eegdash.dataset.DS005398#

participants.tsv (OpenNeuro ds005398). Access recordings and metadata through EEGDash.

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

Dataset Information#

Dataset ID

DS005398

Title

participants.tsv

Year

2024

Authors

Yipeng Zhang, Atsuro Daida, Lawrence Liu, Naoto Kuroda, Yuanyi Ding, Shingo Oana, Tonmoy Monsoor, Chenda Duan, Shaun A. Hussain, Joe X Qiao, Noriko Salamon, Aria Fallah, Myung Shin Sim, Raman Sankar, Richard J. Staba, Jerome Engel Jr., Eishi Asano, Vwani Roychowdhury, Hiroki Nariai

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds005398.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds005398,
  title = {participants.tsv},
  author = {Yipeng Zhang and Atsuro Daida and Lawrence Liu and Naoto Kuroda and Yuanyi Ding and Shingo Oana and Tonmoy Monsoor and Chenda Duan and Shaun A. Hussain and Joe X Qiao and Noriko Salamon and Aria Fallah and Myung Shin Sim and Raman Sankar and Richard J. Staba and Jerome Engel Jr. and Eishi Asano and Vwani Roychowdhury and Hiroki Nariai},
  doi = {10.18112/openneuro.ds005398.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005398.v1.0.1},
}

Highlights#

Subjects & recordings

  • Subjects: 0

  • Recordings: 0

  • Tasks: 0

Channels & sampling rate

  • Channels: 127, 118 (6), 94 (2), 69, 138 (4), 150 (2), 108 (8), 34, 80, 56, 68, 106 (5), 133, 81, 32, 67, 60, 77 (2), 86 (2), 74 (2), 73 (2), 70 (2), 98 (2), 114 (3), 126 (2), 100 (4), 111, 72, 101, 130 (3), 112 (20), 136, 40, 79 (2), 109, 122 (3), 45, 33, 63, 76 (2), 84, 99, 164, 62, 144 (2), 92, 149, 102 (5), 116 (3), 107 (2), 156, 83, 44, 104 (8), 120 (4), 124 (5), 132 (4), 110 (3), 64 (4), 95, 93, 96 (2), 58 (2), 140 (2), 128 (30)

  • Sampling rate (Hz): 2000.0 (49), 1000.0 (135), 200.0

  • 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.ds005398.v1.0.1

Provenance

Quickstart#

Install

pip install eegdash

Load a recording

from eegdash.dataset import DS005398

dataset = DS005398(cache_dir="./data")
recording = dataset[0]
raw = recording.load()

Filter/query

dataset = DS005398(cache_dir="./data", subject="01")

dataset = DS005398(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Quality & caveats#

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

API#

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: Unknown; Subject type: Unknown. Subjects: 185; recordings: 185; tasks: 1.

Parameters:

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

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

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

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

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query#

Merged query with the dataset filter applied.

Type:

dict

records#

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005398 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005398 DOI: https://doi.org/10.18112/openneuro.ds005398.v1.0.1

Examples

>>> from eegdash.dataset import DS005398
>>> dataset = DS005398(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
__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#