025

Study:: nm000230 (NeMAR)
Author (year):: Zuo2025
Canonical:: —

Also importable as: NM000230, Zuo2025.

Modality: eeg; Experiment type: Motor; Subject type: Other. Subjects: 30; recordings: 118; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000230 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000230

Examples

>>> from eegdash.dataset import NM000230
>>> dataset = NM000230(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000231(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

P300 dataset from Hoffmann et al 2008

Study:: nm000231 (NeMAR)
Author (year):: Hoffmann2008
Canonical:: EPFLP300, EPFL_P300, EPFLP300Dataset

Also importable as: NM000231, Hoffmann2008, EPFLP300, EPFL_P300, EPFLP300Dataset.

Modality: eeg; Experiment type: Attention; Subject type: Other. Subjects: 8; recordings: 192; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000231 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000231

Examples

>>> from eegdash.dataset import NM000231
>>> dataset = NM000231(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['EPFLP300', 'EPFL_P300', 'EPFLP300Dataset']

class eegdash.dataset.dataset.NM000232(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

THINGS-EEG2: A large and rich EEG dataset for modeling human visual object recognition

Study:: nm000232 (NeMAR)
Author (year):: Gifford2019
Canonical:: —

Also importable as: NM000232, Gifford2019.

Modality: eeg. Subjects: 10; recordings: 638; tasks: 5.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000232 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000232 DOI: https://doi.org/10.17605/OSF.IO/3JK45

Examples

>>> from eegdash.dataset import NM000232
>>> dataset = NM000232(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000234(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BNCI 2015-009 AMUSE (Auditory Multi-class Spatial ERP) dataset

Study:: nm000234 (NeMAR)
Author (year):: Schreuder2015_ERP
Canonical:: BNCI2015_ERP

Also importable as: NM000234, Schreuder2015_ERP, BNCI2015_ERP.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 21; recordings: 42; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000234 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000234

Examples

>>> from eegdash.dataset import NM000234
>>> dataset = NM000234(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BNCI2015_ERP']

class eegdash.dataset.dataset.NM000235(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Eye-BCI multimodal MI/ME dataset from Guttmann-Flury et al 2025

Study:: nm000235 (NeMAR)
Author (year):: GuttmannFlury2025_Eye_BCI
Canonical:: GuttmannFlury2025_MIME

Also importable as: NM000235, GuttmannFlury2025_Eye_BCI, GuttmannFlury2025_MIME.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 31; recordings: 63; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000235 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000235

Examples

>>> from eegdash.dataset import NM000235
>>> dataset = NM000235(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['GuttmannFlury2025_MIME']

class eegdash.dataset.dataset.NM000236(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Dataset of an EEG-based BCI experiment in Virtual Reality using P300

Study:: nm000236 (NeMAR)
Author (year):: Cattan2019_P300
Canonical:: —

Also importable as: NM000236, Cattan2019_P300.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 21; recordings: 2520; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000236 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000236

Examples

>>> from eegdash.dataset import NM000236
>>> dataset = NM000236(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000237(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

7-day motor imagery BCI EEG dataset from Zhou et al 2021

Study:: nm000237 (NeMAR)
Author (year):: Zhou2021
Canonical:: —

Also importable as: NM000237, Zhou2021.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 20; recordings: 833; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000237 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000237

Examples

>>> from eegdash.dataset import NM000237
>>> dataset = NM000237(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000238(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

SparrKULee: A Speech-Evoked Auditory Response Repository from KU Leuven, Containing the EEG of 85 Participants

Study:: nm000238 (NeMAR)
Author (year):: Accou2024
Canonical:: —

Also importable as: NM000238, Accou2024.

Modality: eeg. Subjects: 87; recordings: 4088; tasks: 366.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000238 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000238 DOI: https://doi.org/10.48804/K3VSND

Examples

>>> from eegdash.dataset import NM000238
>>> dataset = NM000238(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000239(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

P-ary m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2023)

Study:: nm000239 (NeMAR)
Author (year):: MartinezCagigal2023
Canonical:: —

Also importable as: NM000239, MartinezCagigal2023.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 16; recordings: 640; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000239 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000239

Examples

>>> from eegdash.dataset import NM000239
>>> dataset = NM000239(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000240(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Checkerboard m-sequence-based c-VEP dataset from

Study:: nm000240 (NeMAR)
Author (year):: FernandezRodriguez2025
Canonical:: FernandezRodriguez2023

Also importable as: NM000240, FernandezRodriguez2025, FernandezRodriguez2023.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 16; recordings: 383; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000240 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000240

Examples

>>> from eegdash.dataset import NM000240
>>> dataset = NM000240(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['FernandezRodriguez2023']

class eegdash.dataset.dataset.NM000241(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

CerebroVoice: Bilingual sEEG Speech Dataset

Study:: nm000241 (NeMAR)
Author (year):: Zhang2019
Canonical:: —

Also importable as: NM000241, Zhang2019.

Modality: ieeg. Subjects: 2; recordings: 18; tasks: 9.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000241 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000241 DOI: https://doi.org/10.5281/zenodo.13332808

Examples

>>> from eegdash.dataset import NM000241
>>> dataset = NM000241(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000242(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Visual imagery EEG dataset from Gao et al 2026

Study:: nm000242 (NeMAR)
Author (year):: Gao2026_Visual_imagery_et
Canonical:: Gao2026

Also importable as: NM000242, Gao2026_Visual_imagery_et, Gao2026.

Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 22; recordings: 125; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000242 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000242

Examples

>>> from eegdash.dataset import NM000242
>>> dataset = NM000242(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Gao2026']

class eegdash.dataset.dataset.NM000243(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BNCI 2016-002 Emergency Braking during Simulated Driving dataset

Study:: nm000243 (NeMAR)
Author (year):: Haufe2016
Canonical:: BNCI2016, BNCI2016002

Also importable as: NM000243, Haufe2016, BNCI2016, BNCI2016002.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 15; recordings: 15; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000243 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000243

Examples

>>> from eegdash.dataset import NM000243
>>> dataset = NM000243(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BNCI2016', 'BNCI2016002']

class eegdash.dataset.dataset.NM000244(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

P300 dataset BI2014a from a “Brain Invaders” experiment

Study:: nm000244 (NeMAR)
Author (year):: Korczowski2014_P300_BI2014a
Canonical:: BrainInvaders2014a, BI2014a

Also importable as: NM000244, Korczowski2014_P300_BI2014a, BrainInvaders2014a, BI2014a.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 64; recordings: 64; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000244 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000244

Examples

>>> from eegdash.dataset import NM000244
>>> dataset = NM000244(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BrainInvaders2014a', 'BI2014a']

class eegdash.dataset.dataset.NM000245(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Motor Imagery dataset from Cho et al 2017

Study:: nm000245 (NeMAR)
Author (year):: Cho2017
Canonical:: —

Also importable as: NM000245, Cho2017.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 52; recordings: 52; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000245 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000245

Examples

>>> from eegdash.dataset import NM000245
>>> dataset = NM000245(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000246(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Multi-day MI-BCI dataset (WBCIC-SHU) from Yang et al 2025

Study:: nm000246 (NeMAR)
Author (year):: Yang2025_Multi
Canonical:: WBCIC_SHU, WBCICSHU

Also importable as: NM000246, Yang2025_Multi, WBCIC_SHU, WBCICSHU.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 51; recordings: 153; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000246 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000246

Examples

>>> from eegdash.dataset import NM000246
>>> dataset = NM000246(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['WBCIC_SHU', 'WBCICSHU']

class eegdash.dataset.dataset.NM000247(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BigP3BCI Study S1 — 9x8 face/house paradigm (10 healthy subjects)

Study:: nm000247 (NeMAR)
Author (year):: Mainsah2025_BigP3BCI_S1
Canonical:: BigP3BCI_StudyS1, BigP3BCI_S1

Also importable as: NM000247, Mainsah2025_BigP3BCI_S1, BigP3BCI_StudyS1, BigP3BCI_S1.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 10; recordings: 120; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000247 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000247

Examples

>>> from eegdash.dataset import NM000247
>>> dataset = NM000247(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BigP3BCI_StudyS1', 'BigP3BCI_S1']

class eegdash.dataset.dataset.NM000248(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BigP3BCI Study L — 6x6 multi-paradigm (11 ALS subjects)

Study:: nm000248 (NeMAR)
Author (year):: Mainsah2025_BigP3BCI_L
Canonical:: —

Also importable as: NM000248, Mainsah2025_BigP3BCI_L.

Modality: eeg; Experiment type: Attention; Subject type: Other. Subjects: 11; recordings: 330; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000248 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000248

Examples

>>> from eegdash.dataset import NM000248
>>> dataset = NM000248(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000249(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BNCI 2022-001 EEG Correlates of Difficulty Level dataset

Study:: nm000249 (NeMAR)
Author (year):: Jao2022
Canonical:: Jao2020

Also importable as: NM000249, Jao2022, Jao2020.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 13; recordings: 13; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000249 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000249

Examples

>>> from eegdash.dataset import NM000249
>>> dataset = NM000249(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Jao2020']

class eegdash.dataset.dataset.NM000250(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Class for Dreyer2023 dataset management. MI dataset

Study:: nm000250 (NeMAR)
Author (year):: Dreyer2023
Canonical:: —

Also importable as: NM000250, Dreyer2023.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 87; recordings: 520; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000250 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000250

Examples

>>> from eegdash.dataset import NM000250
>>> dataset = NM000250(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000251(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

He et al. 2025 — VocalMind: A Stereotactic EEG Dataset for Vocalized, Mimed, and Imagined Speech in Tonal Language

Study:: nm000251 (NeMAR)
Author (year):: He2025
Canonical:: —

Also importable as: NM000251, He2025.

Modality: ieeg. Subjects: 1; recordings: 6; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000251 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000251 DOI: https://doi.org/10.1038/s41597-025-04741-2

Examples

>>> from eegdash.dataset import NM000251
>>> dataset = NM000251(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000253(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Wang et al. 2024 — Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli

Study:: nm000253 (NeMAR)
Author (year):: Wang2024_et_al_Brain
Canonical:: BrainTreeBank

Also importable as: NM000253, Wang2024_et_al_Brain, BrainTreeBank.

Modality: ieeg. Subjects: 10; recordings: 26; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000253 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000253 DOI: https://doi.org/10.48550/arXiv.2411.08343

Examples

>>> from eegdash.dataset import NM000253
>>> dataset = NM000253(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BrainTreeBank']

class eegdash.dataset.dataset.NM000254(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Naturalistic viewing: An open-access dataset using simultaneous EEG-fMRI

Study:: nm000254 (NeMAR)
Author (year):: Telesford2024
Canonical:: —

Also importable as: NM000254, Telesford2024.

Modality: eeg. Subjects: 22; recordings: 942; tasks: 12.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000254 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000254

Examples

>>> from eegdash.dataset import NM000254
>>> dataset = NM000254(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000255(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The Brain, Body, and Behaviour Dataset (1.0.0) - Experiment 2

Study:: nm000255 (NeMAR)
Author (year):: Madsen2024_E2
Canonical:: —

Also importable as: NM000255, Madsen2024_E2.

Modality: eeg. Subjects: 30; recordings: 291; tasks: 5.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000255 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000255

Examples

>>> from eegdash.dataset import NM000255
>>> dataset = NM000255(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000256(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The Brain, Body, and Behaviour Dataset (1.0.0) - Experiment 3

Study:: nm000256 (NeMAR)
Author (year):: Madsen2024_E3
Canonical:: —

Also importable as: NM000256, Madsen2024_E3.

Modality: eeg. Subjects: 29; recordings: 332; tasks: 6.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000256 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000256

Examples

>>> from eegdash.dataset import NM000256
>>> dataset = NM000256(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000259(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Speier2017

Study:: nm000259 (NeMAR)
Author (year):: Speier2017
Canonical:: —

Also importable as: NM000259, Speier2017.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 10; recordings: 60; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000259 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000259 DOI: https://doi.org/10.1371/journal.pone.0175382

Examples

>>> from eegdash.dataset import NM000259
>>> dataset = NM000259(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000260(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BrainInvaders2012

Study:: nm000260 (NeMAR)
Author (year):: BrainInvaders2012
Canonical:: BI2012, BrainInvaders

Also importable as: NM000260, BrainInvaders2012, BI2012, BrainInvaders.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 23; recordings: 46; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000260 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000260 DOI: https://doi.org/10.5281/zenodo.2649006

Examples

>>> from eegdash.dataset import NM000260
>>> dataset = NM000260(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BI2012', 'BrainInvaders']

class eegdash.dataset.dataset.NM000264(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

BrainInvaders2013a

Study:: nm000264 (NeMAR)
Author (year):: BrainInvaders2013
Canonical:: BrainInvaders2013a, BI2013a

Also importable as: NM000264, BrainInvaders2013, BrainInvaders2013a, BI2013a.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 24; recordings: 292; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000264 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000264 DOI: https://doi.org/10.5281/zenodo.1494163

Examples

>>> from eegdash.dataset import NM000264
>>> dataset = NM000264(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BrainInvaders2013a', 'BI2013a']

class eegdash.dataset.dataset.NM000265(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

GuttmannFlury2025-MI

Study:: nm000265 (NeMAR)
Author (year):: GuttmannFlury2025_MI
Canonical:: —

Also importable as: NM000265, GuttmannFlury2025_MI.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 31; recordings: 126; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000265 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000265 DOI: https://doi.org/10.1038/s41597-025-04861-9

Examples

>>> from eegdash.dataset import NM000265
>>> dataset = NM000265(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000266(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Sosulski2019

Study:: nm000266 (NeMAR)
Author (year):: Sosulski2019
Canonical:: —

Also importable as: NM000266, Sosulski2019.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 13; recordings: 1060; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000266 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000266 DOI: https://doi.org/10.48550/arXiv.2109.06011

Examples

>>> from eegdash.dataset import NM000266
>>> dataset = NM000266(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000267(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Shin2017A

Study:: nm000267 (NeMAR)
Author (year):: Shin2017_Shin2017A
Canonical:: Shin2017A

Also importable as: NM000267, Shin2017_Shin2017A, Shin2017A.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 29; recordings: 174; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000267 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000267 DOI: https://doi.org/10.1109/TNSRE.2016.2628057

Examples

>>> from eegdash.dataset import NM000267
>>> dataset = NM000267(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Shin2017A']

class eegdash.dataset.dataset.NM000268(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Shin2017B

Study:: nm000268 (NeMAR)
Author (year):: Shin2017_Shin2017B
Canonical:: Shin2017B

Also importable as: NM000268, Shin2017_Shin2017B, Shin2017B.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 29; recordings: 174; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000268 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000268 DOI: https://doi.org/10.1109/TNSRE.2016.2628057

Examples

>>> from eegdash.dataset import NM000268
>>> dataset = NM000268(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Shin2017B']

class eegdash.dataset.dataset.NM000270(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

liu2025 - NEMAR Dataset

Study:: nm000270 (NeMAR)
Author (year):: Liu2025
Canonical:: —

Also importable as: NM000270, Liu2025.

Modality: eeg; Experiment type: Motor; Subject type: Unknown. Subjects: 27; recordings: 797; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000270 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000270

Examples

>>> from eegdash.dataset import NM000270
>>> dataset = NM000270(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000271(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

chang2025 - NEMAR Dataset

Study:: nm000271 (NeMAR)
Author (year):: Chang2025_2
Canonical:: Chang2025

Also importable as: NM000271, Chang2025_2, Chang2025.

Modality: eeg; Experiment type: Motor; Subject type: Unknown. Subjects: 28; recordings: 1245; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000271 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000271

Examples

>>> from eegdash.dataset import NM000271
>>> dataset = NM000271(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Chang2025']

class eegdash.dataset.dataset.NM000272(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

romani-bf2025-erp - NEMAR Dataset

Study:: nm000272 (NeMAR)
Author (year):: Romani2025_BF_ERP
Canonical:: Romani2025_erp

Also importable as: NM000272, Romani2025_BF_ERP, Romani2025_erp.

Modality: eeg; Experiment type: Attention; Subject type: Unknown. Subjects: 22; recordings: 1022; tasks: 3.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000272 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000272

Examples

>>> from eegdash.dataset import NM000272
>>> dataset = NM000272(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Romani2025_erp']

class eegdash.dataset.dataset.NM000277(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Mainsah2025-G

Study:: nm000277 (NeMAR)
Author (year):: Mainsah2025_G
Canonical:: BigP3BCI_G, BigP3BCI_StudyG

Also importable as: NM000277, Mainsah2025_G, BigP3BCI_G, BigP3BCI_StudyG.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 20; recordings: 320; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000277 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000277 DOI: https://doi.org/10.13026/0byy-ry86

Examples

>>> from eegdash.dataset import NM000277
>>> dataset = NM000277(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['BigP3BCI_G', 'BigP3BCI_StudyG']

class eegdash.dataset.dataset.NM000301(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Mainsah2025-D

Study:: nm000301 (NeMAR)
Author (year):: Mainsah2025_D
Canonical:: —

Also importable as: NM000301, Mainsah2025_D.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 17; recordings: 307; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000301 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000301 DOI: https://doi.org/10.13026/0byy-ry86

Examples

>>> from eegdash.dataset import NM000301
>>> dataset = NM000301(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000303(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Mainsah2025-O

Study:: nm000303 (NeMAR)
Author (year):: Mainsah2025_O
Canonical:: —

Also importable as: NM000303, Mainsah2025_O.

Modality: eeg; Experiment type: Perception; Subject type: Other. Subjects: 18; recordings: 347; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000303 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000303 DOI: https://doi.org/10.13026/0byy-ry86

Examples

>>> from eegdash.dataset import NM000303
>>> dataset = NM000303(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000310(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

GuttmannFlury2025-SSVEP

Study:: nm000310 (NeMAR)
Author (year):: GuttmannFlury2025_SSVEP
Canonical:: —

Also importable as: NM000310, GuttmannFlury2025_SSVEP.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 11; recordings: 26; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000310 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000310 DOI: https://doi.org/10.1038/s41597-025-04861-9

Examples

>>> from eegdash.dataset import NM000310
>>> dataset = NM000310(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000311(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Multimodal upper-limb MI/ME EEG (Jeong et al. 2020)

Study:: nm000311 (NeMAR)
Author (year):: Jeong2020
Canonical:: —

Also importable as: NM000311, Jeong2020.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 25; recordings: 213; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000311 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000311 DOI: https://doi.org/10.82901/nemar.nm000311

Examples

>>> from eegdash.dataset import NM000311
>>> dataset = NM000311(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000313(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Mainsah2025-S2

Study:: nm000313 (NeMAR)
Author (year):: Mainsah2025_S2
Canonical:: —

Also importable as: NM000313, Mainsah2025_S2.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 24; recordings: 288; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000313 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000313 DOI: https://doi.org/10.13026/0byy-ry86

Examples

>>> from eegdash.dataset import NM000313
>>> dataset = NM000313(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000321(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Mainsah2025-Q

Study:: nm000321 (NeMAR)
Author (year):: Mainsah2025_Q
Canonical:: —

Also importable as: NM000321, Mainsah2025_Q.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Other. Subjects: 36; recordings: 360; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000321 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000321 DOI: https://doi.org/10.13026/0byy-ry86

Examples

>>> from eegdash.dataset import NM000321
>>> dataset = NM000321(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000323(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Lee2019-ERP

Study:: nm000323 (NeMAR)
Author (year):: Lee2019_ERP
Canonical:: OpenBMI_ERP, OpenBMI_P300

Also importable as: NM000323, Lee2019_ERP, OpenBMI_ERP, OpenBMI_P300.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 54; recordings: 216; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000323 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000323 DOI: https://doi.org/10.1093/gigascience/giz002

Examples

>>> from eegdash.dataset import NM000323
>>> dataset = NM000323(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['OpenBMI_ERP', 'OpenBMI_P300']

class eegdash.dataset.dataset.NM000326(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Mainsah2025-C

Study:: nm000326 (NeMAR)
Author (year):: Mainsah2025_C
Canonical:: —

Also importable as: NM000326, Mainsah2025_C.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 19; recordings: 341; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000326 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000326 DOI: https://doi.org/10.13026/0byy-ry86

Examples

>>> from eegdash.dataset import NM000326
>>> dataset = NM000326(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000329(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Brandl2020

Study:: nm000329 (NeMAR)
Author (year):: Brandl2020
Canonical:: —

Also importable as: NM000329, Brandl2020.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 16; recordings: 112; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000329 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000329 DOI: https://doi.org/10.3389/fnins.2020.566147

Examples

>>> from eegdash.dataset import NM000329
>>> dataset = NM000329(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000336(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Mainsah2025-R

Study:: nm000336 (NeMAR)
Author (year):: Mainsah2025_R
Canonical:: —

Also importable as: NM000336, Mainsah2025_R.

Modality: eeg; Experiment type: Attention; Subject type: Other. Subjects: 20; recordings: 480; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000336 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000336 DOI: https://doi.org/10.13026/0byy-ry86

Examples

>>> from eegdash.dataset import NM000336
>>> dataset = NM000336(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000338(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Lee2019-MI

Study:: nm000338 (NeMAR)
Author (year):: Lee2019_MI
Canonical:: OpenBMI_MI

Also importable as: NM000338, Lee2019_MI, OpenBMI_MI.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 54; recordings: 216; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000338 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000338 DOI: https://doi.org/10.1093/gigascience/giz002

Examples

>>> from eegdash.dataset import NM000338
>>> dataset = NM000338(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['OpenBMI_MI']

class eegdash.dataset.dataset.NM000339(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Stieger2021

Study:: nm000339 (NeMAR)
Author (year):: Stieger2021
Canonical:: —

Also importable as: NM000339, Stieger2021.

Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 62; recordings: 598; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000339 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000339 DOI: https://doi.org/10.1038/s41597-021-00883-1

Examples

>>> from eegdash.dataset import NM000339
>>> dataset = NM000339(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000340(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Mainsah2025-J

Study:: nm000340 (NeMAR)
Author (year):: Mainsah2025_J
Canonical:: —

Also importable as: NM000340, Mainsah2025_J.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 20; recordings: 502; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000340 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000340 DOI: https://doi.org/10.13026/0byy-ry86

Examples

>>> from eegdash.dataset import NM000340
>>> dataset = NM000340(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000341(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Cattan2019-PHMD

Study:: nm000341 (NeMAR)
Author (year):: Cattan2019_PHMD
Canonical:: —

Also importable as: NM000341, Cattan2019_PHMD.

Modality: eeg; Experiment type: Resting-state; Subject type: Healthy. Subjects: 12; recordings: 12; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000341 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000341 DOI: https://doi.org/10.5281/zenodo.2617084

Examples

>>> from eegdash.dataset import NM000341
>>> dataset = NM000341(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000342(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

CastillosCVEP40

Study:: nm000342 (NeMAR)
Author (year):: Castillos2023_CastillosCVEP40
Canonical:: CastillosCVEP40

Also importable as: NM000342, Castillos2023_CastillosCVEP40, CastillosCVEP40.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 12; recordings: 12; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000342 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000342 DOI: https://doi.org/10.1016/j.neuroimage.2023.120446

Examples

>>> from eegdash.dataset import NM000342
>>> dataset = NM000342(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['CastillosCVEP40']

class eegdash.dataset.dataset.NM000343(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Hinss2021

Study:: nm000343 (NeMAR)
Author (year):: Hinss2021
Canonical:: Hinss2021_v2

Also importable as: NM000343, Hinss2021, Hinss2021_v2.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 15; recordings: 30; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000343 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000343 DOI: https://doi.org/10.1038/s41597-022-01898-y

Examples

>>> from eegdash.dataset import NM000343
>>> dataset = NM000343(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Hinss2021_v2']

class eegdash.dataset.dataset.NM000344(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

CastillosBurstVEP100

Study:: nm000344 (NeMAR)
Author (year):: Castillos2023_CastillosBurstVEP100
Canonical:: —

Also importable as: NM000344, Castillos2023_CastillosBurstVEP100.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 12; recordings: 12; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000344 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000344 DOI: https://doi.org/10.1016/j.neuroimage.2023.120446

Examples

>>> from eegdash.dataset import NM000344
>>> dataset = NM000344(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000345(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

CastillosBurstVEP40

Study:: nm000345 (NeMAR)
Author (year):: Castillos2023_CastillosBurstVEP40
Canonical:: —

Also importable as: NM000345, Castillos2023_CastillosBurstVEP40.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 12; recordings: 12; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000345 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000345 DOI: https://doi.org/10.1016/j.neuroimage.2023.120446

Examples

>>> from eegdash.dataset import NM000345
>>> dataset = NM000345(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000346(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

CastillosCVEP100

Study:: nm000346 (NeMAR)
Author (year):: Castillos2023_CastillosCVEP100
Canonical:: —

Also importable as: NM000346, Castillos2023_CastillosCVEP100.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 12; recordings: 12; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000346 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000346 DOI: https://doi.org/10.1016/j.neuroimage.2023.120446

Examples

>>> from eegdash.dataset import NM000346
>>> dataset = NM000346(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

class eegdash.dataset.dataset.NM000347(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

HefmiIch2025

Study:: nm000347 (NeMAR)
Author (year):: HefmiIch2025
Canonical:: HEFMI_ICH, HEFMIICH

Also importable as: NM000347, HefmiIch2025, HEFMI_ICH, HEFMIICH.

Modality: eeg; Experiment type: Motor; Subject type: Other. Subjects: 37; recordings: 98; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000347 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000347 DOI: https://doi.org/10.1038/s41597-025-06100-7

Examples

>>> from eegdash.dataset import NM000347
>>> dataset = NM000347(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['HEFMI_ICH', 'HEFMIICH']

class eegdash.dataset.dataset.NM000348(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Yang2025

Study:: nm000348 (NeMAR)
Author (year):: Yang2025
Canonical:: —

Also importable as: NM000348, Yang2025.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 51; recordings: 153; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000348 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000348 DOI: https://doi.org/10.1038/s41597-025-04826-y

Examples

>>> from eegdash.dataset import NM000348
>>> dataset = NM000348(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = ['Yang2025']

class eegdash.dataset.dataset.NM000351(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Mainsah2025-P

Study:: nm000351 (NeMAR)
Author (year):: Mainsah2025_P
Canonical:: —

Also importable as: NM000351, Mainsah2025_P.

Modality: eeg; Experiment type: Attention; Subject type: Other. Subjects: 19; recordings: 228; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query

Merged query with the dataset filter applied.

Type:: dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000351 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000351 DOI: https://doi.org/10.13026/0byy-ry86

Examples

>>> from eegdash.dataset import NM000351
>>> dataset = NM000351(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

canonical_name = []

eegdash.dataset.dataset.NOD_EEG[source]: alias of DS005811

eegdash.dataset.dataset.NOD_MEG[source]: alias of DS005810

eegdash.dataset.dataset.NenckiSymfonia[source]: alias of DS004621

eegdash.dataset.dataset.Neuma[source]: alias of DS004588

eegdash.dataset.dataset.NeuroMorph[source]: alias of DS005241

eegdash.dataset.dataset.Nierula2019[source]: alias of DS005307

eegdash.dataset.dataset.Ning2024[source]: alias of DS004830

eegdash.dataset.dataset.Normannseth2026[source]: alias of DS007615

eegdash.dataset.dataset.OMEGA[source]: alias of DS000247

eegdash.dataset.dataset.ORHA[source]: alias of DS005363

eegdash.dataset.dataset.OcularLDT[source]: alias of DS002312

eegdash.dataset.dataset.Oikonomou2016[source]: alias of NM000119

eegdash.dataset.dataset.Omelyusik2026[source]: alias of DS006136

eegdash.dataset.dataset.Onton2024[source]: alias of DS006695

eegdash.dataset.dataset.OpenBMI_ERP[source]: alias of NM000323

eegdash.dataset.dataset.OpenBMI_MI[source]: alias of NM000338

eegdash.dataset.dataset.OpenBMI_P300[source]: alias of NM000323

eegdash.dataset.dataset.PAL[source]: alias of DS005059

eegdash.dataset.dataset.PDEEG[source]: alias of DS007526

eegdash.dataset.dataset.PD_EEG[source]: alias of DS007526

eegdash.dataset.dataset.PEARLNeuro[source]: alias of DS004796

eegdash.dataset.dataset.PEERS[source]: alias of DS004395

eegdash.dataset.dataset.PRIOS[source]: alias of DS004370

eegdash.dataset.dataset.PROMENADE[source]: alias of DS005946

eegdash.dataset.dataset.PWIe[source]: alias of DS005932

eegdash.dataset.dataset.Penalver2024[source]: alias of DS004502

eegdash.dataset.dataset.Peng2018[source]: alias of DS005777

eegdash.dataset.dataset.PerceiveImagine[source]: alias of DS005697

eegdash.dataset.dataset.PhysionetMI[source]: alias of DS004362

eegdash.dataset.dataset.Podcast[source]: alias of DS005574

eegdash.dataset.dataset.Pohle2019[source]: alias of DS006374

eegdash.dataset.dataset.RAM_catFR[source]: alias of DS005491

eegdash.dataset.dataset.RESPect_CCEP[source]: alias of DS004080

eegdash.dataset.dataset.RESPect_intraop[source]: alias of DS003844

eegdash.dataset.dataset.RESPect_longterm[source]: alias of DS003848

eegdash.dataset.dataset.Ramzaoui2024[source]: alias of DS006979

eegdash.dataset.dataset.Rani2019[source]: alias of DS004012

eegdash.dataset.dataset.Rockhill2022[source]: alias of DS004473

eegdash.dataset.dataset.Rodrigues2017[source]: alias of NM000221

eegdash.dataset.dataset.Romani2025[source]: alias of NM000147

eegdash.dataset.dataset.Romani2025_erp[source]: alias of NM000272

eegdash.dataset.dataset.Runabout[source]: alias of DS003620

eegdash.dataset.dataset.SINGSING[source]: alias of DS006629

eegdash.dataset.dataset.SSVEPMAMEM2[source]: alias of NM000120

eegdash.dataset.dataset.SSVEP_MAMEM3[source]: alias of NM000121

eegdash.dataset.dataset.STRONG[source]: alias of DS004849

eegdash.dataset.dataset.STReEF[source]: alias of DS005448

eegdash.dataset.dataset.Sakakura2024[source]: alias of DS004859

eegdash.dataset.dataset.Sakakura2025[source]: alias of DS004551

eegdash.dataset.dataset.Sato2024[source]: alias of DS007602

eegdash.dataset.dataset.Sato2025[source]: alias of DS007591

eegdash.dataset.dataset.SeizeIT2[source]: alias of DS005873

eegdash.dataset.dataset.Shalamberidze2025[source]: alias of DS007609

eegdash.dataset.dataset.Shin2017A[source]: alias of NM000267

eegdash.dataset.dataset.Shin2017B[source]: alias of NM000268

eegdash.dataset.dataset.SleepEDF[source]: alias of NM000185

eegdash.dataset.dataset.SleepEDFExpanded[source]: alias of NM000185

eegdash.dataset.dataset.Somato[source]: alias of DS003104

eegdash.dataset.dataset.Surrey_cEEGrid_sleep[source]: alias of DS005207

eegdash.dataset.dataset.THINGS[source]: alias of DS003825

eegdash.dataset.dataset.THINGSMEG[source]: alias of DS004212

eegdash.dataset.dataset.THINGS_EEG[source]: alias of DS003825

eegdash.dataset.dataset.THINGS_MEG[source]: alias of DS004212

eegdash.dataset.dataset.TMNRED[source]: alias of DS005383

eegdash.dataset.dataset.TNO[source]: alias of DS004660

eegdash.dataset.dataset.TX14[source]: alias of DS004841

eegdash.dataset.dataset.TX15[source]: alias of DS004842

eegdash.dataset.dataset.TX18[source]: alias of DS004854

eegdash.dataset.dataset.TX20[source]: alias of DS004657

eegdash.dataset.dataset.Todorovic2023[source]: alias of DS005261

eegdash.dataset.dataset.ToonFaces[source]: alias of DS004324

eegdash.dataset.dataset.Touryan1999[source]: alias of DS004118

eegdash.dataset.dataset.Tripathy2024[source]: alias of DS007473

eegdash.dataset.dataset.VEPCON[source]: alias of DS003505

eegdash.dataset.dataset.Veillette2019[source]: alias of DS005403

eegdash.dataset.dataset.Vianney2025[source]: alias of DS007358

eegdash.dataset.dataset.VisualContextTrajectory[source]: alias of DS004603

eegdash.dataset.dataset.VisualContextTrajectory_v2[source]: alias of DS006817

eegdash.dataset.dataset.WBCICSHU[source]: alias of NM000246

eegdash.dataset.dataset.WBCIC_SHU[source]: alias of NM000246

eegdash.dataset.dataset.WIRED_ICM[source]: alias of DS004993

eegdash.dataset.dataset.Wakeman2015[source]: alias of DS000117

eegdash.dataset.dataset.WakemanHenson[source]: alias of DS000117

eegdash.dataset.dataset.WakemanHenson_EEG_MEG[source]: alias of DS002718

eegdash.dataset.dataset.Weibo2014[source]: alias of NM000146

eegdash.dataset.dataset.Weisend2007[source]: alias of DS004107

eegdash.dataset.dataset.Wimmer2024[source]: alias of DS004398

eegdash.dataset.dataset.Yang2025[source]: alias of NM000348

eegdash.dataset.dataset.Yu2019[source]: alias of DS006386

eegdash.dataset.dataset.Yucel2014[source]: alias of DS005929

eegdash.dataset.dataset.Yucel2015[source]: alias of DS005776

eegdash.dataset.dataset.Zhang2025[source]: alias of NM000211

eegdash.dataset.dataset.Zhao2024[source]: alias of DS005473

eegdash.dataset.dataset.Zhou2016_NEMAR[source]: alias of NM000226

eegdash.dataset.dataset.Zhou2024[source]: alias of DS007471

eegdash.dataset.dataset.catFR_Categorized_Free_Recall[source]: alias of DS004809

eegdash.dataset.dataset.catFR_closed_loop[source]: alias of DS005558

eegdash.dataset.dataset.catFR_open_loop[source]: alias of DS005491

eegdash.dataset.dataset.catFR_stim[source]: alias of DS005491

eegdash.dataset.dataset.eldBETA[source]: alias of NM000130

eegdash.dataset.dataset.emg2qwerty[source]: alias of NM000104

eegdash.dataset.dataset.neuromorph[source]: alias of DS005241

eegdash.dataset.dataset.ocular_ldt[source]: alias of DS002312

eegdash.dataset.dataset.pyFR[source]: alias of DS004865