eegdash.dataset.DS005383#

participants.tsv (OpenNeuro ds005383). Access recordings and metadata through EEGDash.

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

Dataset Information#

Dataset ID

DS005383

Title

participants.tsv

Year

2024

Authors

Yanru Bai, Qi Tang, Ran Zhao, Hongxing Liu, Mingkun Guo, Shuming Zhang, Minghan Guo, Junjie Wang, Changjian Wang, Mu Xing, Guangjian Ni, Dong Ming

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds005383.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds005383,
  title = {participants.tsv},
  author = {Yanru Bai and Qi Tang and Ran Zhao and Hongxing Liu and Mingkun Guo and Shuming Zhang and Minghan Guo and Junjie Wang and Changjian Wang and Mu Xing and Guangjian Ni and Dong Ming},
  doi = {10.18112/openneuro.ds005383.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005383.v1.0.0},
}

Highlights#

Subjects & recordings

  • Subjects: 0

  • Recordings: 0

  • Tasks: 0

Channels & sampling rate

  • Channels: 30

  • Sampling rate (Hz): 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.ds005383.v1.0.0

Provenance

Quickstart#

Install

pip install eegdash

Load a recording

from eegdash.dataset import DS005383

dataset = DS005383(cache_dir="./data")
recording = dataset[0]
raw = recording.load()

Filter/query

dataset = DS005383(cache_dir="./data", subject="01")

dataset = DS005383(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Quality & caveats#

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

API#

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: Unknown; Subject type: Unknown. 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 DOI: https://doi.org/10.18112/openneuro.ds005383.v1.0.0

Examples

>>> from eegdash.dataset import DS005383
>>> dataset = DS005383(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#