eegdash.dataset.DS004551#

participants.tsv (OpenNeuro ds004551). Access recordings and metadata through EEGDash.

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

Dataset Information#

Dataset ID

DS004551

Title

participants.tsv

Year

2023

Authors

Kazuki Sakakura, Naoto Kuroda, Masaki Sonoda, Takumi Mitsuhashi, Ethan Firestone, Aimee F. Luat, Neena I. Marupudi, Sandeep Sood, Eishi Asano

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004551.v1.0.6

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004551,
  title = {participants.tsv},
  author = {Kazuki Sakakura and Naoto Kuroda and Masaki Sonoda and Takumi Mitsuhashi and Ethan Firestone and Aimee F. Luat and Neena I. Marupudi and Sandeep Sood and Eishi Asano},
  doi = {10.18112/openneuro.ds004551.v1.0.6},
  url = {https://doi.org/10.18112/openneuro.ds004551.v1.0.6},
}

Highlights#

Subjects & recordings

  • Subjects: 0

  • Recordings: 0

  • Tasks: 0

Channels & sampling rate

  • Channels: 120, 128 (82), 110 (2), 106, 102 (2), 122 (2), 112 (5), 116, 118 (3), 148 (2), 130 (2), 136, 138 (3), 124 (2), 58, 96, 144 (2), 108 (2), 126, 142 (2), 132, 134 (2), 104 (2), 146, 84

  • Sampling rate (Hz): 1000.0

  • 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.ds004551.v1.0.6

Provenance

Quickstart#

Install

pip install eegdash

Load a recording

from eegdash.dataset import DS004551

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

Filter/query

dataset = DS004551(cache_dir="./data", subject="01")

dataset = DS004551(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Quality & caveats#

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

API#

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: Unknown; Subject type: Unknown. Subjects: 114; recordings: 125; tasks: 1.

Parameters:

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

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

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

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

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query#

Merged query with the dataset filter applied.

Type:

dict

records#

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

Type:

list[dict] | None

Notes

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

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004551 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004551 DOI: https://doi.org/10.18112/openneuro.ds004551.v1.0.6

Examples

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