eegdash.dataset package#

Submodules#

Module contents#

Public API for dataset helpers and dynamically generated datasets.

class eegdash.dataset.DS000117(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Multisubject, multimodal face processing

Study:

ds000117 (OpenNeuro)

Author (year):

Wakeman2018

Canonical:

—

Also importable as: DS000117, Wakeman2018.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 17; recordings: 104; 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/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 = []
class eegdash.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.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:

—

Also importable as: DS000247, Niso2018.

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 = []
class eegdash.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:

—

Also importable as: DS000248, Gramfort2018.

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 = []
class eegdash.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.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.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.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.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.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:

—

Also importable as: DS002001, Mendola2019.

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 = []
class eegdash.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.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: Healthy. 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.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.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.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.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:

—

Also importable as: DS002312, Brooks2019.

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 = []
class eegdash.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.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.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.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.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.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.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.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:

—

Also importable as: DS002718, Wakeman2020.

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 = []
class eegdash.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.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.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.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.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.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.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.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.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:

—

Also importable as: DS002791, Mheich2020_DataSet1.

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 = []
class eegdash.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.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.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:

—

Also importable as: DS002833, Mheich2020_DataSet2.

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 = []
class eegdash.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.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.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:

—

Also importable as: DS002908, Bogacz2020.

Modality: meg; Experiment type: Decision-making; Subject type: Healthy. 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 = []
class eegdash.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.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.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.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:

—

Also importable as: DS003061, Delorme2020_auditory_oddball.

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 = []
class eegdash.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: Unknown. 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.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:

—

Also importable as: DS003082, Cote2020.

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 = []
class eegdash.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:

—

Also importable as: DS003104, Parkkonen2020.

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 = []
class eegdash.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.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.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.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.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:

—

Also importable as: DS003352, Hermann2020.

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 = []
class eegdash.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.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.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.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.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.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.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.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.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:

—

Also importable as: DS003483, Cognitive2021.

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 = []
class eegdash.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.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.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:

—

Also importable as: DS003505, Pascucci2021.

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 = []
class eegdash.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.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.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.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.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.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.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.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.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.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.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.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.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.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:

—

Also importable as: DS003620, Liebherr2021.

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 = []
class eegdash.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.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:

—

Also importable as: DS003633, Liu2021.

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 = []
class eegdash.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.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.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.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.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: Affect; 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.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.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.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:

—

Also importable as: DS003694, Griffiths2021.

Modality: meg; Experiment type: Memory; Subject type: Healthy. 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 = []
class eegdash.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.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:

—

Also importable as: DS003703, Kalenkovich2021.

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 = []
class eegdash.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:

—

Also importable as: DS003708, Hermes2021.

Modality: ieeg; Experiment type: Other; Subject type: Other. 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 = []
class eegdash.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:

—

Also importable as: DS003710, Williams2021.

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 = []
class eegdash.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.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:

—

Also importable as: DS003751, Mishra2021.

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 = []
class eegdash.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.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.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.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:

—

Also importable as: DS003774, Miyapuram2021.

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 = []
class eegdash.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.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.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.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.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.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.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.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:

—

Also importable as: DS003825, Grootswagers2021.

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 = []
class eegdash.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.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:

—

Also importable as: DS003844, Zweiphenning2021.

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 = []
class eegdash.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.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:

—

Also importable as: DS003848, Blooijs2021.

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 = []
class eegdash.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.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.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.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.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.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.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.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.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.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:

—

Also importable as: DS004010, Waschke2022.

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 = []
class eegdash.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.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:

—

Also importable as: DS004012, Rani2022.

Modality: meg; Experiment type: Perception; 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 = []
class eegdash.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.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.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.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.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.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.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.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.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.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.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: Healthy. 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.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.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:

—

Also importable as: DS004080, Blooijs2023_CCEP_ECoG.

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 = []
class eegdash.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:

—

Also importable as: DS004100, Bernabei2022.

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 = []
class eegdash.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:

—

Also importable as: DS004105, Garcia2022.

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 = []
class eegdash.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:

—

Also importable as: DS004106, Touryan2022.

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 = []
class eegdash.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:

—

Also importable as: DS004107, Weisend2022.

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 = []
class eegdash.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.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:

—

Also importable as: DS004118, Touryan2022_BCIT_Calibration.

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 = []
class eegdash.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:

—

Also importable as: DS004119, Touryan2022_BCIT_Basic.

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 = []
class eegdash.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:

—

Also importable as: DS004120, Touryan2022_BCIT_Baseline.

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 = []
class eegdash.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:

—

Also importable as: DS004121, Touryan2022_BCIT_Mind.

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 = []
class eegdash.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.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:

—

Also importable as: DS004123, Touryan2022_BCIT_Traffic.

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 = []
class eegdash.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.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.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.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.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.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.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.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.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.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:

—

Also importable as: DS004212, Hebart2022.

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 = []
class eegdash.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.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: 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/ds004252 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004252 DOI: https://doi.org/10.18112/openneuro.ds004252.v1.1.0 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.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.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.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.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.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:

—

Also importable as: DS004278, Kidder2022.

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 = []
class eegdash.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.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.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.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.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.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.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:

—

Also importable as: DS004324, Chacon2022.

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 = []
class eegdash.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.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:

—

Also importable as: DS004346, Ferrante2022.

Modality: meg; Experiment type: Decision-making; 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 = []
class eegdash.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.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:

—

Also importable as: DS004348, Mikkelsen2022.

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 = []
class eegdash.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.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.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.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:

—

Also importable as: DS004362, Schalk2022.

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 = []
class eegdash.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.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.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.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:

—

Also importable as: DS004370, Blooijs2022_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 = []
class eegdash.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.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.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.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:

—

Also importable as: DS004395, Kahana2023.

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 = []
class eegdash.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:

—

Also importable as: DS004398, Wimmer2023.

Modality: meg; Experiment type: Decision-making; 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/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 = []
class eegdash.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.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:

—

Also importable as: DS004444, Iwama2023_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 = []
class eegdash.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:

—

Also importable as: DS004446, Iwama2023_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 = []
class eegdash.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:

—

Also importable as: DS004447, Iwama2023_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 = []
class eegdash.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:

—

Also importable as: DS004448, Iwama2023_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 = []
class eegdash.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:

—

Also importable as: DS004457, Huang2023.

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 = []
class eegdash.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.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:

—

Also importable as: DS004473, Rockhill2023.

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 = []
class eegdash.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.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.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:

—

Also importable as: DS004483, Planton2023.

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 = []
class eegdash.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:

—

Also importable as: DS004502, Penalver2023.

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 = []
class eegdash.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.v1.0.9 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.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.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.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.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.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.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:

—

Also importable as: DS004519, Ester2023_Internal.

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 = []
class eegdash.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:

—

Also importable as: DS004520, Ester2023_Changes.

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 = []
class eegdash.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:

—

Also importable as: DS004521, Ester2023_Changes_behavioral.

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 = []
class eegdash.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.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:

—

Also importable as: DS004541, Ferron2023.

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 = []
class eegdash.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:

—

Also importable as: DS004551, Sakakura2023_children_slow_wave.

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 = []
class eegdash.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.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.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.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:

—

Also importable as: DS004572, Kekecs2023.

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 = []
class eegdash.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.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.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.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.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.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.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.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:

—

Also importable as: DS004588, Georgiadis2023.

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 = []
class eegdash.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.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:

—

Also importable as: DS004598, Faraz2023.

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 = []
class eegdash.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: 545; 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.v1.0.3 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.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:

—

Also importable as: DS004603, Lowe2023.

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 = []
class eegdash.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:

—

Also importable as: DS004621, Patrycja2023_Nencki.

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 = []
class eegdash.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:

—

Also importable as: DS004624, Mivalt2025.

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 = []
class eegdash.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.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.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.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.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:

—

Also importable as: DS004657, Metcalfe2023_Driving.

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 = []
class eegdash.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:

—

Also importable as: DS004660, Johnson2023_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 = []
class eegdash.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:

—

Also importable as: DS004661, Johnson2023_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 = []
class eegdash.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.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.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.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.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.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.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.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.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.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:

—

Also importable as: DS004774, Boom2023.

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 = []
class eegdash.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.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.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.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:

—

Also importable as: DS004796, Patrycja2023_Polish.

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 = []
class eegdash.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.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:

—

Also importable as: DS004809, Herrema2023_Categorized_Free_Recall.

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 = []
class eegdash.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.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.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.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:

—

Also importable as: DS004830, Ning2023.

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 = []
class eegdash.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.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.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:

—

Also importable as: DS004841, Larkin2023_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 = []
class eegdash.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:

—

Also importable as: DS004842, Larkin2023_TX15.

Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. 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 = []
class eegdash.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.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.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:

—

Also importable as: DS004849, Johnson2023_STRONG.

Modality: eeg; Experiment type: Memory; 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/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 = []
class eegdash.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:

—

Also importable as: DS004850, Johnson2023_ODE.

Modality: eeg; Experiment type: Memory; 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/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 = []
class eegdash.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:

—

Also importable as: DS004851, Johnson2023_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 = []
class eegdash.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:

—

Also importable as: DS004852, Johnson2023_InsurgentCivilian.

Modality: eeg; Experiment type: Memory; 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/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 = []
class eegdash.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: 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/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.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:

—

Also importable as: DS004854, Johnson2023_TX18.

Modality: eeg; Experiment type: Memory; 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/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 = []
class eegdash.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: 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/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.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:

—

Also importable as: DS004859, Sakakura2023_children_Stroop.

Modality: ieeg; Experiment type: Attention; Subject type: Development. 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 = []
class eegdash.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.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:

—

Also importable as: DS004865, Herrema2023_pyFR_Delayed_Free.

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 = []
class eegdash.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.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.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.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: Unknown; 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.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.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.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:

—

Also importable as: DS004944, Costa2024.

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 = []
class eegdash.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:

—

Also importable as: DS004951, Haupt2024_Braille.

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 = []
class eegdash.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.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.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:

—

Also importable as: DS004977, Huang2024.

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 = []
class eegdash.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.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:

—

Also importable as: DS004993, Hamilton2024.

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 = []
class eegdash.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:

—

Also importable as: DS004995, Moerel2024.

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 = []
class eegdash.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.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:

—

Also importable as: DS005007, Kitazawa2024.

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 = []
class eegdash.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.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:

—

Also importable as: DS005028, Chandravadia2024.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. 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 = []
class eegdash.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.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.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:

—

Also importable as: DS005059, Herrema2024_Paired.

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 = []
class eegdash.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.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.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.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.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.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.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.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.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.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.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.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.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:

—

Also importable as: DS005170, Zhang2024_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 = []
class eegdash.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:

—

Also importable as: DS005178, Tabar2024.

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 = []
class eegdash.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:

—

Also importable as: DS005185, Mikkelsen2024_Ear_Sleep_Monitoring.

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 = []
class eegdash.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.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:

—

Also importable as: DS005207, Mikkelsen2024_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 = []
class eegdash.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:

—

Also importable as: DS005241, Rodriguez2024.

Modality: meg; Experiment type: Perception; 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 = []
class eegdash.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:

—

Also importable as: DS005261, Todorovic2024.

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 = []
class eegdash.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:

—

Also importable as: DS005262, Metwalli2024.

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 = []
class eegdash.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.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: Unknown. 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.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.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.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: Perception; 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.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.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.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: Perception; Subject type: Healthy. 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.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.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.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.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.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.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:

—

Also importable as: DS005307, Nierula2024.

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 = []
class eegdash.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.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.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.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:

—

Also importable as: DS005345, Ma2024.

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 = []
class eegdash.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.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.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:

—

Also importable as: DS005363, Haupt2024_Object.

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 = []
class eegdash.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:

—

Also importable as: DS005383, Bai2024.

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 = []
class eegdash.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.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.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.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:

—

Also importable as: DS005403, Veillette2024.

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 = []
class eegdash.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:

—

Also importable as: DS005406, Formica2024.

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 = []
class eegdash.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.DS005408(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

The effect of speech masking on the human subcortical response to continuous 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.v1.0.1

Examples

>>> from eegdash.dataset import DS005408
>>> dataset = DS005408(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.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.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.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.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.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:

—

Also importable as: DS005420, Gama2024.

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 = []
class eegdash.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.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:

—

Also importable as: DS005448, Jelsma2024.

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 = []
class eegdash.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:

—

Also importable as: DS005473, Xiangyue2024_29_BP.

Modality: eeg; Experiment type: Perception; 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 = []
class eegdash.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: Unknown; 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.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: Healthy. 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.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:

—

Also importable as: DS005491, Herrema2024_Categorized.

Modality: ieeg; Experiment type: Memory; Subject type: Healthy. 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 = []
class eegdash.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:

—

Also importable as: DS005494, Herrema2024_Cued.

Modality: ieeg; Experiment type: Memory; Subject type: Healthy. 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 = []
class eegdash.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:

—

Also importable as: DS005505, Shirazi2024_R1.

Modality: eeg; Experiment type: Learning; 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 = []
class eegdash.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:

—

Also importable as: DS005506, Shirazi2024_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 = []
class eegdash.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:

—

Also importable as: DS005507, Shirazi2024_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 = []
class eegdash.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:

—

Also importable as: DS005508, Shirazi2024_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 = []
class eegdash.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:

—

Also importable as: DS005509, Shirazi2024_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 = []
class eegdash.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:

—

Also importable as: DS005510, Shirazi2024_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 = []
class eegdash.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:

—

Also importable as: DS005512, Shirazi2024_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 = []
class eegdash.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:

—

Also importable as: DS005514, Shirazi2024_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 = []
class eegdash.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:

—

Also importable as: DS005515, Shirazi2024_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 = []
class eegdash.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:

—

Also importable as: DS005516, Shirazi2024_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 = []
class eegdash.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.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: Healthy. 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.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.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.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.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:

—

Also importable as: DS005545, Kanno2024.

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 = []
class eegdash.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:

—

Also importable as: DS005555, LopezLarraz2024.

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 = []
class eegdash.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.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:

—

Also importable as: DS005558, Herrema2024_Categorized_Free.

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 = []
class eegdash.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.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: Decision-making; 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.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:

—

Also importable as: DS005574, Zada2024.

Modality: ieeg; 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/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 = []
class eegdash.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.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.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.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: Perception; Subject type: Healthy. 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.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.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.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.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.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.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.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.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.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.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:

—

Also importable as: DS005697, Li2024_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 = []
class eegdash.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.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:

—

Also importable as: DS005776, Yucel2025_Electrical.

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 = []
class eegdash.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:

—

Also importable as: DS005777, Peng2025.

Modality: fnirs; Experiment type: Clinical/Intervention; Subject type: Healthy. 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 = []
class eegdash.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.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.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:

—

Also importable as: DS005810, Zhang2025_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 = []
class eegdash.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:

—

Also importable as: DS005811, Zhang2025_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 = []
class eegdash.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.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.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:

—

Also importable as: DS005857, Broitman2025.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. 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 = []
class eegdash.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.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:

—

Also importable as: DS005866, TerhuneCotter2025_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 = []
class eegdash.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:

—

Also importable as: DS005868, TerhuneCotter2025_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 = []
class eegdash.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:

—

Also importable as: DS005872, Plomecka2025.

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 = []
class eegdash.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:

—

Also importable as: DS005873, Bhagubai2025.

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 = []
class eegdash.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.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.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:

—

Also importable as: DS005929, MotionYucel2014.

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 = []
class eegdash.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: Unknown; 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.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.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:

—

Also importable as: DS005932, Holcomb2025.

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 = []
class eegdash.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: Healthy. 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.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:

—

Also importable as: DS005946, Frau2025.

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 = []
class eegdash.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.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.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:

—

Also importable as: DS005963, Mesquita2025.

Modality: fnirs; Experiment type: Motor; Subject type: Healthy. 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 = []
class eegdash.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:

—

Also importable as: DS005964, Luke2025.

Modality: fnirs; Experiment type: Perception; 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/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 = []
class eegdash.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.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.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.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:

—

Also importable as: DS006035, Lin2025.

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 = []
class eegdash.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.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.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.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.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.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:

—

Also importable as: DS006107, Kuroda2025.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. 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 = []
class eegdash.dataset.DS006110(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

PsiConnect

Study:

ds006110 (OpenNeuro)

Author (year):

nan

Canonical:

—

Also importable as: DS006110, nan.

Modality: eeg; Experiment type: Affect; Subject type: Other. Subjects: 65; recordings: 124; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006110 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006110 DOI: https://doi.org/10.18112/openneuro.ds006110.v1.2.0

Examples

>>> from eegdash.dataset import DS006110
>>> dataset = DS006110(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.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.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:

—

Also importable as: DS006136, Omelyusik2025.

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 = []
class eegdash.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.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:

—

Also importable as: DS006159, LeganesFonteneau2025.

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 = []
class eegdash.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:

—

Also importable as: DS006171, Melcon2025.

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 = []
class eegdash.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.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.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.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.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.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.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:

—

Also importable as: DS006317, Zhang2025_Chisco_2_0.

Modality: eeg; Experiment type: Perception; 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.v1.1.1

Examples

>>> from eegdash.dataset import DS006317
>>> dataset = DS006317(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.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.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:

—

Also importable as: DS006366, Rose2025.

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 = []
class eegdash.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.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.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:

—

Also importable as: DS006374, Pohle2025.

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 = []
class eegdash.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: Other; Subject type: Healthy. 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.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:

—

Also importable as: DS006386, Yu2025.

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 = []
class eegdash.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:

—

Also importable as: DS006392, Attia2025.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Other. 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 = []
class eegdash.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.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.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.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:

—

Also importable as: DS006446, Kinley2025.

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 = []
class eegdash.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.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.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:

—

Also importable as: DS006465, Ma2025.

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 = []
class eegdash.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:

—

Also importable as: DS006466, Kim2025_HeartBEAM_Older_Adult.

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 = []
class eegdash.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:

—

Also importable as: DS006468, Habersetzer2025.

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 = []
class eegdash.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.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.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.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: 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/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.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:

—

Also importable as: DS006545, ReliabilityDubois2024.

Modality: fnirs; Experiment type: Perception; Subject type: Healthy. 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 = []
class eegdash.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:

—

Also importable as: DS006547, Ghaffari2025.

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 = []
class eegdash.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: Perception; 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/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.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.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: 67; recordings: 67; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS 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.v1.0.5

Examples

>>> from eegdash.dataset import DS006576
>>> dataset = DS006576(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.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.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:

—

Also importable as: DS006629, Chanoine2025.

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 = []
class eegdash.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.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.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.v1.0.4

Examples

>>> from eegdash.dataset import DS006673
>>> dataset = DS006673(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.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:

—

Also importable as: DS006695, Onton2025.

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 = []
class eegdash.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.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.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.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.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.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.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.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:

—

Also importable as: DS006817, Lowe2025.

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/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 = []
class eegdash.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.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:

—

Also importable as: DS006840, Cai2025.

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 = []
class eegdash.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.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.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.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.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.DS006897(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

No effect of rhythmic visual stimulation on experimental pain perception

Study:

ds006897 (OpenNeuro)

Author (year):

nan

Canonical:

—

Also importable as: DS006897, nan.

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/ds006897 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006897 DOI: https://doi.org/10.18112/openneuro.ds006897.v1.0.0

Examples

>>> from eegdash.dataset import DS006897
>>> dataset = DS006897(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.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.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.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: Perception; Subject type: Healthy. 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.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.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.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.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.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.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.v1.0.1

Examples

>>> from eegdash.dataset import DS006963
>>> dataset = DS006963(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.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:

—

Also importable as: DS006979, Ramzaoui2025.

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 = []
class eegdash.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.DS007012(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

FOODEEG: An open dataset of human electroencephalographic and behavioural responses to food images

Study:

ds007012 (OpenNeuro)

Author (year):

nan

Canonical:

—

Also importable as: DS007012, nan.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 117; 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/ds007012 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007012 DOI: https://doi.org/10.18112/openneuro.ds007012.v1.1.0

Examples

>>> from eegdash.dataset import DS007012
>>> dataset = DS007012(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.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.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:

—

Also importable as: DS007028, Kajikawa2025.

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 = []
class eegdash.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:

—

Also importable as: DS007052, Couperus2025_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 = []
class eegdash.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:

—

Also importable as: DS007056, Couperus2025_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 = []
class eegdash.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:

—

Also importable as: DS007069, Couperus2025_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 = []
class eegdash.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.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.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:

—

Also importable as: DS007096, Couperus2025_PURSUE_N170_Face.

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 = []
class eegdash.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:

—

Also importable as: DS007118, Hatano2025_part1.

Modality: ieeg; Experiment type: Sleep; Subject type: Epilepsy. 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 = []
class eegdash.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: Other; Subject type: Epilepsy. 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.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.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:

—

Also importable as: DS007137, Couperus2025_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 = []
class eegdash.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:

—

Also importable as: DS007139, Couperus2025_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 = []
class eegdash.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.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:

—

Also importable as: DS007169, Barras2026_Multimodal.

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 = []
class eegdash.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:

—

Also importable as: DS007172, Reinke2026.

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 = []
class eegdash.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.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.v1.0.2

Examples

>>> from eegdash.dataset import DS007176
>>> dataset = DS007176(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.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:

—

Also importable as: DS007180, FuentesGuerra2026.

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 = []
class eegdash.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.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:

—

Also importable as: DS007216, Kucyi2026.

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 = []
class eegdash.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.DS007222(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Visual Occlusions with Treadmill Walking Speeds EEG

Study:

ds007222 (OpenNeuro)

Author (year):

nan

Canonical:

—

Also importable as: DS007222, nan.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 15; recordings: 75; 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/ds007222 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007222 DOI: https://doi.org/10.18112/openneuro.ds007222.v1.0.0

Examples

>>> from eegdash.dataset import DS007222
>>> dataset = DS007222(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.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:

—

Also importable as: DS007262, Barras2026_Cognitive.

Modality: eeg; Experiment type: Decision-making; 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 = []
class eegdash.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:

—

Also importable as: DS007314, Martzoukou2026_tACS.

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 = []
class eegdash.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:

—

Also importable as: DS007315, Martzoukou2026_tACS_Patients.

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 = []
class eegdash.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:

—

Also importable as: DS007322, Mishra2026.

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 = []
class eegdash.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:

—

Also importable as: DS007338, Plomecka2026.

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 = []
class eegdash.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.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:

—

Also importable as: DS007353, Zhang2026.

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 = []
class eegdash.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:

—

Also importable as: DS007358, Vianney2026.

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 = []
class eegdash.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:

—

Also importable as: DS007406, Edit2026.

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 = []
class eegdash.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:

—

Also importable as: DS007420, Gao2026_Light_Weight_Multi.

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 = []
class eegdash.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:

—

Also importable as: DS007427, Isaza2026_Comprehensive.

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 = []
class eegdash.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:

—

Also importable as: DS007431, Ataseven2026.

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 = []
class eegdash.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.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.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:

—

Also importable as: DS007463, Fogarty2026_Very.

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 = []
class eegdash.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:

—

Also importable as: DS007471, Zhou2026.

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 = []
class eegdash.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:

—

Also importable as: DS007473, Fogarty2026_High.

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 = []
class eegdash.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: Healthy. 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.DS007509(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

1hrPVTdataset_allSubjects

Study:

ds007509 (OpenNeuro)

Author (year):

nan

Canonical:

—

Also importable as: DS007509, nan.

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/ds007509 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007509 DOI: https://doi.org/10.18112/openneuro.ds007509.v1.0.3

Examples

>>> from eegdash.dataset import DS007509
>>> dataset = DS007509(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.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:

—

Also importable as: DS007521, Moerel2026.

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 = []
class eegdash.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:

—

Also importable as: DS007523, Bel2026.

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.v1.0.1

Examples

>>> from eegdash.dataset import DS007523
>>> dataset = DS007523(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.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:

—

Also importable as: DS007524, Pallier2025.

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.v1.1.0

Examples

>>> from eegdash.dataset import DS007524
>>> dataset = DS007524(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.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:

—

Also importable as: DS007526, Katzir2026.

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.v1.0.1

Examples

>>> from eegdash.dataset import DS007526
>>> dataset = DS007526(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.dataset.DS007537(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

A multimodal dataset of EEG, eye-tracking, and physiological signals during smartphone interaction

Study:

ds007537 (OpenNeuro)

Author (year):

nan

Canonical:

—

Also importable as: DS007537, nan.

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/ds007537 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007537 DOI: https://doi.org/10.18112/openneuro.ds007537.v1.0.0

Examples

>>> from eegdash.dataset import DS007537
>>> dataset = DS007537(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.dataset.DS007541(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

ds007541

Study:

ds007541 (OpenNeuro)

Author (year):

nan

Canonical:

—

Also importable as: DS007541, nan.

Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 32; recordings: 960; 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/ds007541 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007541

Examples

>>> from eegdash.dataset import DS007541
>>> dataset = DS007541(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.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.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: Healthy. 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.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:

—

Also importable as: DS007591, Sato2026_Delineating.

Modality: eeg; Experiment type: Other; Subject type: Other. 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 = []
class eegdash.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:

—

Also importable as: DS007602, Sato2026_Speech.

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 = []
class eegdash.dataset.DS007605(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EPOC

Study:

ds007605 (OpenNeuro)

Author (year):

nan

Canonical:

—

Also importable as: DS007605, nan.

Modality: eeg; Experiment type: Attention; Subject type: Other. Subjects: 97; recordings: 97; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007605 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007605 DOI: https://doi.org/10.18112/openneuro.ds007605.v1.0.0

Examples

>>> from eegdash.dataset import DS007605
>>> dataset = DS007605(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.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:

—

Also importable as: DS007609, Shalamberidze2026.

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 = []
class eegdash.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 = []
class eegdash.dataset.DS007629(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

ROAMM

Study:

ds007629 (OpenNeuro)

Author (year):

nan

Canonical:

—

Also importable as: DS007629, nan.

Modality: eeg; Experiment type: Attention; 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/ds007629 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007629 DOI: https://doi.org/10.18112/openneuro.ds007629.v1.1.0

Examples

>>> from eegdash.dataset import DS007629
>>> dataset = DS007629(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.dataset.DS007630(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

EEG-Speech Brain Decoding Dataset

Study:

ds007630 (OpenNeuro)

Author (year):

nan

Canonical:

—

Also importable as: DS007630, nan.

Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 3; recordings: 1974; 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/ds007630 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007630 DOI: https://doi.org/10.18112/openneuro.ds007630.v1.0.0

Examples

>>> from eegdash.dataset import DS007630
>>> dataset = DS007630(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.dataset.DS007640(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Dataset of emotion recognition using validated video stimuli with large-scale behavioral survey and MEG recordings

Study:

ds007640 (OpenNeuro)

Author (year):

nan

Canonical:

—

Also importable as: DS007640, nan.

Modality: meg; Experiment type: Affect; Subject type: Healthy. Subjects: 23; recordings: 94; 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/ds007640 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007640 DOI: https://doi.org/10.18112/openneuro.ds007640.v1.0.1

Examples

>>> from eegdash.dataset import DS007640
>>> dataset = DS007640(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.dataset.DS007647(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Different Doors

Study:

ds007647 (OpenNeuro)

Author (year):

nan

Canonical:

—

Also importable as: DS007647, nan.

Modality: eeg; Experiment type: Decision-making; 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/ds007647 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007647 DOI: https://doi.org/10.18112/openneuro.ds007647.v1.0.1

Examples

>>> from eegdash.dataset import DS007647
>>> dataset = DS007647(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.dataset.DS007648(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

CrossModal Study

Study:

ds007648 (OpenNeuro)

Author (year):

nan

Canonical:

—

Also importable as: DS007648, nan.

Modality: eeg; Experiment type: Attention; 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/ds007648 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007648 DOI: https://doi.org/10.18112/openneuro.ds007648.v1.1.0

Examples

>>> from eegdash.dataset import DS007648
>>> dataset = DS007648(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.dataset.DS007655(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

MorseEEG-ATP

Study:

ds007655 (OpenNeuro)

Author (year):

nan

Canonical:

—

Also importable as: DS007655, nan.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 32; 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/ds007655 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007655 DOI: https://doi.org/10.18112/openneuro.ds007655.v1.0.1

Examples

>>> from eegdash.dataset import DS007655
>>> dataset = DS007655(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.dataset.DS007663(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

CrossModal Study

Study:

ds007663 (OpenNeuro)

Author (year):

nan

Canonical:

—

Also importable as: DS007663, nan.

Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 27; 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/ds007663 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007663 DOI: https://doi.org/10.18112/openneuro.ds007663.v1.0.0

Examples

>>> from eegdash.dataset import DS007663
>>> dataset = DS007663(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.dataset.DS007666(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Grupos Pre and Post bids

Study:

ds007666 (OpenNeuro)

Author (year):

nan

Canonical:

—

Also importable as: DS007666, nan.

Modality: eeg; Experiment type: Unknown; Subject type: Healthy. Subjects: 55; recordings: 110; tasks: 20.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007666 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007666 DOI: https://doi.org/10.18112/openneuro.ds007666.v1.0.0

Examples

>>> from eegdash.dataset import DS007666
>>> dataset = DS007666(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
canonical_name = []
class eegdash.dataset.DS007667(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

Grupos Pre and Post bids

Study:

ds007667 (OpenNeuro)

Author (year):

nan

Canonical:

—

Also importable as: DS007667, nan.

Modality: eeg; Experiment type: Resting-state; Subject type: Healthy. Subjects: 55; recordings: 110; tasks: 20.

Parameters: