eegdash.dataset.DS005574#

participants.tsv (OpenNeuro ds005574). Access recordings and metadata through EEGDash.

Modality: [‘ieeg’] Tasks: 0 License: CC0 Subjects: 0 Recordings: 0 Source: openneuro

Dataset Information#

Dataset ID

DS005574

Title

participants.tsv

Year

2024

Authors

Zaid Zada, Samuel A. Nastase, Bobbi Aubrey, Itamar Jalon, Ariel Goldstein, Sebastian Michelmann, Haocheng Wang, Liat Hasenfratz, Werner Doyle, Daniel Friedman, Patricia Dugan, Lucia Melloni, Sasha Devore, Orrin Devinsky, Adeen Flinker, Uri Hasson

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds005574.v1.0.2

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds005574,
  title = {participants.tsv},
  author = {Zaid Zada and Samuel A. Nastase and Bobbi Aubrey and Itamar Jalon and Ariel Goldstein and Sebastian Michelmann and Haocheng Wang and Liat Hasenfratz and Werner Doyle and Daniel Friedman and Patricia Dugan and Lucia Melloni and Sasha Devore and Orrin Devinsky and Adeen Flinker and Uri Hasson},
  doi = {10.18112/openneuro.ds005574.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds005574.v1.0.2},
}

Highlights#

Subjects & recordings

  • Subjects: 0

  • Recordings: 0

  • Tasks: 0

Channels & sampling rate

  • Channels: 91, 174, 138, 114, 124, 264, 205, 178, 167

  • Sampling rate (Hz): 2048.0, 512.0 (8)

  • Duration (hours): 0

Tasks & conditions

  • Tasks: 0

  • Experiment type: Unknown

  • Subject type: Unknown

Files & format

  • Size on disk: Unknown

  • File count: Unknown

  • Format: Unknown

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds005574.v1.0.2

Provenance

Quickstart#

Install

pip install eegdash

Load a recording

from eegdash.dataset import DS005574

dataset = DS005574(cache_dir="./data")
recording = dataset[0]
raw = recording.load()

Filter/query

dataset = DS005574(cache_dir="./data", subject="01")

dataset = DS005574(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Quality & caveats#

  • No dataset-specific caveats are listed in the available metadata.

API#

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: Unknown; Subject type: Unknown. Subjects: 9; recordings: 9; tasks: 1.

Parameters:

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

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

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

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

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query#

Merged query with the dataset filter applied.

Type:

dict

records#

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005574 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005574 DOI: https://doi.org/10.18112/openneuro.ds005574.v1.0.2

Examples

>>> from eegdash.dataset import DS005574
>>> dataset = DS005574(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
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

See Also#