eegdash.dataset.DS004819#

participants.tsv (OpenNeuro ds004819). Access recordings and metadata through EEGDash.

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

Dataset Information#

Dataset ID

DS004819

Title

participants.tsv

Year

2023

Authors

Keundong Lee, Angelique C. Paulk, Yun Goo Ro, Daniel R. Cleary, Karen J. Tonsfeldt, Yoav Kfir, John Pezaris, Youngbin Tchoe, Jihwan Lee, Andrew M. Bourhis, Ritwik Vatsyayan, Joel R. Martin, Samantha M. Russman, Jimmy C. Yang, Amy Baohan, R. Mark Richardson, Ziv M. Williams, Shelley I. Fried, Hoi Sang U, Ahmed M. Raslan, Sharona Ben-Haim, Eric Halgren, Sydney S. Cash, Shadi. A. Dayeh

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004819.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004819,
  title = {participants.tsv},
  author = {Keundong Lee and Angelique C. Paulk and Yun Goo Ro and Daniel R. Cleary and Karen J. Tonsfeldt and Yoav Kfir and John Pezaris and Youngbin Tchoe and Jihwan Lee and Andrew M. Bourhis and Ritwik Vatsyayan and Joel R. Martin and Samantha M. Russman and Jimmy C. Yang and Amy Baohan and R. Mark Richardson and Ziv M. Williams and Shelley I. Fried and Hoi Sang U and Ahmed M. Raslan and Sharona Ben-Haim and Eric Halgren and Sydney S. Cash and Shadi. A. Dayeh},
  doi = {10.18112/openneuro.ds004819.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004819.v1.0.0},
}

Highlights#

Subjects & recordings

  • Subjects: 0

  • Recordings: 0

  • Tasks: 0

Channels & sampling rate

  • Channels: 64

  • Sampling rate (Hz): Unknown

  • 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.ds004819.v1.0.0

Provenance

Quickstart#

Install

pip install eegdash

Load a recording

from eegdash.dataset import DS004819

dataset = DS004819(cache_dir="./data")
recording = dataset[0]
raw = recording.load()

Filter/query

dataset = DS004819(cache_dir="./data", subject="01")

dataset = DS004819(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Quality & caveats#

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

API#

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 DOI: https://doi.org/10.18112/openneuro.ds004819.v1.0.0

Examples

>>> from eegdash.dataset import DS004819
>>> dataset = DS004819(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
__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#