eegdash.dataset package#

Submodules#

Module contents#

Public API for dataset helpers and dynamically generated datasets.

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

Bases: EEGDashDataset

OpenNeuro dataset ds000117. Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 17; recordings: 104; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds000117 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds000117

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds000246. Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 2; recordings: 3; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds000246 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds000246

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds000247. Modality: meg; Experiment type: Resting-state; Subject type: Healthy. Subjects: 6; recordings: 10; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds000247 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds000247

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds000248. Modality: meg; Experiment type: Attention; Subject type: Healthy. Subjects: 2; recordings: 3; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds000248 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds000248

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds001785. Modality: eeg; Experiment type: Perception; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds001787. Modality: eeg; Experiment type: Attention; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds001810. Modality: eeg; Experiment type: Attention; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds001849. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds001971. Modality: eeg; Experiment type: Motor; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002001. Modality: meg; Experiment type: Unknown; Subject type: Unknown. Subjects: 11; recordings: 69; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002001 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002001

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002034. Modality: eeg; Experiment type: Attention; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002094. Modality: eeg; Experiment type: Resting state. 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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002158. Modality: eeg; Experiment type: Affect; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002181. Modality: eeg; Experiment type: Resting-state; Subject type: Development. Subjects: 226; recordings: 226; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002181 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002181

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002218. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 18; recordings: 18; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002218 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002218

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002336. Modality: eeg; Experiment type: Motor; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002338. Modality: eeg; Experiment type: Motor; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002550. Modality: meg; Experiment type: Memory; Subject type: Healthy. Subjects: 22; recordings: 377; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002550 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002550

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002578. Modality: eeg; Experiment type: Attention; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002680. Modality: eeg; Experiment type: Motor; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002691. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 20; recordings: 20; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002691 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002691

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002712. Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 25; recordings: 82; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002712 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002712

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002718. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 18; recordings: 18; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002718 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002718

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002720. Modality: eeg; Experiment type: Affect; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002721. Modality: eeg; Experiment type: Affect; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002722. Modality: eeg; Experiment type: Affect; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002723. Modality: eeg; Experiment type: Affect; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002724. Modality: eeg; Experiment type: Affect; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002725. Modality: eeg; Experiment type: Affect; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002761. Modality: meg; Experiment type: Memory; Subject type: Healthy. Subjects: 25; recordings: 249; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002761 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002761

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002778. Modality: eeg; Experiment type: Resting state; Subject type: Parkinson's. 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

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002799. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 27; recordings: 16824; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002799 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002799

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002814. Modality: eeg; Experiment type: Perception; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002833. Modality: eeg; Experiment type: Decision-making. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002885. Modality: meg; Experiment type: Other; Subject type: Other. Subjects: 2; recordings: 7; tasks: 4.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002885 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002885

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002893. Modality: eeg; Experiment type: Attention; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds002908. Modality: meg; Experiment type: Unknown; Subject type: Unknown. Subjects: 13; recordings: 53; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds002908 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002908

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)[source]

Bases: EEGDashDataset

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

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003004 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003004

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003029. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 35; recordings: 106; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003029 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003029

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003039. Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 19; recordings: 19; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003039 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003039

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003061. Modality: eeg; Experiment type: Perception. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003078. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Surgery. Subjects: 6; recordings: 72; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003078 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003078

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003082. 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/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)[source]

Bases: EEGDashDataset

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

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003104 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003104

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003190. Modality: eeg; Experiment type: Perception. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003194. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Parkinson's. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003195. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Parkinson's. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003343. Modality: eeg; Experiment type: Perception; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003352. Modality: meg; Experiment type: Perception; 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003374. Modality: ieeg; Experiment type: Affect; Subject type: Epilepsy. Subjects: 9; recordings: 18; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003374 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003374

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003392. Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 12; recordings: 33; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003392 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003392

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003420. Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 23; recordings: 92; tasks: 0.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003420 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003420

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003421. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 20; recordings: 80; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003421 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003421

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003458. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 23; recordings: 23; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003458 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003458

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003474. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003478. Modality: eeg; Experiment type: Resting state; Subject type: Healthy. 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

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003490. Modality: eeg; Experiment type: Attention; Subject type: Parkinson's. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003498. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 20; recordings: 385; tasks: 0.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003498 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003498

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003505. Modality: eeg; Experiment type: Perception; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003506. Modality: eeg; Experiment type: Decision-making; Subject type: Parkinson's. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003509. Modality: eeg; Experiment type: Learning; Subject type: Parkinson's. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003516. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 25; recordings: 25; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003516 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003516

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003517. Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 17; recordings: 34; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003517 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003517

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003518. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003519. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003522. Modality: eeg; Experiment type: Decision-making; Subject type: TBI. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003523. Modality: eeg; Experiment type: Memory; Subject type: TBI. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003555. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003568. Modality: meg; Experiment type: Affect; Subject type: Healthy. Subjects: 51; recordings: 118; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003568 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003568

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003570. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 40; recordings: 40; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003570 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003570

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003574. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 18; recordings: 18; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003574 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003574

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003602. Modality: eeg; Experiment type: Decision-making; Subject type: Other. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003620. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 44; recordings: 100; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003620 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003620

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003626. Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 10; recordings: 30; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003626 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003626

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003633. Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 12; recordings: 96; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003633 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003633

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003638. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 57; recordings: 57; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003638 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003638

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)[source]

Bases: EEGDashDataset

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003655. Modality: eeg; Experiment type: Memory; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003670. Modality: eeg; Experiment type: Attention. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003682. Modality: meg; Experiment type: Learning; Subject type: Healthy. Subjects: 28; recordings: 336; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003682 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003682

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003688. Modality: ieeg; Experiment type: Perception; Subject type: Epilepsy. Subjects: 51; recordings: 107; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003688 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003688

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003690. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. 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

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)[source]

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

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)[source]

Bases: EEGDashDataset

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

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003702 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003702

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003708. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Unknown. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003710. Modality: eeg; Experiment type: Perception; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003739. Modality: eeg; Experiment type: Perception; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003751. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 38; recordings: 38; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003751 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003751

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003753. Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 25; recordings: 25; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

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

Examples

>>> from eegdash.dataset import DS003753
>>> dataset = DS003753(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
class eegdash.dataset.DS003766(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003766. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003768. Modality: eeg; Experiment type: Sleep; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003774. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 20; recordings: 240; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003774 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003774

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003775. Modality: eeg; Experiment type: Resting-state; Subject type: Healthy. Subjects: 111; recordings: 153; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003775 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003775

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003800. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Dementia. Subjects: 13; recordings: 24; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003800 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003800

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003801. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 20; recordings: 20; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003801 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003801

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)[source]

Bases: EEGDashDataset

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

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003805 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003805

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003810. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. 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

Examples

>>> from eegdash.dataset import DS003810
>>> dataset = DS003810(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
class eegdash.dataset.DS003816(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003816. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 48; recordings: 1077; tasks: 8.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003816 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003816

Examples

>>> from eegdash.dataset import DS003816
>>> dataset = DS003816(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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003822. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 25; recordings: 25; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003822 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003822

Examples

>>> from eegdash.dataset import DS003822
>>> dataset = DS003822(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
class eegdash.dataset.DS003825(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003825. Modality: eeg; Experiment type: Perception; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003838. Modality: eeg; Experiment type: Memory; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003844. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 6; recordings: 38; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003844 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003844

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003846. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003848. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 6; recordings: 22; tasks: 6.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003848 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003848

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003876. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 39; recordings: 54; tasks: 3.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003876 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003876

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003885. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 24; recordings: 24; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003885 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003885

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003887. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 24; recordings: 24; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003887 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003887

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003922. Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 14; recordings: 164; tasks: 3.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds003922 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003922

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003944. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Schizophrenia/Psychosis. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003947. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Schizophrenia/Psychosis. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003969. Modality: eeg; Experiment type: Attention; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds003987. Modality: eeg; Experiment type: Attention; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004000. Modality: eeg; Experiment type: Decision-making; Subject type: Schizophrenia/Psychosis. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004010. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 24; recordings: 24; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004010 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004010

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004011. Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 22; recordings: 132; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004011 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004011

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004015. Modality: eeg; Experiment type: Attention; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004017. Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 21; recordings: 63; tasks: 0.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004017 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004017

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004018. Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 16; recordings: 32; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004018 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004018

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004019. Modality: eeg; Experiment type: Other; Subject type: Obese. Subjects: 62; recordings: 62; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004019 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004019

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004022. Modality: eeg; Experiment type: Motor; Subject type: Other. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004024. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004033. Modality: eeg; Experiment type: Motor. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004040. Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 13; recordings: 26; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004040 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004040

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004043. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 20; recordings: 20; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004043 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004043

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004067. Modality: eeg; Experiment type: Affect; Subject type: Healthy. 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

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004078. Modality: meg; Experiment type: Other; Subject type: Healthy. Subjects: 12; recordings: 720; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004078 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004078

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004080. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 74; recordings: 117; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004080 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004080

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004100. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 57; recordings: 319; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004100 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004100

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004105. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 17; recordings: 34; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004105 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004105

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004106. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 27; recordings: 29; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004106 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004106

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004117. Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 23; recordings: 85; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004117 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004117

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004119. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 21; recordings: 22; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004119 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004119

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004120. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 109; recordings: 131; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004120 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004120

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004121. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 21; recordings: 60; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004121 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004121

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004122. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 32; recordings: 63; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004122 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004122

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004123. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 29; recordings: 30; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004123 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004123

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004127. Modality: ieeg; Experiment type: Other; Subject type: Other. Subjects: 8; recordings: 73; tasks: 11.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004127 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004127

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004147. Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 12; recordings: 12; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004147 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004147

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004148. Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 60; recordings: 900; tasks: 5.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004148 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004148

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004151. Modality: eeg; Experiment type: Attention; Subject type: Obese. Subjects: 57; recordings: 57; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004151 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004151

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004152. Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 21; recordings: 21; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004152 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004152

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004166. Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 71; recordings: 213; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004166 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004166

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004194. Modality: ieeg; Experiment type: Perception; Subject type: Epilepsy. Subjects: 14; recordings: 209; tasks: 7.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004194 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004194

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004196. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. Subjects: 4; recordings: 4; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004196 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004196

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004200. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 20; recordings: 20; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004200 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004200

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004229. Modality: meg; Experiment type: Perception; Subject type: Dyslexia. Subjects: 2; recordings: 3; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004229 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004229

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)[source]

Bases: EEGDashDataset

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

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004252 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004252

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004256. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 53; recordings: 53; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004256 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004256

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004262. Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 21; recordings: 21; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004262 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004262

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004264. Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 21; recordings: 21; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004264 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004264

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004278. Modality: meg; 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/ds004278 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004278

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004279. Modality: eeg; Experiment type: Perception; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004284. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 18; recordings: 18; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004284 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004284

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004295. Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 26; recordings: 26; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004295 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004295

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004306. Modality: eeg; Experiment type: Perception; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004315. Modality: eeg; Experiment type: Affect; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004317. Modality: eeg; Experiment type: Affect; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004324. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 26; recordings: 26; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004324 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004324

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004330. Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 30; recordings: 270; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004330 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004330

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004347. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 24; recordings: 24; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004347 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004347

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004348. Modality: eeg; Experiment type: Sleep; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004350. Modality: eeg; Experiment type: Memory; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004356. Modality: eeg; Experiment type: Perception; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004357. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 16; recordings: 16; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004357 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004357

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004362. Modality: eeg; Experiment type: Motor; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004367. Modality: eeg; Experiment type: Perception; Subject type: Schizophrenia/Psychosis. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004368. Modality: eeg; Experiment type: Perception; Subject type: Schizophrenia/Psychosis. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004369. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 41; recordings: 41; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004369 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004369

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004370. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Surgery. Subjects: 7; recordings: 15; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004370 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004370

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004381. Modality: eeg; Experiment type: Other; Subject type: Surgery. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004388. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 40; recordings: 399; tasks: 3.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004388 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004388

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004389. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 26; recordings: 260; tasks: 4.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004389 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004389

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004395. Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 364; recordings: 6483; tasks: 3.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004395 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004395

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004398. Modality: meg; Experiment type: Unknown; Subject type: Unknown. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004398 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004398

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004408. Modality: eeg; Experiment type: Other; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004444. Modality: eeg; Experiment type: Motor; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004446. Modality: eeg; Experiment type: Motor; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004447. Modality: eeg; Experiment type: Motor; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004448. Modality: eeg; Experiment type: Motor; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004457. Modality: ieeg; Experiment type: Clinical/Intervention; 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004460. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 20; recordings: 40; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004460 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004460

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004473. Modality: ieeg; Experiment type: Motor; Subject type: Epilepsy. Subjects: 8; recordings: 8; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004473 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004473

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004475. Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 30; recordings: 30; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004475 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004475

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004477. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 9; recordings: 9; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004477 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004477

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004483. Modality: meg; Experiment type: Memory; Subject type: Healthy. Subjects: 19; recordings: 282; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004483 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004483

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004504. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Dementia. Subjects: 88; recordings: 88; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004504 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004504

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004505. Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 25; recordings: 25; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004505 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004505

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004511. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 45; recordings: 134; tasks: 3.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004511 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004511

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004514. Modality: eeg, fnirs; Experiment type: Other; Subject type: Healthy. Subjects: 12; recordings: 24; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004514 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004514

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004515. Modality: eeg; Experiment type: Affect; Subject type: Other. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004517. Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 7; recordings: 7; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004517 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004517

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004519. Modality: eeg; Experiment type: Attention. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004520. Modality: eeg; Experiment type: Memory. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004521. Modality: eeg; Experiment type: Motor. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004532. Modality: eeg; Experiment type: Learning; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004551. Modality: ieeg; Experiment type: Sleep; 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004554. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 16; recordings: 16; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004554 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004554

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004561. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 23; recordings: 23; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004561 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004561

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004563. Modality: eeg; Experiment type: Perception; Subject type: Other. Subjects: 40; recordings: 119; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004563 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004563

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004572. Modality: eeg; Experiment type: Perception. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004574. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Parkinson's. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004577. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004579. Modality: eeg; Experiment type: Decision-making; Subject type: Parkinson's. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004580. Modality: eeg; Experiment type: Decision-making; Subject type: Parkinson's. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004582. Modality: eeg; Experiment type: Affect; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004584. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Parkinson's. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004587. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004588. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 42; recordings: 42; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004588 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004588

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004595. Modality: eeg; Experiment type: Decision-making; Subject type: Other. 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

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004602. Modality: eeg; Experiment type: Perception; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004603. Modality: eeg; Experiment type: Perception; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004621. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004624. Modality: ieeg; Experiment type: Clinical/Intervention; 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004625. Modality: eeg; Experiment type: Attention. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004626. Modality: eeg; Experiment type: Attention; Subject type: Other. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004635. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 48; recordings: 48; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004635 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004635

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004642. Modality: ieeg; Experiment type: Other; Subject type: Surgery. Subjects: 10; recordings: 10; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004642 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004642

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004657. Modality: eeg; Experiment type: Decision-making. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004660. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 21; recordings: 42; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004660 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004660

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004661. Modality: eeg; Experiment type: Memory. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004696. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004703. Modality: ieeg; Experiment type: Memory; Subject type: Surgery. Subjects: 10; recordings: 11; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004703 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004703

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004706. Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 34; recordings: 298; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004706 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004706

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004718. Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 51; recordings: 51; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004718 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004718

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004738. Modality: meg; Experiment type: Other; Subject type: Other. Subjects: 4; recordings: 25; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004738 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004738

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004745. Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 6; recordings: 6; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004745 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004745

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004752. Modality: eeg, ieeg; Experiment type: Memory; Subject type: Epilepsy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004770. Modality: ieeg; Experiment type: Memory; Subject type: Epilepsy. Subjects: 10; recordings: 22; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004770 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004770

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004771. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 61; recordings: 61; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004771 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004771

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004784. Modality: eeg; Experiment type: Attention; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004785. Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 17; recordings: 17; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004785 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004785

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004789. Modality: ieeg; Experiment type: Memory; 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004796. Modality: eeg; Experiment type: Memory/Resting state; Subject type: Other. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004802. Modality: eeg; Experiment type: Affect; Subject type: Other. 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

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004816. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 20; recordings: 20; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004816 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004816

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004817. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 20; recordings: 20; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004817 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004817

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004830. Modality: fnirs; Experiment type: Unknown; Subject type: Unknown. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004837. Modality: meg; Experiment type: Perception; Subject type: Schizophrenia/Psychosis. Subjects: 60; recordings: 106; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004837 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004837

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004840. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Other. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004841. Modality: eeg; Experiment type: Attention. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004842. Modality: eeg; Experiment type: Attention. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004843. Modality: eeg; Experiment type: Attention. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004844. Modality: eeg; Experiment type: Decision-making. 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

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)[source]

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

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)[source]

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

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)[source]

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

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)[source]

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

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)[source]

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

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)[source]

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

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)[source]

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

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004860. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 31; recordings: 31; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004860 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004860

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004865. Modality: ieeg; Experiment type: Memory; Subject type: Surgery. Subjects: 42; recordings: 172; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004865 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004865

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004883. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004902. Modality: eeg; Experiment type: Resting state; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004917. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 24; recordings: 24; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004917 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004917

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004940. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 22; recordings: 48; tasks: 2.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004940 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004940

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004942. Modality: eeg; Experiment type: Memory; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004944. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 22; recordings: 44; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004944 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004944

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004951. Modality: eeg; Experiment type: Learning. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004952. Modality: eeg; Experiment type: Attention; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004973. Modality: fnirs; Experiment type: Attention; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004977. Modality: ieeg; Experiment type: Other; Subject type: Epilepsy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004980. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 17; recordings: 17; tasks: 1.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004980 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004980

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004993. Modality: ieeg; Experiment type: Perception; Subject type: Epilepsy. Subjects: 3; recordings: 3; tasks: 3.

Parameters:

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

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

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

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

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004993 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004993

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004995. Modality: eeg; Experiment type: Attention. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds004998. Modality: meg; Experiment type: Motor; Subject type: Parkinson's. Subjects: 20; recordings: 145; tasks: 6.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004998 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004998

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005021. Modality: eeg; Experiment type: Attention; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005028. Modality: eeg; Experiment type: Motor. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005034. Modality: eeg; Experiment type: Memory; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005048. Modality: eeg; Experiment type: Attention; Subject type: Dementia. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005059. Modality: ieeg; Experiment type: Memory; 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005065. Modality: meg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 21; recordings: 275; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005065 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005065

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005079. Modality: eeg; Experiment type: Affect; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005083. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Surgery. Subjects: 61; recordings: 1357; tasks: 3.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005083 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005083

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005087. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 20; recordings: 60; tasks: 3.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005087 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005087

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005089. Modality: eeg; Experiment type: Attention; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005095. Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 48; recordings: 48; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005095 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005095

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005106. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 42; recordings: 42; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005106 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005106

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005107. Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 21; recordings: 350; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005107 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005107

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005114. Modality: eeg; Experiment type: Attention; Subject type: TBI. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005121. Modality: eeg; Experiment type: Memory; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005131. Modality: eeg; Experiment type: Attention/Memory; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005169. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 20; recordings: 112; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005169 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005169

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005170. Modality: eeg; Experiment type: other. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005178. Modality: eeg; Experiment type: Sleep; Subject type: Healthy. Subjects: 10; recordings: 140; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005178 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005178

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005185. Modality: eeg; Experiment type: Sleep; Subject type: Healthy. Subjects: 20; recordings: 356; tasks: 3.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005185 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005185

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005189. Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 30; recordings: 30; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005189 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005189

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005207. Modality: eeg; Experiment type: Sleep; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005241. Modality: meg; Experiment type: Unknown; Subject type: Healthy. Subjects: 24; recordings: 117; tasks: 2.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005241 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005241

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005261. Modality: meg; Experiment type: Learning; Subject type: Healthy. Subjects: 17; recordings: 128; tasks: 2.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005261 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005261

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005262. Modality: eeg; Experiment type: other; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005273. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 33; recordings: 33; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005273 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005273

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005274. Modality: eeg; Subject type: Healthy. Subjects: 22; recordings: 22; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005274 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005274

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005279. Modality: meg; Experiment type: Other; Subject type: Healthy. Subjects: 30; recordings: 90; tasks: 0.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005279 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005279

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005280. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 223; recordings: 669; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005280 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005280

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005285. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 29; recordings: 116; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005285 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005285

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005286. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 30; recordings: 30; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005286 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005286

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005291. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 65; recordings: 65; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005291 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005291

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005292. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 142; recordings: 426; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005292 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005292

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005293. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 95; recordings: 570; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005293 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005293

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005296. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005305. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. 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

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005340. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 15; recordings: 15; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005340 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005340

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005342. Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 32; recordings: 32; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005342 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005342

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005343. Modality: eeg; Experiment type: Perception; Subject type: Development. Subjects: 43; recordings: 43; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005343 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005343

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005345. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 26; recordings: 26; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005345 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005345

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005346. Modality: meg; Experiment type: Memory; Subject type: Healthy. Subjects: 30; recordings: 90; tasks: 3.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005346 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005346

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005356. Modality: meg; Experiment type: Learning; Subject type: Depression. Subjects: 85; recordings: 116; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005356 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005356

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005363. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 43; recordings: 43; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005363 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005363

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005383. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 30; recordings: 240; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005383 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005383

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005385. Modality: eeg; Experiment type: Resting-state; Subject type: Healthy. Subjects: 608; recordings: 3264; tasks: 2.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005385 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005385

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005397. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 26; recordings: 26; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005397 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005397

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005398. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 185; recordings: 185; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005398 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005398

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005403. Modality: eeg; Experiment type: Motor; 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

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005407. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 25; recordings: 29; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005407 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005407

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005408. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 25; recordings: 29; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005408 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005408

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005410. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 81; recordings: 81; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005410 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005410

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005411. Modality: ieeg; Experiment type: Memory; Subject type: Epilepsy. Subjects: 47; recordings: 193; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005411 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005411

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005415. Modality: ieeg; Experiment type: Perception; Subject type: Epilepsy. Subjects: 13; recordings: 13; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005415 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005415

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005416. Modality: eeg; Experiment type: Resting-state; Subject type: Healthy. Subjects: 23; recordings: 23; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005416 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005416

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005429. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 15; recordings: 61; tasks: 3.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005429 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005429

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005448. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 13; recordings: 18; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005448 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005448

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)[source]

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

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005489. Modality: ieeg; Experiment type: Clinical/Intervention; 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005491. Modality: ieeg; Experiment type: Memory; 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

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005505. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 136; recordings: 1342; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005505 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005505

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005506. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 150; recordings: 1405; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005506 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005506

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005507. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 184; recordings: 1812; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005507 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005507

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005508. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 324; recordings: 3342; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005508 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005508

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005509. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 330; recordings: 3326; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005509 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005509

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005510. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 135; recordings: 1227; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005510 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005510

Examples

>>> from eegdash.dataset import DS005510
>>> dataset = DS005510(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
class eegdash.dataset.DS005511(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005511. Modality: EEG. 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/ds005511 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005511

Examples

>>> from eegdash.dataset import DS005511
>>> dataset = DS005511(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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005512. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 257; recordings: 2320; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005512 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005512

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005514. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 295; recordings: 2885; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005514 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005514

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005515. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 533; recordings: 2516; tasks: 8.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005515 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005515

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005516. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 430; recordings: 3397; tasks: 8.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005516 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005516

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005520. Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 23; recordings: 69; tasks: 3.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005520 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005520

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005522. Modality: ieeg; Experiment type: Memory; Subject type: Unknown. Subjects: 55; recordings: 176; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005522 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005522

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005523. Modality: ieeg; Experiment type: Memory; Subject type: Surgery. Subjects: 21; recordings: 102; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005523 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005523

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005530. Modality: eeg; Experiment type: Sleep; Subject type: Healthy. Subjects: 17; recordings: 21; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005530 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005530

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005540. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 59; recordings: 103; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005540 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005540

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005545. Modality: ieeg; Experiment type: Memory; 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

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005557. Modality: ieeg; Experiment type: Memory; Subject type: Other. Subjects: 16; recordings: 58; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005557 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005557

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005558. Modality: ieeg; Experiment type: Memory; 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005565. Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 24; recordings: 24; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005565 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005565

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005574. Modality: ieeg; Experiment type: Other; Subject type: Unknown. Subjects: 9; recordings: 9; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005574 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005574

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005586. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 23; recordings: 23; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005586 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005586

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005620. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. Subjects: 21; recordings: 202; tasks: 3.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005620 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005620

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005628. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 102; recordings: 306; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005628 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005628

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005642. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 21; recordings: 21; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005642 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005642

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005648. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 21; recordings: 21; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005648 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005648

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005662. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 80; recordings: 80; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005662 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005662

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005670. Modality: ieeg; Experiment type: Resting-state; Subject type: Epilepsy. Subjects: 2; recordings: 2; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005670 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005670

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005672. Modality: eeg; Experiment type: Memory; 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005688. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. Subjects: 20; recordings: 89; tasks: 5.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005688 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005688

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005691. Modality: ieeg; Experiment type: Attention; Subject type: Other. Subjects: 8; recordings: 8; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005691 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005691

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005692. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 30; recordings: 59; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005692 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005692

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005697. Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 51; recordings: 51; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005697 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005697

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005752. Modality: meg; Experiment type: Other; Subject type: Healthy. Subjects: 123; recordings: 1055; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005752 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005752

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005776. Modality: fnirs; Experiment type: Unknown; Subject type: Unknown. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005777. Modality: fnirs; Experiment type: Unknown; Subject type: Unknown. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005779. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. Subjects: 19; recordings: 250; tasks: 16.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005779 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005779

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005795. Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 34; recordings: 39; tasks: 2.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005795 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005795

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005810. Modality: meg; Experiment type: Perception; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005811. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 19; recordings: 448; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005811 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005811

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005815. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 20; recordings: 103; tasks: 3.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005815 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005815

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005841. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 48; recordings: 288; tasks: 6.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005841 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005841

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005863. Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 127; recordings: 357; tasks: 4.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005863 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005863

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005866. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 60; recordings: 60; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005866 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005866

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005868. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 48; recordings: 48; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005868 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005868

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005872. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 1; recordings: 1; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005872 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005872

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005873. Modality: eeg, emg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 125; recordings: 5654; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005873 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005873

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005876. Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 29; recordings: 29; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005876 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005876

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005907. Modality: eeg; Experiment type: Learning; Subject type: Alcohol. Subjects: 53; recordings: 53; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005907 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005907

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005930. 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/ds005930 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005930

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005931. Modality: ieeg; Experiment type: Motor; Subject type: Epilepsy. Subjects: 8; recordings: 16; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005931 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005931

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005932. Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 29; recordings: 29; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005932 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005932

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005935. Modality: fnirs; Experiment type: Unknown; Subject type: Unknown. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005946. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 39; recordings: 39; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005946 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005946

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005953. Modality: ieeg; Experiment type: Perception; Subject type: Surgery. Subjects: 2; recordings: 3; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005953 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005953

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005960. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 41; recordings: 41; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005960 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005960

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds005963. Modality: fnirs; Experiment type: Unknown; Subject type: Unknown. Subjects: 11; recordings: 291; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS 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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006012. Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 21; recordings: 193; tasks: 2.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006012 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006012

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006018. Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 127; recordings: 357; tasks: 4.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006018 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006018

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006033. Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 3; recordings: 5; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006033 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006033

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006035. Modality: meg; Experiment type: Motor; 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006036. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Dementia. Subjects: 88; recordings: 88; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006036 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006036

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006040. Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 28; recordings: 392; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006040 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006040

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006065. Modality: ieeg; Experiment type: Clinical/Intervention; 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006095. Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 71; recordings: 1182; tasks: 9.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006095 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006095

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006104. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 24; recordings: 56; tasks: 3.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006104 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006104

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006126. Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 5; recordings: 90; tasks: 6.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006126 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006126

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006142. Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 27; recordings: 27; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006142 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006142

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)[source]

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

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006233. Modality: ieeg; Experiment type: Other; Subject type: Surgery. Subjects: 108; recordings: 347; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006233 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006233

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006234. Modality: ieeg; Experiment type: Other; 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006253. Modality: ieeg; Experiment type: Decision-making; Subject type: Epilepsy. Subjects: 23; recordings: 201; tasks: 4.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006253 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006253

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006260. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 76; recordings: 366; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006260 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006260

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006269. Modality: eeg; Experiment type: Resting-state; Subject type: Other. Subjects: 24; recordings: 40; tasks: 2.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006269 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006269

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006317. Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 2; recordings: 64; tasks: 2.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006317 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006317

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006334. Modality: meg; Experiment type: Memory; 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006366. Modality: eeg; Experiment type: Sleep; Subject type: Healthy. Subjects: 92; recordings: 148; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006366 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006366

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006367. Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 52; recordings: 52; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006367 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006367

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006370. Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 56; recordings: 56; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006370 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006370

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006377. Modality: fnirs; Experiment type: Unknown; Subject type: Unknown. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006392. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Unknown. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006394. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 33; recordings: 60; tasks: 2.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006394 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006394

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006434. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 66; recordings: 118; tasks: 5.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006434 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006434

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006437. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. Subjects: 9; recordings: 63; tasks: 5.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006437 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006437

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006446. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 29; recordings: 29; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006446 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006446

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006460. 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/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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006465. Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 20; recordings: 80; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006465 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006465

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006466. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 66; recordings: 1257; tasks: 6.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006466 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006466

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006468. Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 24; recordings: 189; tasks: 4.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006468 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006468

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006480. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 68; recordings: 68; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006480 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006480

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006502. Modality: meg; Experiment type: Learning; Subject type: Healthy. Subjects: 31; recordings: 380; tasks: 4.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006502 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006502

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006519. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 21; recordings: 35; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006519 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006519

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006525. Modality: eeg; Experiment type: Resting-state; Subject type: Unknown. Subjects: 34; recordings: 34; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006525 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006525

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006545. Modality: fnirs; Experiment type: Unknown; Subject type: Unknown. Subjects: 50; recordings: 838; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS 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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006547. Modality: eeg; Experiment type: Perception; 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

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006563. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 12; recordings: 12; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006563 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006563

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006576. Modality: eeg; Experiment type: Sleep; Subject type: Healthy. Subjects: 48; recordings: 48; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006576 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006576

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006593. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 21; recordings: 21; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006593 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006593

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006629. Modality: meg; Experiment type: Perception; Subject type: Healthy. Subjects: 19; recordings: 38; tasks: 2.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006629 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006629

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006647. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 4; recordings: 4; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006647 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006647

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006648. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 47; recordings: 47; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006648 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006648

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006695. Modality: eeg; Experiment type: Sleep; 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006720. Modality: meg; Experiment type: Memory; Subject type: Healthy. Subjects: 24; recordings: 246; tasks: 3.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006720 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006720

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006735. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 27; recordings: 27; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006735 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006735

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006761. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 31; recordings: 31; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006761 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006761

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006768. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 30; recordings: 210; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006768 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006768

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006801. Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 21; recordings: 42; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006801 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006801

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006802. Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 24; recordings: 24; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006802 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006802

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006803. Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 63; recordings: 126; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006803 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006803

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006839. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 36; recordings: 144; tasks: 4.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006839 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006839

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006848. Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 30; recordings: 52; tasks: 2.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006848 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006848

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006850. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 63; recordings: 126; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006850 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006850

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006861. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 120; recordings: 239; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006861 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006861

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006866. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 148; recordings: 148; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006866 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006866

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006890. Modality: ieeg; Experiment type: Motor; Subject type: Healthy. Subjects: 2; recordings: 870; tasks: 5.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006890 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006890

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006902. Modality: fnirs; Experiment type: Perception; Subject type: Healthy. 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

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006910. Modality: ieeg; Experiment type: Other; Subject type: Unknown. Subjects: 121; recordings: 384; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006910 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006910

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006914. Modality: ieeg; Experiment type: Other; Subject type: Epilepsy. Subjects: 110; recordings: 353; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006914 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006914

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006921. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Other. Subjects: 38; recordings: 152; tasks: 2.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006921 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006921

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006923. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Other. Subjects: 140; recordings: 280; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006923 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006923

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006940. Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 7; recordings: 935; tasks: 15.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006940 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006940

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006945. Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 5; recordings: 14; tasks: 3.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006945 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006945

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds006963. Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 32; recordings: 32; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds006963 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006963

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds007006. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 10; recordings: 50; tasks: 5.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007006 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007006

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds007020. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Parkinson's. Subjects: 94; recordings: 94; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007020 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007020

Examples

>>> from eegdash.dataset import DS007020
>>> dataset = DS007020(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
class eegdash.dataset.DS007028(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds007028. Modality: eeg. Subjects: 3; recordings: 3; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007028 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007028

Examples

>>> from eegdash.dataset import DS007028
>>> dataset = DS007028(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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds007081. Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 41; recordings: 41; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007081 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007081

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds007095. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 8; recordings: 6019; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007095 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007095

Examples

>>> from eegdash.dataset import DS007095
>>> dataset = DS007095(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
class eegdash.dataset.DS007118(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds007118. Modality: ieeg. Subjects: 65; recordings: 82; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007118 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007118

Examples

>>> from eegdash.dataset import DS007118
>>> dataset = DS007118(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
class eegdash.dataset.DS007119(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds007119. Modality: ieeg. Subjects: 103; recordings: 106; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007119 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007119

Examples

>>> from eegdash.dataset import DS007119
>>> dataset = DS007119(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
class eegdash.dataset.DS007120(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds007120. Modality: ieeg. Subjects: 65; recordings: 70; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007120 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007120

Examples

>>> from eegdash.dataset import DS007120
>>> dataset = DS007120(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
class eegdash.dataset.DS007162(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds007162. Modality: eeg. Subjects: 34; recordings: 69; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007162 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007162

Examples

>>> from eegdash.dataset import DS007162
>>> dataset = DS007162(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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds007172. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 100; recordings: 501; tasks: 6.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007172 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007172

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)[source]

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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds007176. Modality: eeg; Experiment type: Resting-state; Subject type: Healthy. Subjects: 45; recordings: 300; tasks: 2.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007176 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007176

Examples

>>> from eegdash.dataset import DS007176
>>> dataset = DS007176(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
class eegdash.dataset.DS007180(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds007180. Modality: eeg. Subjects: 25; recordings: 25; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007180 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007180

Examples

>>> from eegdash.dataset import DS007180
>>> dataset = DS007180(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
class eegdash.dataset.DS007181(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds007181. Modality: eeg. Subjects: 59; recordings: 59; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007181 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007181

Examples

>>> from eegdash.dataset import DS007181
>>> dataset = DS007181(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
class eegdash.dataset.DS007262(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

OpenNeuro dataset ds007262. Modality: eeg. Subjects: 18; recordings: 18; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007262 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007262

Examples

>>> from eegdash.dataset import DS007262
>>> dataset = DS007262(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)[source]

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r1. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 136; recordings: 1342; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r1 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r1

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r10. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 533; recordings: 2516; tasks: 8.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r10 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r10

Examples

>>> from eegdash.dataset import EEG2025R10
>>> dataset = EEG2025R10(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
class eegdash.dataset.EEG2025R10MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r10mini. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 20; recordings: 220; tasks: 8.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r10mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r10mini

Examples

>>> from eegdash.dataset import EEG2025R10MINI
>>> dataset = EEG2025R10MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
class eegdash.dataset.EEG2025R11(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r11. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 430; recordings: 3397; tasks: 8.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r11 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r11

Examples

>>> from eegdash.dataset import EEG2025R11
>>> dataset = EEG2025R11(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
class eegdash.dataset.EEG2025R11MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r11mini. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 20; recordings: 220; tasks: 8.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r11mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r11mini

Examples

>>> from eegdash.dataset import EEG2025R11MINI
>>> dataset = EEG2025R11MINI(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
class eegdash.dataset.EEG2025R1MINI(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r1mini. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 20; recordings: 239; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r1mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r1mini

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r2. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 150; recordings: 1405; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r2 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r2

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r2mini. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 20; recordings: 240; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r2mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r2mini

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r3. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 184; recordings: 1812; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r3 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r3

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r3mini. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 20; recordings: 240; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r3mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r3mini

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r4. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 324; recordings: 3342; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r4 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r4

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r4mini. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 20; recordings: 240; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r4mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r4mini

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r5. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 330; recordings: 3326; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r5 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r5

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r5mini. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 20; recordings: 240; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r5mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r5mini

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r6. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 135; recordings: 1227; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r6 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r6

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r6mini. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 20; recordings: 237; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r6mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r6mini

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r7. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 381; recordings: 3100; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r7 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r7

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r7mini. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 20; recordings: 239; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r7mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r7mini

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r8. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 257; recordings: 2320; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r8 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r8

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r8mini. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 20; recordings: 238; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r8mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r8mini

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r9. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 295; recordings: 2885; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r9 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r9

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset EEG2025r9mini. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 20; recordings: 237; tasks: 10.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query

Merged query with the dataset filter applied.

Type:

dict

records

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/EEG2025r9mini NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=EEG2025r9mini

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)[source]

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][source]

Get a list of channel labels from channels.tsv.

channel_types(data_filepath: str) → list[str][source]

Get a list of channel types from channels.tsv.

check_eeg_dataset() → bool[source]

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][source]

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_all_participants_tsv() → dict[str, dict[str, Any]][source]

Get all rows from participants.tsv as a dictionary.

Returns:

A dictionary mapping participant_id to a dict of column values. Returns {} if no participants.tsv exists or it is empty.

Return type:

dict

get_bids_file_attribute(attribute: str, data_filepath: str) → Any[source]

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][source]

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][source]

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_orphan_participants() → dict[str, dict[str, Any]][source]

Get participant rows that have no matching file in the dataset.

Identifies subjects present in participants.tsv but with no corresponding recording file in self.files.

Returns:

A dictionary mapping orphan participant_id to their TSV data. Returns {} if there are no orphans, no TSV, or no files.

Return type:

dict

get_relative_bidspath(filepath: str | Path) → str[source]

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[source]

Get the number of time points in the recording.

Calculated from SamplingFrequency and RecordingDuration in the modality-specific JSON sidecar (e.g., eeg.json or meg.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][source]

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)[source]

Bases: EEGDashDataset

A dataset helper for the EEG 2025 Challenge.

This class simplifies access to the EEG 2025 Challenge datasets. It is a specialized version of EEGDashDataset that is pre-configured for the challenge’s data releases. It automatically maps a release name (e.g., “R1”) to the corresponding OpenNeuro dataset and handles the selection of subject subsets (e.g., “mini” release).

Parameters:

  • release (str) – The name of the challenge release to load. Must be one of the keys in RELEASE_TO_OPENNEURO_DATASET_MAP (e.g., “R1”, “R2”, …, “R11”).

  • cache_dir (str) – The local directory where the dataset will be downloaded and cached.

  • mini (bool, default True) – If True, the dataset is restricted to the official “mini” subset of subjects for the specified release. If False, all subjects for the release are included.

  • query (dict, optional) – An additional MongoDB-style query to apply as a filter. This query is combined with the release and subject filters using a logical AND. The query must not contain the dataset key, as this is determined by the release parameter.

  • s3_bucket (str, optional) – The base S3 bucket URI where the challenge data is stored. Defaults to the official challenge bucket.

  • **kwargs – Additional keyword arguments that are passed directly to the EEGDashDataset constructor.

Raises:

ValueError – If the specified release is unknown, or if the query argument contains a dataset key. Also raised if mini is True and a requested subject is not part of the official mini-release subset.

See also

EEGDashDataset

The base class for creating datasets from queries.

class eegdash.dataset.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)[source]

Bases: BaseConcatDataset

Create a new EEGDashDataset from a given query or local BIDS dataset directory and dataset name. An EEGDashDataset is pooled collection of EEGDashBaseDataset instances (individual recordings) and is a subclass of braindecode’s BaseConcatDataset.

Examples

Basic usage with dataset and subject filtering:

>>> from eegdash import EEGDashDataset
>>> dataset = EEGDashDataset(
...     cache_dir="./data",
...     dataset="ds002718",
...     subject="012"
... )
>>> print(f"Number of recordings: {len(dataset)}")

Filter by multiple subjects and specific task:

>>> subjects = ["012", "013", "014"]
>>> dataset = EEGDashDataset(
...     cache_dir="./data",
...     dataset="ds002718",
...     subject=subjects,
...     task="RestingState"
... )

Load and inspect EEG data from recordings:

>>> if len(dataset) > 0:
...     recording = dataset[0]
...     raw = recording.load()
...     print(f"Sampling rate: {raw.info['sfreq']} Hz")
...     print(f"Number of channels: {len(raw.ch_names)}")
...     print(f"Duration: {raw.times[-1]:.1f} seconds")

Advanced filtering with raw MongoDB queries:

>>> from eegdash import EEGDashDataset
>>> query = {
...     "dataset": "ds002718",
...     "subject": {"$in": ["012", "013"]},
...     "task": "RestingState"
... }
>>> dataset = EEGDashDataset(cache_dir="./data", query=query)

Working with dataset collections and braindecode integration:

>>> # EEGDashDataset is a braindecode BaseConcatDataset
>>> for i, recording in enumerate(dataset):
...     if i >= 2:  # limit output
...         break
...     print(f"Recording {i}: {recording.description}")
...     raw = recording.load()
...     print(f"  Channels: {len(raw.ch_names)}, Duration: {raw.times[-1]:.1f}s")
Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Raw MongoDB query to filter records. If provided, it is merged with keyword filtering arguments (see **kwargs) using logical AND. You must provide at least a dataset (either in query or as a keyword argument). Only fields in ALLOWED_QUERY_FIELDS are considered for filtering.

  • dataset (str) – Dataset identifier (e.g., "ds002718"). Required if query does not already specify a dataset.

  • task (str | list[str]) – Task name(s) to filter by (e.g., "RestingState").

  • subject (str | list[str]) – Subject identifier(s) to filter by (e.g., "NDARCA153NKE").

  • session (str | list[str]) – Session identifier(s) to filter by (e.g., "1").

  • run (str | list[str]) – Run identifier(s) to filter by (e.g., "1").

  • description_fields (list[str]) – Fields to extract from each record and include in dataset descriptions (e.g., “subject”, “session”, “run”, “task”).

  • s3_bucket (str | None) – Optional S3 bucket URI (e.g., “s3://mybucket”) to use instead of the default OpenNeuro bucket when downloading data files.

  • records (list[dict] | None) – Pre-fetched metadata records. If provided, the dataset is constructed directly from these records and no MongoDB query is performed.

  • download (bool, default True) – If False, load from local BIDS files only. Local data are expected under cache_dir / dataset; no DB or S3 access is attempted.

  • n_jobs (int) – Number of parallel jobs to use where applicable (-1 uses all cores).

  • eeg_dash_instance (EEGDash | None) – Optional existing EEGDash client to reuse for DB queries. If None, a new client is created on demand, not used in the case of no download.

  • database (str | None) – Database name to use (e.g., “eegdash”, “eegdash_staging”). If None, uses the default database.

  • auth_token (str | None) – Authentication token for accessing protected databases. Required for staging or admin operations.

  • **kwargs (dict) –

    Additional keyword arguments serving two purposes:

    • Filtering: any keys present in ALLOWED_QUERY_FIELDS are treated as query filters (e.g., dataset, subject, task, …).

    • Dataset options: remaining keys are forwarded to EEGDashRaw.

property cumulative_sizes: list[int]

Recompute cumulative sizes from current dataset lengths.

Overrides the cached version from BaseConcatDataset because individual dataset lengths can change after lazy raw loading (estimated ntimes from JSON metadata may differ from actual n_times in the raw file).

download_all(n_jobs: int | None = None) → None[source]

Download missing remote files in parallel.

Parameters:

n_jobs (int | None) – Number of parallel workers to use. If None, defaults to self.n_jobs.

save(path, overwrite=False)[source]

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

class eegdash.dataset.NM000103(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]

Bases: EEGDashDataset

OpenNeuro dataset nm000103. Modality: eeg; Experiment type: Unknown; Subject type: Development. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset nm000104. Modality: emg; Experiment type: Motor; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset nm000105. Modality: emg; Experiment type: Motor; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset nm000106. Modality: emg; Experiment type: Motor; Subject type: Healthy. 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

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)[source]

Bases: EEGDashDataset

OpenNeuro dataset nm000107. Modality: emg; Experiment type: Motor; Subject type: Healthy. 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

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][source]

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]