eegdash.dataset package

Submodules

• eegdash.dataset.base module
• eegdash.dataset.bids_dataset module
• eegdash.dataset.dataset module
• eegdash.dataset.io module
• eegdash.dataset.registry module

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)

Bases: EEGDashDataset

OpenNeuro dataset ds000117. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds000246. Modality: meg; Experiment type: Unknown; Subject type: Unknown. Subjects: 3; recordings: 57; 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds000247. Modality: meg; Experiment type: Unknown; Subject type: Unknown. Subjects: 7; recordings: 282; 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds000248. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds001785. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds001787. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds001810. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds001849. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds001971. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002001. Modality: meg; Experiment type: Unknown; Subject type: Unknown. Subjects: 12; recordings: 1006; 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002034. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002094. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 20; recordings: 43; tasks: 3.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002158. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002181. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002218. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002312. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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()

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

Bases: EEGDashDataset

OpenNeuro dataset ds002336. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002338. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002550. Modality: meg; Experiment type: Unknown; Subject type: Working memory. Subjects: 23; recordings: 12754; 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002578. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002680. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002691. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002712. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002718. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002720. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 18; recordings: 165; 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/ds002720 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002720 DOI: https://doi.org/10.18112/openneuro.ds002720.v1.0.1

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002721. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 31; recordings: 185; 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/ds002721 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002721 DOI: https://doi.org/10.18112/openneuro.ds002721.v1.0.2

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002722. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 19; recordings: 94; 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/ds002722 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002722 DOI: https://doi.org/10.18112/openneuro.ds002722.v1.0.1

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002723. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 8; recordings: 44; 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/ds002723 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002723 DOI: https://doi.org/10.18112/openneuro.ds002723.v1.1.0

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002724. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 10; recordings: 96; 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/ds002724 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002724 DOI: https://doi.org/10.18112/openneuro.ds002724.v1.0.1

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002725. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002761. Modality: meg; Experiment type: Unknown; Subject type: MEG. Subjects: 26; recordings: 2806; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002778. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002791. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002799. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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()

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

Bases: EEGDashDataset

OpenNeuro dataset ds002814. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002833. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002885. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002893. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds002908. Modality: meg; Experiment type: Unknown; Subject type: Decision Making. Subjects: 14; recordings: 539; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003004. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 34; recordings: 34; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003029. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003039. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003061. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003078. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003082. Modality: meg; Experiment type: Unknown; Subject type: Unknown. Subjects: 3; recordings: 82; 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/ds003082 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003082

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003104. Modality: meg; Experiment type: Unknown; Subject type: Unknown. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003104 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003104 DOI: https://doi.org/10.18112/openneuro.ds003104.v1.0.0

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003190. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003194. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003195. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003343. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003352. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003374. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003392. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003420. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003421. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003458. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003474. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003478. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003483. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003490. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003498. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003505. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003506. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003509. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003516. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003517. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003518. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003519. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003522. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003523. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003555. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003568. Modality: meg; Experiment type: Unknown; Subject type: Unknown. Subjects: 52; recordings: 3710; 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.v1.0.4

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003570. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003574. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003602. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003620. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003626. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003633. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003638. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003645. Modality: eeg, meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003655. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003670. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003682. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003688. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003690. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003694. Modality: meg; Experiment type: Unknown; Subject type: Unknown. Subjects: 28; recordings: 132; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003702. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 47; recordings: 47; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003703. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003708. Modality: ieeg; Experiment type: Unknown; Subject type: connectivity. Subjects: 2; 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/ds003708 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003708 DOI: https://doi.org/10.18112/openneuro.ds003708.v1.0.4

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003710. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003739. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003751. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003753. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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 DOI: https://doi.org/10.18112/openneuro.ds003753.v1.1.0

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003766. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003768. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

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

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

Bases: EEGDashDataset

OpenNeuro dataset ds003774. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS003774
>>> dataset = DS003774(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS003775(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds003775. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS003775
>>> dataset = DS003775(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS003800(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds003800. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS003800
>>> dataset = DS003800(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS003801(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds003801. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS003801
>>> dataset = DS003801(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS003805(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds003805. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003805 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003805 DOI: https://doi.org/10.18112/openneuro.ds003805.v1.0.0

Examples

>>> from eegdash.dataset import DS003805
>>> dataset = DS003805(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS003810(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds003810. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS003810
>>> dataset = DS003810(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS003822(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds003822. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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 DOI: https://doi.org/10.18112/openneuro.ds003822.v1.1.0

Examples

>>> from eegdash.dataset import DS003822
>>> dataset = DS003822(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS003825(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds003825. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS003825
>>> dataset = DS003825(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS003838(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds003838. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS003838
>>> dataset = DS003838(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS003844(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds003844. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS003844
>>> dataset = DS003844(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS003846(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds003846. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS003846
>>> dataset = DS003846(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS003848(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds003848. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS003848
>>> dataset = DS003848(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS003876(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds003876. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS003876
>>> dataset = DS003876(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS003885(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds003885. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS003885
>>> dataset = DS003885(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS003887(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds003887. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS003887
>>> dataset = DS003887(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS003922(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds003922. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS003922
>>> dataset = DS003922(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS003944(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds003944. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS003944
>>> dataset = DS003944(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS003947(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds003947. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS003947
>>> dataset = DS003947(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS003969(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds003969. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS003969
>>> dataset = DS003969(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS003987(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds003987. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS003987
>>> dataset = DS003987(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004000(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004000. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004000
>>> dataset = DS004000(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004010(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004010. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004010
>>> dataset = DS004010(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004011(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004011. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004011
>>> dataset = DS004011(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004012(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004012. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004012
>>> dataset = DS004012(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004015(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004015. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004015
>>> dataset = DS004015(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004017(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004017. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004017
>>> dataset = DS004017(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004018(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004018. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004018
>>> dataset = DS004018(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004019(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004019. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004019
>>> dataset = DS004019(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004022(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004022. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004022
>>> dataset = DS004022(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004024(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004024. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004024
>>> dataset = DS004024(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004033(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004033. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004033
>>> dataset = DS004033(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004040(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004040. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004040
>>> dataset = DS004040(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004043(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004043. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004043
>>> dataset = DS004043(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004067(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004067. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004067
>>> dataset = DS004067(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004075(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004075. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 29; recordings: 116; tasks: 4.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004075 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004075 DOI: https://doi.org/10.18112/openneuro.ds004075.v1.0.0

Examples

>>> from eegdash.dataset import DS004075
>>> dataset = DS004075(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004078(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004078. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004078
>>> dataset = DS004078(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004080(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004080. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004080
>>> dataset = DS004080(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004100(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004100. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004100
>>> dataset = DS004100(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004105(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004105. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004105
>>> dataset = DS004105(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004106(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004106. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004106
>>> dataset = DS004106(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004107(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004107. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004107
>>> dataset = DS004107(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004117(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004117. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004117
>>> dataset = DS004117(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004118(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004118. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004118
>>> dataset = DS004118(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004119(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004119. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004119
>>> dataset = DS004119(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004120(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004120. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004120
>>> dataset = DS004120(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004121(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004121. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004121
>>> dataset = DS004121(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004122(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004122. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004122
>>> dataset = DS004122(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004123(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004123. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004123
>>> dataset = DS004123(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004127(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004127. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004127
>>> dataset = DS004127(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004147(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004147. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004147
>>> dataset = DS004147(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004148(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004148. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004148
>>> dataset = DS004148(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004151(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004151. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004151
>>> dataset = DS004151(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004152(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004152. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004152
>>> dataset = DS004152(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004166(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004166. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004166
>>> dataset = DS004166(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004194(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004194. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004194
>>> dataset = DS004194(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004196(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004196. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004196
>>> dataset = DS004196(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004200(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004200. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004200
>>> dataset = DS004200(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004212(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004212. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004212
>>> dataset = DS004212(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004229(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004229. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004229
>>> dataset = DS004229(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004252(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004252. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004252 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004252 DOI: https://doi.org/10.18112/openneuro.ds004252.v1.0.2

Examples

>>> from eegdash.dataset import DS004252
>>> dataset = DS004252(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004256(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004256. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004256
>>> dataset = DS004256(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004262(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004262. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004262
>>> dataset = DS004262(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004264(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004264. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004264
>>> dataset = DS004264(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004276(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004276. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004276
>>> dataset = DS004276(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004278(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004278. Modality: meg; Experiment type: Unknown; Subject type: Unknown. Subjects: 31; recordings: 876; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

Examples

>>> from eegdash.dataset import DS004278
>>> dataset = DS004278(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004279(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004279. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004279
>>> dataset = DS004279(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004284(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004284. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004284
>>> dataset = DS004284(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004295(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004295. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004295
>>> dataset = DS004295(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004306(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004306. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004306
>>> dataset = DS004306(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004315(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004315. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004315
>>> dataset = DS004315(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004317(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004317. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004317
>>> dataset = DS004317(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004324(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004324. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004324
>>> dataset = DS004324(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004330(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004330. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004330
>>> dataset = DS004330(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004346(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004346. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004346
>>> dataset = DS004346(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004347(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004347. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004347
>>> dataset = DS004347(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004348(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004348. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004348
>>> dataset = DS004348(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004350(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004350. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004350
>>> dataset = DS004350(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004356(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004356. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004356
>>> dataset = DS004356(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004357(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004357. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004357
>>> dataset = DS004357(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004362(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004362. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004362
>>> dataset = DS004362(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004367(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004367. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004367
>>> dataset = DS004367(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004368(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004368. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004368
>>> dataset = DS004368(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004369(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004369. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004369
>>> dataset = DS004369(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004370(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004370. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004370
>>> dataset = DS004370(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004381(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004381. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004381
>>> dataset = DS004381(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004388(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004388. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004388
>>> dataset = DS004388(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004389(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004389. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004389
>>> dataset = DS004389(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004395(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004395. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004395
>>> dataset = DS004395(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004398(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004398. Modality: meg; Experiment type: Unknown; Subject type: Unknown. Subjects: 2; recordings: 20; 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/ds004398 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004398

Examples

>>> from eegdash.dataset import DS004398
>>> dataset = DS004398(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004408(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004408. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004408
>>> dataset = DS004408(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004444(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004444. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004444
>>> dataset = DS004444(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004446(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004446. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004446
>>> dataset = DS004446(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004447(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004447. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004447
>>> dataset = DS004447(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004448(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004448. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004448
>>> dataset = DS004448(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004457(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004457. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 6; recordings: 2801; 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/ds004457 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004457 DOI: https://doi.org/10.18112/openneuro.ds004457.v1.0.2

Examples

>>> from eegdash.dataset import DS004457
>>> dataset = DS004457(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004460(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004460. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004460
>>> dataset = DS004460(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004473(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004473. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004473
>>> dataset = DS004473(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004475(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004475. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004475
>>> dataset = DS004475(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004477(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004477. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 9; recordings: 9; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004477 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004477 DOI: https://doi.org/10.18112/openneuro.ds004477.v1.0.2

Examples

>>> from eegdash.dataset import DS004477
>>> dataset = DS004477(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004483(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004483. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004483
>>> dataset = DS004483(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004502(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004502. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004502
>>> dataset = DS004502(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004504(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004504. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 88; recordings: 88; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004504 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004504 DOI: https://doi.org/10.18112/openneuro.ds004504.v1.0.8

Examples

>>> from eegdash.dataset import DS004504
>>> dataset = DS004504(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004505(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004505. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004505
>>> dataset = DS004505(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004511(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004511. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004511
>>> dataset = DS004511(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004514(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004514. Modality: eeg, fnirs; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS004515(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004515. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004515
>>> dataset = DS004515(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004517(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004517. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS004519(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004519. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004519
>>> dataset = DS004519(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004520(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004520. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004520
>>> dataset = DS004520(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004521(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004521. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 34; recordings: 34; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004521 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004521 DOI: https://doi.org/10.18112/openneuro.ds004521.v1.0.1

Examples

>>> from eegdash.dataset import DS004521
>>> dataset = DS004521(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004532(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004532. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004532
>>> dataset = DS004532(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004551(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004551. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004551
>>> dataset = DS004551(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004554(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004554. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004554
>>> dataset = DS004554(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004561(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004561. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004561
>>> dataset = DS004561(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004563(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004563. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004563
>>> dataset = DS004563(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004572(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004572. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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.v1.3.1

Examples

>>> from eegdash.dataset import DS004572
>>> dataset = DS004572(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004574(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004574. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004574
>>> dataset = DS004574(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004577(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004577. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004577
>>> dataset = DS004577(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004579(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004579. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004579
>>> dataset = DS004579(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004580(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004580. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004580
>>> dataset = DS004580(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004582(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004582. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004582
>>> dataset = DS004582(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004584(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004584. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004584
>>> dataset = DS004584(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004587(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004587. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004587
>>> dataset = DS004587(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004588(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004588. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004588
>>> dataset = DS004588(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004595(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004595. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004595
>>> dataset = DS004595(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004598(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004598. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004598
>>> dataset = DS004598(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004602(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004602. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 182; recordings: 546; tasks: 3.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004602 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004602 DOI: https://doi.org/10.18112/openneuro.ds004602.v1.0.1

Examples

>>> from eegdash.dataset import DS004602
>>> dataset = DS004602(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004603(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004603. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004603
>>> dataset = DS004603(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004621(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004621. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004621
>>> dataset = DS004621(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004624(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004624. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 4; recordings: 66425; tasks: 32.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

Examples

>>> from eegdash.dataset import DS004624
>>> dataset = DS004624(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004625(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004625. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004625
>>> dataset = DS004625(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004626(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004626. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004626
>>> dataset = DS004626(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004635(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004635. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004635
>>> dataset = DS004635(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004642(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004642. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004642
>>> dataset = DS004642(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004657(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004657. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004657
>>> dataset = DS004657(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004660(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004660. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004660
>>> dataset = DS004660(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004661(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004661. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 17; recordings: 17; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004661 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004661 DOI: https://doi.org/10.18112/openneuro.ds004661.v1.1.0

Examples

>>> from eegdash.dataset import DS004661
>>> dataset = DS004661(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004696(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004696. Modality: ieeg; Experiment type: Unknown; Subject type: Single pulse electrical stimulation, limbic circuitry. Subjects: 10; recordings: 5243; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

Examples

>>> from eegdash.dataset import DS004696
>>> dataset = DS004696(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004703(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004703. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004703
>>> dataset = DS004703(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004706(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004706. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004706
>>> dataset = DS004706(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004718(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004718. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004718
>>> dataset = DS004718(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004738(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004738. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004738
>>> dataset = DS004738(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004745(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004745. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004745
>>> dataset = DS004745(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004752(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004752. Modality: eeg, ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004752
>>> dataset = DS004752(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004770(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004770. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004770
>>> dataset = DS004770(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004771(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004771. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004771
>>> dataset = DS004771(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004774(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004774. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 9; recordings: 9; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/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()

class eegdash.dataset.DS004784(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004784. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004784
>>> dataset = DS004784(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004785(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004785. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 17; recordings: 17; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004785 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004785 DOI: https://doi.org/10.18112/openneuro.ds004785.v1.0.1

Examples

>>> from eegdash.dataset import DS004785
>>> dataset = DS004785(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004789(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004789. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004789
>>> dataset = DS004789(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004796(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004796. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004796
>>> dataset = DS004796(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004802(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004802. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004802
>>> dataset = DS004802(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004809(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004809. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004809
>>> dataset = DS004809(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004816(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004816. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004816
>>> dataset = DS004816(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004817(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004817. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004817
>>> dataset = DS004817(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004819(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004819. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004819
>>> dataset = DS004819(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004830(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004830. Modality: fnirs; Experiment type: Unknown; Subject type: Spatial Attention Decoding, Auditory Neuroscience, Complex Scene Analysis, fNIRS, BCI, Machine Learning. Subjects: 13; recordings: 226; 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/ds004830 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004830 DOI: https://doi.org/10.18112/openneuro.ds004830.v1.0.1

Examples

>>> from eegdash.dataset import DS004830
>>> dataset = DS004830(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004837(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004837. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004837
>>> dataset = DS004837(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004840(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004840. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004840
>>> dataset = DS004840(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004841(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004841. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004841
>>> dataset = DS004841(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004842(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004842. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004842
>>> dataset = DS004842(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004843(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004843. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004843
>>> dataset = DS004843(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004844(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004844. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004844
>>> dataset = DS004844(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004849(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004849. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004849 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004849 DOI: https://doi.org/10.18112/openneuro.ds004849.v1.0.0

Examples

>>> from eegdash.dataset import DS004849
>>> dataset = DS004849(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004850(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004850. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004850 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004850 DOI: https://doi.org/10.18112/openneuro.ds004850.v1.0.0

Examples

>>> from eegdash.dataset import DS004850
>>> dataset = DS004850(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004851(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004851. 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

Examples

>>> from eegdash.dataset import DS004851
>>> dataset = DS004851(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004852(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004852. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004852 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004852 DOI: https://doi.org/10.18112/openneuro.ds004852.v1.0.0

Examples

>>> from eegdash.dataset import DS004852
>>> dataset = DS004852(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004853(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004853. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004853 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004853 DOI: https://doi.org/10.18112/openneuro.ds004853.v1.0.0

Examples

>>> from eegdash.dataset import DS004853
>>> dataset = DS004853(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004854(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004854. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004854 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004854 DOI: https://doi.org/10.18112/openneuro.ds004854.v1.0.0

Examples

>>> from eegdash.dataset import DS004854
>>> dataset = DS004854(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004855(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004855. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004855 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004855 DOI: https://doi.org/10.18112/openneuro.ds004855.v1.0.0

Examples

>>> from eegdash.dataset import DS004855
>>> dataset = DS004855(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004859(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004859. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 7; recordings: 9; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004859 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004859 DOI: https://doi.org/10.18112/openneuro.ds004859.v1.0.0

Examples

>>> from eegdash.dataset import DS004859
>>> dataset = DS004859(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004860(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004860. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004860
>>> dataset = DS004860(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004865(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004865. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004865
>>> dataset = DS004865(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004883(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004883. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004883
>>> dataset = DS004883(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004902(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004902. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004902
>>> dataset = DS004902(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004917(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004917. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004917
>>> dataset = DS004917(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004929(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004929. Modality: fnirs; Experiment type: Unknown; Subject type: Unknown. Subjects: 13; recordings: 233; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

class eegdash.dataset.DS004940(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004940. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS004942(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004942. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004942
>>> dataset = DS004942(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004944(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004944. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004944
>>> dataset = DS004944(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004951(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004951. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004951
>>> dataset = DS004951(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004952(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004952. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004952
>>> dataset = DS004952(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004973(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004973. Modality: fnirs; Experiment type: Unknown; Subject type: Highly automated driving  vehicles. Subjects: 21; recordings: 1177; 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()

class eegdash.dataset.DS004977(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004977. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 5; recordings: 4479; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

Examples

>>> from eegdash.dataset import DS004977
>>> dataset = DS004977(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004980(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004980. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 17; recordings: 17; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004980 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004980 DOI: https://doi.org/10.18112/openneuro.ds004980.v1.0.0

Examples

>>> from eegdash.dataset import DS004980
>>> dataset = DS004980(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004993(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004993. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004993
>>> dataset = DS004993(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004995(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004995. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004995
>>> dataset = DS004995(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS004998(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds004998. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS004998
>>> dataset = DS004998(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005007(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005007. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005007
>>> dataset = DS005007(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005021(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005021. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005021
>>> dataset = DS005021(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005028(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005028. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 11; recordings: 105; tasks: 3.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005028 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005028 DOI: https://doi.org/10.18112/openneuro.ds005028.v1.0.0

Examples

>>> from eegdash.dataset import DS005028
>>> dataset = DS005028(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005034(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005034. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005034
>>> dataset = DS005034(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005048(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005048. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005048
>>> dataset = DS005048(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005059(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005059. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005059
>>> dataset = DS005059(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005065(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005065. Modality: meg; Experiment type: Unknown; Subject type: Unknown. Subjects: 22; recordings: 3397; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

Examples

>>> from eegdash.dataset import DS005065
>>> dataset = DS005065(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005079(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005079. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005079
>>> dataset = DS005079(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005083(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005083. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005087(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005087. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005087
>>> dataset = DS005087(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005089(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005089. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005089
>>> dataset = DS005089(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005095(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005095. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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.v1.0.1

Examples

>>> from eegdash.dataset import DS005095
>>> dataset = DS005095(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005106(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005106. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005106
>>> dataset = DS005106(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005107(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005107. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005107
>>> dataset = DS005107(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005114(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005114. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005114
>>> dataset = DS005114(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005121(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005121. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005121
>>> dataset = DS005121(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005131(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005131. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005131
>>> dataset = DS005131(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005169(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005169. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005169
>>> dataset = DS005169(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005170(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005170. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005170
>>> dataset = DS005170(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005178(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005178. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005185(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005185. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005189(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005189. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005189
>>> dataset = DS005189(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005207(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005207. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005207
>>> dataset = DS005207(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005241(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005241. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005241
>>> dataset = DS005241(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005261(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005261. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005261
>>> dataset = DS005261(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005262(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005262. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005262
>>> dataset = DS005262(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005273(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005273. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005273
>>> dataset = DS005273(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005274(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005274. 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

Examples

>>> from eegdash.dataset import DS005274
>>> dataset = DS005274(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005279(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005279. Modality: meg; Experiment type: Unknown; Subject type: Neurolingusitics. Subjects: 31; recordings: 1632; tasks: 54.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

Examples

>>> from eegdash.dataset import DS005279
>>> dataset = DS005279(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005280(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005280. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005284(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005284. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005285(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005285. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005286(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005286. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005289(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005289. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 39; recordings: 195; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005289 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005289 DOI: https://doi.org/10.18112/openneuro.ds005289.v1.0.0

Examples

>>> from eegdash.dataset import DS005289
>>> dataset = DS005289(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005291(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005291. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005292(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005292. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005293(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005293. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005296(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005296. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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.v1.0.0

Examples

>>> from eegdash.dataset import DS005296
>>> dataset = DS005296(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005305(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005305. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005305
>>> dataset = DS005305(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005307(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005307. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005307
>>> dataset = DS005307(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005340(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005340. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005340
>>> dataset = DS005340(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005342(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005342. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005342
>>> dataset = DS005342(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005343(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005343. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005345(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005345. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005346(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005346. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005356(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005356. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005363(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005363. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005363
>>> dataset = DS005363(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005383(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005383. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005383
>>> dataset = DS005383(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005385(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005385. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005385
>>> dataset = DS005385(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005397(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005397. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005397
>>> dataset = DS005397(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005398(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005398. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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.v1.0.1

Examples

>>> from eegdash.dataset import DS005398
>>> dataset = DS005398(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005403(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005403. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005403
>>> dataset = DS005403(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005406(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005406. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005406
>>> dataset = DS005406(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005407(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005407. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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.v1.0.0

Examples

>>> from eegdash.dataset import DS005407
>>> dataset = DS005407(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

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

Bases: EEGDashDataset

OpenNeuro dataset ds005408. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 25; recordings: 29; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005408 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005408 DOI: https://doi.org/10.18112/openneuro.ds005408.v1.0.0

Examples

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

class eegdash.dataset.DS005410(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005410. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005410
>>> dataset = DS005410(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005411(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005411. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005411
>>> dataset = DS005411(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005415(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005415. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005415
>>> dataset = DS005415(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005416(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005416. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005416
>>> dataset = DS005416(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005420(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005420. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005420
>>> dataset = DS005420(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005429(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005429. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005429
>>> dataset = DS005429(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005448(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005448. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005473(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005473. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005486(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005486. 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

Examples

>>> from eegdash.dataset import DS005486
>>> dataset = DS005486(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005489(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005489. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 37; recordings: 154; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005489 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005489 DOI: https://doi.org/10.18112/openneuro.ds005489.v1.0.3

Examples

>>> from eegdash.dataset import DS005489
>>> dataset = DS005489(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005491(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005491. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 19; recordings: 51; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005491 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005491 DOI: https://doi.org/10.18112/openneuro.ds005491.v1.0.0

Examples

>>> from eegdash.dataset import DS005491
>>> dataset = DS005491(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005494(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005494. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 20; recordings: 51; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005494 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005494 DOI: https://doi.org/10.18112/openneuro.ds005494.v1.0.1

Examples

>>> from eegdash.dataset import DS005494
>>> dataset = DS005494(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005505(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005505. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005505
>>> dataset = DS005505(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005506(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005506. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005506
>>> dataset = DS005506(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005507(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005507. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005507
>>> dataset = DS005507(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005508(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005508. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005508
>>> dataset = DS005508(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005509(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005509. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005509
>>> dataset = DS005509(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005510(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005510. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005510
>>> dataset = DS005510(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005512(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005512. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005512
>>> dataset = DS005512(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005514(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005514. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005514
>>> dataset = DS005514(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005515(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005515. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005516(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005516. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005520(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005520. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005520
>>> dataset = DS005520(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005522(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005522. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 55; recordings: 176; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005522 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005522 DOI: https://doi.org/10.18112/openneuro.ds005522.v1.0.0

Examples

>>> from eegdash.dataset import DS005522
>>> dataset = DS005522(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005523(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005523. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005523
>>> dataset = DS005523(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005530(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005530. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005530
>>> dataset = DS005530(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005540(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005540. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005540
>>> dataset = DS005540(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005545(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005545. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005545
>>> dataset = DS005545(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005555(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005555. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005555
>>> dataset = DS005555(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005557(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005557. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005557
>>> dataset = DS005557(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005558(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005558. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005558
>>> dataset = DS005558(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005565(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005565. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005565
>>> dataset = DS005565(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005571(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005571. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005571
>>> dataset = DS005571(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005574(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005574. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 9; recordings: 9; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005574 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005574 DOI: https://doi.org/10.18112/openneuro.ds005574.v1.0.2

Examples

>>> from eegdash.dataset import DS005574
>>> dataset = DS005574(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005586(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005586. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005586
>>> dataset = DS005586(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005594(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005594. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005594
>>> dataset = DS005594(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005620(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005620. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005620
>>> dataset = DS005620(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005624(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005624. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 24; recordings: 35; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005624 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005624 DOI: https://doi.org/10.18112/openneuro.ds005624.v1.0.0

Examples

>>> from eegdash.dataset import DS005624
>>> dataset = DS005624(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005628(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005628. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005642(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005642. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005648(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005648. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005648 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005648 DOI: https://doi.org/10.18112/openneuro.ds005648.v1.0.0

Examples

>>> from eegdash.dataset import DS005648
>>> dataset = DS005648(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005662(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005662. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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.v2.0.0

Examples

>>> from eegdash.dataset import DS005662
>>> dataset = DS005662(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005670(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005670. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005670
>>> dataset = DS005670(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005672(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005672. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005672
>>> dataset = DS005672(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005688(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005688. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005688
>>> dataset = DS005688(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005691(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005691. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005691
>>> dataset = DS005691(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005692(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005692. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005692
>>> dataset = DS005692(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005697(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005697. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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

Examples

>>> from eegdash.dataset import DS005697
>>> dataset = DS005697(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005752(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005752. Modality: meg; Experiment type: Unknown; Subject type: Unknown. Subjects: 253; recordings: 22385; 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/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()

class eegdash.dataset.DS005776(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005776. Modality: fnirs; Experiment type: Unknown; Subject type: Sensory Neuroscience. Subjects: 12; recordings: 293; 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()

class eegdash.dataset.DS005777(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005777. Modality: fnirs; Experiment type: Unknown; Subject type: Sensory Neuroscience. Subjects: 15; recordings: 698; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

class eegdash.dataset.DS005779(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005779. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005787(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005787. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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/ds005787 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005787 DOI: https://doi.org/10.18112/openneuro.ds005787.v1.0.0

Examples

>>> from eegdash.dataset import DS005787
>>> dataset = DS005787(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005795(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005795. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005810(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005810. Modality: meg; Experiment type: Unknown; Subject type: Unknown. Subjects: 31; recordings: 286; 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.v1.0.6

Examples

>>> from eegdash.dataset import DS005810
>>> dataset = DS005810(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005811(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005811. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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.v1.0.8

Examples

>>> from eegdash.dataset import DS005811
>>> dataset = DS005811(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005815(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005815. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005841(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005841. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005857(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005857. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 29; recordings: 110; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005857 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005857 DOI: https://doi.org/10.18112/openneuro.ds005857.v1.0.0

Examples

>>> from eegdash.dataset import DS005857
>>> dataset = DS005857(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005863(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005863. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005866(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005866. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005868(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005868. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005872(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005872. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/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()

class eegdash.dataset.DS005873(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005873. Modality: eeg, emg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005876(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005876. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005907(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005907. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005929(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005929. Modality: fnirs; Experiment type: Unknown; Subject type: Unknown. Subjects: 8; recordings: 77; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

class eegdash.dataset.DS005930(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005930. Modality: fnirs; Experiment type: Unknown; Subject type: Motor. Subjects: 13; recordings: 233; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

class eegdash.dataset.DS005931(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005931. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005932(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005932. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005935(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005935. Modality: fnirs; Experiment type: Unknown; Subject type: Mirror Neuron System. Subjects: 22; recordings: 430; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

class eegdash.dataset.DS005946(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005946. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005953(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005953. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005960(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005960. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS005963(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005963. Modality: fnirs; Experiment type: Unknown; Subject type: Unknown. Subjects: 11; recordings: 291; 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/ds005963 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005963

Examples

>>> from eegdash.dataset import DS005963
>>> dataset = DS005963(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS005964(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds005964. Modality: fnirs; Experiment type: Unknown; Subject type: Unknown. Subjects: 18; 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/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()

class eegdash.dataset.DS006012(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006012. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006018(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006018. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006033(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006033. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006035(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006035. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006036(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006036. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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.v1.0.5

Examples

>>> from eegdash.dataset import DS006036
>>> dataset = DS006036(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS006040(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006040. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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.v1.0.1

Examples

>>> from eegdash.dataset import DS006040
>>> dataset = DS006040(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS006065(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006065. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006095(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006095. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006104(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006104. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006107(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006107. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 166; recordings: 167; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006107 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006107 DOI: https://doi.org/10.18112/openneuro.ds006107.v1.0.0

Examples

>>> from eegdash.dataset import DS006107
>>> dataset = DS006107(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS006126(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006126. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006142(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006142. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006159(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006159. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 62; recordings: 671; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

class eegdash.dataset.DS006171(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006171. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006233(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006233. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006234(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006234. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006253(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006253. Modality: ieeg; Experiment type: Unknown; Subject type: Decision-Making, Metacognition. 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()

class eegdash.dataset.DS006260(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006260. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006269(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006269. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006317(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006317. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 2; recordings: 64; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006317 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006317 DOI: https://doi.org/10.18112/openneuro.ds006317.v1.1.0

Examples

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

class eegdash.dataset.DS006334(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006334. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006366(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006366. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006367(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006367. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006370(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006370. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006374(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006374. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006377(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006377. Modality: fnirs; Experiment type: Unknown; Subject type: fNIRS. Subjects: 116; recordings: 3952; 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()

class eegdash.dataset.DS006392(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006392. Modality: ieeg; Experiment type: Unknown; Subject type: vision. Subjects: 2; recordings: 595; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

class eegdash.dataset.DS006394(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006394. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006434(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006434. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006437(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006437. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006446(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006446. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006459(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006459. Modality: fnirs; Experiment type: Unknown; Subject type: Unknown. Subjects: 18; 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/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()

class eegdash.dataset.DS006460(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006460. Modality: fnirs; Experiment type: Unknown; Subject type: Unknown. Subjects: 18; recordings: 176; 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/ds006460 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006460

Examples

>>> from eegdash.dataset import DS006460
>>> dataset = DS006460(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS006465(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006465. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006466(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006466. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006468(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006468. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006480(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006480. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006502(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006502. Modality: meg; Experiment type: Unknown; Subject type: Motor skill learning and consolidation. Subjects: 32; recordings: 4758; 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()

class eegdash.dataset.DS006519(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006519. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006525(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006525. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 34; recordings: 34; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/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()

class eegdash.dataset.DS006545(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006545. Modality: fnirs; Experiment type: Unknown; Subject type: Unknown. Subjects: 50; recordings: 838; 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/ds006545 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006545

Examples

>>> from eegdash.dataset import DS006545
>>> dataset = DS006545(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS006547(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006547. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006554(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006554. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 47; recordings: 47; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006554 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006554 DOI: https://doi.org/10.18112/openneuro.ds006554.v1.0.0

Examples

>>> from eegdash.dataset import DS006554
>>> dataset = DS006554(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS006563(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006563. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006576(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006576. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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/ds006576 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006576 DOI: https://doi.org/10.18112/openneuro.ds006576.v1.0.2

Examples

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

class eegdash.dataset.DS006593(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006593. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006629(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006629. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006647(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006647. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006648(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006648. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 47; recordings: 47; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/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()

class eegdash.dataset.DS006673(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006673. Modality: fnirs; Experiment type: Unknown; Subject type: Unknown. Subjects: 18; recordings: 1556; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006673 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006673 DOI: https://doi.org/10.18112/openneuro.ds006673.v1.0.2

Examples

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

class eegdash.dataset.DS006695(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006695. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006720(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006720. Modality: meg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006735(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006735. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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.v1.0.4

Examples

>>> from eegdash.dataset import DS006735
>>> dataset = DS006735(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS006761(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006761. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006768(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006768. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006801(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006801. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006802(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006802. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006803(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006803. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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.v1.0.0

Examples

>>> from eegdash.dataset import DS006803
>>> dataset = DS006803(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS006817(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006817. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 34; recordings: 34; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006817 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006817 DOI: https://doi.org/10.18112/openneuro.ds006817.v1.0.0

Examples

>>> from eegdash.dataset import DS006817
>>> dataset = DS006817(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS006839(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006839. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006840(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006840. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006848(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006848. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006850(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006850. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006861(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006861. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006866(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006866. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006890(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006890. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006902(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006902. Modality: fnirs; Experiment type: Unknown; Subject type: pain, exercise. Subjects: 43; recordings: 259; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

class eegdash.dataset.DS006903(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006903. Modality: fnirs; Experiment type: Unknown; Subject type: Unknown. Subjects: 18; recordings: 409; 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()

class eegdash.dataset.DS006910(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006910. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 121; recordings: 384; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006910 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006910 DOI: https://doi.org/10.18112/openneuro.ds006910.v1.0.1

Examples

>>> from eegdash.dataset import DS006910
>>> dataset = DS006910(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS006914(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006914. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006921(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006921. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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.v1.0.0

Examples

>>> from eegdash.dataset import DS006921
>>> dataset = DS006921(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS006923(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006923. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006940(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006940. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS006945(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006945. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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.v1.0.0

Examples

>>> from eegdash.dataset import DS006945
>>> dataset = DS006945(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.DS006963(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds006963. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 32; recordings: 32; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006963 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006963 DOI: https://doi.org/10.18112/openneuro.ds006963.v1.0.0

Examples

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

class eegdash.dataset.DS007006(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds007006. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS007020(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds007020. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS007081(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds007081. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS007095(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds007095. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS007172(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds007172. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS007175(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds007175. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. 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()

class eegdash.dataset.DS007176(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset ds007176. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 45; recordings: 300; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007176 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007176 DOI: https://doi.org/10.18112/openneuro.ds007176.v1.0.1

Examples

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

class eegdash.dataset.EEG2025R1(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r1. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 136; recordings: 1342; tasks: 10.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r1 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r1 DOI: https://doi.org/10.18112/openneuro.ds005505.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R1
>>> dataset = EEG2025R1(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.EEG2025R10(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r10. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 533; recordings: 2516; tasks: 8.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r10 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r10

Examples

>>> from eegdash.dataset import EEG2025R10
>>> dataset = EEG2025R10(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.EEG2025R10MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r10mini. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 20; recordings: 220; tasks: 8.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r10mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r10mini

Examples

>>> from eegdash.dataset import EEG2025R10MINI
>>> dataset = EEG2025R10MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.EEG2025R11(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r11. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 430; recordings: 3397; tasks: 8.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r11 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r11

Examples

>>> from eegdash.dataset import EEG2025R11
>>> dataset = EEG2025R11(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.EEG2025R11MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r11mini. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 20; recordings: 220; tasks: 8.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r11mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r11mini

Examples

>>> from eegdash.dataset import EEG2025R11MINI
>>> dataset = EEG2025R11MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.EEG2025R1MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r1mini. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 20; recordings: 239; tasks: 10.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r1mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r1mini DOI: https://doi.org/10.18112/openneuro.ds005505.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R1MINI
>>> dataset = EEG2025R1MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.EEG2025R2(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r2. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 150; recordings: 1405; tasks: 10.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r2 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r2 DOI: https://doi.org/10.18112/openneuro.ds005506.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R2
>>> dataset = EEG2025R2(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.EEG2025R2MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r2mini. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 20; recordings: 240; tasks: 10.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r2mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r2mini DOI: https://doi.org/10.18112/openneuro.ds005506.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R2MINI
>>> dataset = EEG2025R2MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.EEG2025R3(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r3. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 184; recordings: 1812; tasks: 10.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r3 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r3 DOI: https://doi.org/10.18112/openneuro.ds005507.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R3
>>> dataset = EEG2025R3(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.EEG2025R3MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r3mini. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 20; recordings: 240; tasks: 10.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r3mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r3mini DOI: https://doi.org/10.18112/openneuro.ds005507.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R3MINI
>>> dataset = EEG2025R3MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.EEG2025R4(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r4. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 324; recordings: 3342; tasks: 10.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r4 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r4 DOI: https://doi.org/10.18112/openneuro.ds005508.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R4
>>> dataset = EEG2025R4(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.EEG2025R4MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r4mini. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 20; recordings: 240; tasks: 10.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r4mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r4mini DOI: https://doi.org/10.18112/openneuro.ds005508.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R4MINI
>>> dataset = EEG2025R4MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.EEG2025R5(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r5. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 330; recordings: 3326; tasks: 10.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r5 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r5 DOI: https://doi.org/10.18112/openneuro.ds005509.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R5
>>> dataset = EEG2025R5(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.EEG2025R5MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r5mini. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 20; recordings: 240; tasks: 10.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r5mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r5mini DOI: https://doi.org/10.18112/openneuro.ds005509.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R5MINI
>>> dataset = EEG2025R5MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.EEG2025R6(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r6. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 135; recordings: 1227; tasks: 10.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r6 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r6 DOI: https://doi.org/10.18112/openneuro.ds005510.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R6
>>> dataset = EEG2025R6(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.EEG2025R6MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r6mini. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 20; recordings: 237; tasks: 10.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r6mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r6mini DOI: https://doi.org/10.18112/openneuro.ds005510.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R6MINI
>>> dataset = EEG2025R6MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.EEG2025R7(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r7. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 381; recordings: 3100; tasks: 10.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r7 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r7 DOI: https://doi.org/10.18112/openneuro.ds005511.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R7
>>> dataset = EEG2025R7(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.EEG2025R7MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r7mini. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 20; recordings: 239; tasks: 10.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r7mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r7mini DOI: https://doi.org/10.18112/openneuro.ds005511.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R7MINI
>>> dataset = EEG2025R7MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.EEG2025R8(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r8. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 257; recordings: 2320; tasks: 10.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r8 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r8 DOI: https://doi.org/10.18112/openneuro.ds005512.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R8
>>> dataset = EEG2025R8(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.EEG2025R8MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r8mini. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 20; recordings: 238; tasks: 10.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r8mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r8mini DOI: https://doi.org/10.18112/openneuro.ds005512.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R8MINI
>>> dataset = EEG2025R8MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.EEG2025R9(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r9. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 295; recordings: 2885; tasks: 10.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r9 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r9 DOI: https://doi.org/10.18112/openneuro.ds005514.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R9
>>> dataset = EEG2025R9(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.EEG2025R9MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r9mini. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 20; recordings: 237; tasks: 10.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r9mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r9mini DOI: https://doi.org/10.18112/openneuro.ds005514.v1.0.1

Examples

>>> from eegdash.dataset import EEG2025R9MINI
>>> dataset = EEG2025R9MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.EEGBIDSDataset(data_dir=None, dataset='', allow_symlinks=False, modalities=None)

Bases: object

An interface to a local BIDS dataset containing electrophysiology recordings.

This class centralizes interactions with a BIDS dataset on the local filesystem, providing methods to parse metadata, find files, and retrieve BIDS-related information. Supports multiple modalities including EEG, MEG, iEEG, and NIRS.

The class uses MNE-BIDS constants to stay synchronized with the BIDS specification and automatically supports all file formats recognized by MNE.

Parameters:

• data_dir (str or Path) – The path to the local BIDS dataset directory.

• dataset (str) – A name for the dataset (e.g., “ds002718”).

• allow_symlinks (bool, default False) – If True, accept broken symlinks (e.g., git-annex) for metadata extraction. If False, require actual readable files for data loading. Set to True when doing metadata digestion without loading raw data.

• modalities (list of str or None, default None) – List of modalities to search for (e.g., [“eeg”, “meg”]). If None, defaults to all electrophysiology modalities from MNE-BIDS: [‘meg’, ‘eeg’, ‘ieeg’, ‘nirs’].

RAW_EXTENSIONS

Mapping of file extensions to their companion files, dynamically built from mne_bids.config.reader.

Type:

dict

files

List of all recording file paths found in the dataset.

Type:

list of str

detected_modality

The modality of the first file found (e.g., ‘eeg’, ‘meg’).

Type:

str

Examples

>>> # Load EEG-only dataset
>>> dataset = EEGBIDSDataset(
...     data_dir="/path/to/ds002718",
...     dataset="ds002718",
...     modalities=["eeg"]
... )

>>> # Load dataset with multiple modalities
>>> dataset = EEGBIDSDataset(
...     data_dir="/path/to/ds005810",
...     dataset="ds005810",
...     modalities=["meg", "eeg"]
... )

>>> # Metadata extraction from git-annex (symlinks)
>>> dataset = EEGBIDSDataset(
...     data_dir="/path/to/dataset",
...     dataset="ds000001",
...     allow_symlinks=True
... )

RAW_EXTENSIONS = {'.CNT': ['.CNT'], '.EDF': ['.EDF'], '.EEG': ['.EEG'], '.bdf': ['.bdf'], '.bin': ['.bin'], '.cdt': ['.cdt'], '.cnt': ['.cnt'], '.con': ['.con'], '.ds': ['.ds'], '.edf': ['.edf'], '.fif': ['.fif'], '.lay': ['.lay'], '.pdf': ['.pdf'], '.set': ['.set', '.fdt'], '.snirf': ['.snirf'], '.sqd': ['.sqd'], '.vhdr': ['.vhdr', '.eeg', '.vmrk']}

channel_labels(data_filepath: str) → list[str]

Get a list of channel labels from channels.tsv.

Parameters:

data_filepath (str) – The path to the data file.

Returns:

A list of channel names.

Return type:

list of str

channel_types(data_filepath: str) → list[str]

Get a list of channel types from channels.tsv.

Parameters:

data_filepath (str) – The path to the data file.

Returns:

A list of channel types.

Return type:

list of str

check_eeg_dataset() → bool

Check if the BIDS dataset contains EEG data.

Returns:

True if the dataset’s modality is EEG, False otherwise.

Return type:

bool

eeg_json(data_filepath: str) → dict[str, Any]

Get the merged eeg.json metadata for a data file.

Parameters:

data_filepath (str) – The path to the data file.

Returns:

The merged eeg.json metadata.

Return type:

dict

get_bids_file_attribute(attribute: str, data_filepath: str) → Any

Retrieve a specific attribute from BIDS metadata.

Parameters:

• attribute (str) – The name of the attribute to retrieve (e.g., “sfreq”, “subject”).

• data_filepath (str) – The path to the data file.

Returns:

The value of the requested attribute, or None if not found.

Return type:

Any

get_bids_metadata_files(filepath: str | Path, metadata_file_extension: str) → list[Path]

Retrieve all metadata files that apply to a given data file.

Follows the BIDS inheritance principle to find all relevant metadata files (e.g., channels.tsv, eeg.json) for a specific recording.

Parameters:

• filepath (str or Path) – The path to the data file.

• metadata_file_extension (str) – The extension of the metadata file to search for (e.g., “channels.tsv”).

Returns:

A list of paths to the matching metadata files.

Return type:

list of Path

get_files() → list[str]

Get all EEG recording file paths in the BIDS dataset.

Returns:

A list of file paths for all valid EEG recordings.

Return type:

list of str

get_relative_bidspath(filepath: str | Path) → str

Get the dataset-relative path for a file.

Parameters:

filepath (str or Path) – The absolute or relative path to a file in the BIDS dataset.

Returns:

The path relative to the dataset root, prefixed with the dataset name. e.g., “ds004477/sub-001/eeg/sub-001_task-PES_eeg.json”

Return type:

str

num_times(data_filepath: str) → int

Get the number of time points in the recording.

Calculated from SamplingFrequency and RecordingDuration in eeg.json.

Parameters:

data_filepath (str) – The path to the data file.

Returns:

The approximate number of time points.

Return type:

int

subject_participant_tsv(data_filepath: str) → dict[str, Any]

Get the participants.tsv record for a subject.

Parameters:

data_filepath (str) – The path to a data file belonging to the subject.

Returns:

A dictionary of the subject’s information from participants.tsv.

Return type:

dict

class eegdash.dataset.EEGChallengeDataset(release: str, cache_dir: str, mini: bool = True, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

A dataset helper for the EEG 2025 Challenge.

This class simplifies access to the EEG 2025 Challenge datasets. It is a specialized version of EEGDashDataset that is pre-configured for the challenge’s data releases. It automatically maps a release name (e.g., “R1”) to the corresponding OpenNeuro dataset and handles the selection of subject subsets (e.g., “mini” release).

Parameters:

• release (str) – The name of the challenge release to load. Must be one of the keys in RELEASE_TO_OPENNEURO_DATASET_MAP (e.g., “R1”, “R2”, …, “R11”).

• cache_dir (str) – The local directory where the dataset will be downloaded and cached.

• mini (bool, default True) – If True, the dataset is restricted to the official “mini” subset of subjects for the specified release. If False, all subjects for the release are included.

• query (dict, optional) – An additional MongoDB-style query to apply as a filter. This query is combined with the release and subject filters using a logical AND. The query must not contain the dataset key, as this is determined by the release parameter.

• s3_bucket (str, optional) – The base S3 bucket URI where the challenge data is stored. Defaults to the official challenge bucket.

• **kwargs – Additional keyword arguments that are passed directly to the EEGDashDataset constructor.

Raises:

ValueError – If the specified release is unknown, or if the query argument contains a dataset key. Also raised if mini is True and a requested subject is not part of the official mini-release subset.

See also

EEGDashDataset

The base class for creating datasets from queries.

class eegdash.dataset.EEGDashDataset(cache_dir: str | Path, query: dict[str, Any] = None, description_fields: list[str] | None = None, s3_bucket: str | None = None, records: list[dict] | None = None, download: bool = True, n_jobs: int = -1, eeg_dash_instance: Any = None, database: str | None = None, auth_token: str | None = None, **kwargs)

Bases: BaseConcatDataset

Create a new EEGDashDataset from a given query or local BIDS dataset directory and dataset name. An EEGDashDataset is pooled collection of EEGDashBaseDataset instances (individual recordings) and is a subclass of braindecode’s BaseConcatDataset.

Examples

Basic usage with dataset and subject filtering:

>>> from eegdash import EEGDashDataset
>>> dataset = EEGDashDataset(
...     cache_dir="./data",
...     dataset="ds002718",
...     subject="012"
... )
>>> print(f"Number of recordings: {len(dataset)}")

Filter by multiple subjects and specific task:

>>> subjects = ["012", "013", "014"]
>>> dataset = EEGDashDataset(
...     cache_dir="./data",
...     dataset="ds002718",
...     subject=subjects,
...     task="RestingState"
... )

Load and inspect EEG data from recordings:

>>> if len(dataset) > 0:
...     recording = dataset[0]
...     raw = recording.load()
...     print(f"Sampling rate: {raw.info['sfreq']} Hz")
...     print(f"Number of channels: {len(raw.ch_names)}")
...     print(f"Duration: {raw.times[-1]:.1f} seconds")

Advanced filtering with raw MongoDB queries:

>>> from eegdash import EEGDashDataset
>>> query = {
...     "dataset": "ds002718",
...     "subject": {"$in": ["012", "013"]},
...     "task": "RestingState"
... }
>>> dataset = EEGDashDataset(cache_dir="./data", query=query)

Working with dataset collections and braindecode integration:

>>> # EEGDashDataset is a braindecode BaseConcatDataset
>>> for i, recording in enumerate(dataset):
...     if i >= 2:  # limit output
...         break
...     print(f"Recording {i}: {recording.description}")
...     raw = recording.load()
...     print(f"  Channels: {len(raw.ch_names)}, Duration: {raw.times[-1]:.1f}s")

Parameters:

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

• query (dict | None) – Raw MongoDB query to filter records. If provided, it is merged with keyword filtering arguments (see **kwargs) using logical AND. You must provide at least a dataset (either in query or as a keyword argument). Only fields in ALLOWED_QUERY_FIELDS are considered for filtering.

• dataset (str) – Dataset identifier (e.g., "ds002718"). Required if query does not already specify a dataset.

• task (str | list[str]) – Task name(s) to filter by (e.g., "RestingState").

• subject (str | list[str]) – Subject identifier(s) to filter by (e.g., "NDARCA153NKE").

• session (str | list[str]) – Session identifier(s) to filter by (e.g., "1").

• run (str | list[str]) – Run identifier(s) to filter by (e.g., "1").

• description_fields (list[str]) – Fields to extract from each record and include in dataset descriptions (e.g., “subject”, “session”, “run”, “task”).

• s3_bucket (str | None) – Optional S3 bucket URI (e.g., “s3://mybucket”) to use instead of the default OpenNeuro bucket when downloading data files.

• records (list[dict] | None) – Pre-fetched metadata records. If provided, the dataset is constructed directly from these records and no MongoDB query is performed.

• download (bool, default True) – If False, load from local BIDS files only. Local data are expected under cache_dir / dataset; no DB or S3 access is attempted.

• n_jobs (int) – Number of parallel jobs to use where applicable (-1 uses all cores).

• eeg_dash_instance (EEGDash | None) – Optional existing EEGDash client to reuse for DB queries. If None, a new client is created on demand, not used in the case of no download.

• database (str | None) – Database name to use (e.g., “eegdash”, “eegdash_staging”). If None, uses the default database.

• auth_token (str | None) – Authentication token for accessing protected databases. Required for staging or admin operations.

• **kwargs (dict) –

  Additional keyword arguments serving two purposes:

  • Filtering: any keys present in ALLOWED_QUERY_FIELDS are treated as query filters (e.g., dataset, subject, task, …).

  • Dataset options: remaining keys are forwarded to EEGDashRaw.

datasets: list[T]

download_all(n_jobs: int | None = None) → None

Download missing remote files in parallel.

Parameters:

n_jobs (int | None) – Number of parallel workers to use. If None, defaults to self.n_jobs.

save(path, overwrite=False)

Save the dataset to disk.

Parameters:

• path (str or Path) – Destination file path.

• overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

class eegdash.dataset.NM000103(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset nm000103. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 447; recordings: 3522; tasks: 10.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000103 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000103 DOI: https://doi.org/10.5281/zenodo.17306881

Examples

>>> from eegdash.dataset import NM000103
>>> dataset = NM000103(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.NM000104(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset nm000104. Modality: emg; Experiment type: Unknown; Subject type: Unknown. Subjects: 108; recordings: 1135; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000104 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000104 DOI: https://doi.org/10.5281/zenodo.17287903

Examples

>>> from eegdash.dataset import NM000104
>>> dataset = NM000104(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.NM000105(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset nm000105. Modality: emg; Experiment type: Unknown; Subject type: Unknown. Subjects: 100; recordings: 100; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000105 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000105 DOI: https://doi.org/10.5281/zenodo.17283593

Examples

>>> from eegdash.dataset import NM000105
>>> dataset = NM000105(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.NM000106(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset nm000106. Modality: emg; Experiment type: Unknown; Subject type: Unknown. Subjects: 100; recordings: 807; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000106 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000106 DOI: https://doi.org/10.5281/zenodo.17283865

Examples

>>> from eegdash.dataset import NM000106
>>> dataset = NM000106(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

class eegdash.dataset.NM000107(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

OpenNeuro dataset nm000107. Modality: emg; Experiment type: Unknown; Subject type: Unknown. Subjects: 100; recordings: 182; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000107 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000107 DOI: https://doi.org/10.5281/zenodo.17282507

Examples

>>> from eegdash.dataset import NM000107
>>> dataset = NM000107(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

eegdash.dataset.register_openneuro_datasets(summary_file: str | Path | None = None, *, base_class=None, namespace: Dict[str, Any] | None = None, add_to_all: bool = True, from_api: bool = False, api_url: str = 'https://data.eegdash.org/api', database: str = 'eegdash') → Dict[str, type]

Dynamically create and register dataset classes from a summary file or API.

This function reads a CSV file or queries the API containing summaries of datasets and dynamically creates a Python class for each dataset. These classes inherit from a specified base class and are pre-configured with the dataset’s ID.

Parameters:

• summary_file (str or pathlib.Path) – The path to the CSV file containing the dataset summaries.

• base_class (type, optional) – The base class from which the new dataset classes will inherit. If not provided, eegdash.api.EEGDashDataset is used.

• namespace (dict, optional) – The namespace (e.g., globals()) into which the newly created classes will be injected. Defaults to the local globals() of this module.

• add_to_all (bool, default True) – If True, the names of the newly created classes are added to the __all__ list of the target namespace, making them importable with from … import *.

Returns:

A dictionary mapping the names of the registered classes to the class types themselves.

Return type:

dict[str, type]