eegdash.dataset.DS004166#

participants.tsv (OpenNeuro ds004166). Access recordings and metadata through EEGDash.

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

Dataset Information#

Dataset ID

DS004166

Title

participants.tsv

Year

2022

Authors

Yang Li (data and curation), Wenjin Fu (data), Qiumei Zhang (data), Xiongying Chen (data), Xiaohong Li (data), Boqi Du (data), Xiaoxiang Deng (data), Feng Ji (curation), Qi Dong (curation), Feng Ji (curation), Susanne M. Jaeggi (curation), Chuansheng Chen (curation), Jun Li (data and curation)

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004166.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004166,
  title = {participants.tsv},
  author = {Yang Li (data and curation) and Wenjin Fu (data) and Qiumei Zhang (data) and Xiongying Chen (data) and Xiaohong Li (data) and Boqi Du (data) and Xiaoxiang Deng (data) and Feng Ji (curation) and Qi Dong (curation) and Feng Ji (curation) and Susanne M. Jaeggi (curation) and Chuansheng Chen (curation) and Jun Li (data and curation)},
  doi = {10.18112/openneuro.ds004166.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004166.v1.0.0},
}

Highlights#

Subjects & recordings

  • Subjects: 0

  • Recordings: 0

  • Tasks: 0

Channels & sampling rate

  • Channels: Unknown

  • 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.ds004166.v1.0.0

Provenance

Quickstart#

Install

pip install eegdash

Load a recording

from eegdash.dataset import DS004166

dataset = DS004166(cache_dir="./data")
recording = dataset[0]
raw = recording.load()

Filter/query

dataset = DS004166(cache_dir="./data", subject="01")

dataset = DS004166(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Quality & caveats#

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

API#

class eegdash.dataset.DS004166(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004166. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 71; recordings: 213; 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/ds004166 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004166 DOI: https://doi.org/10.18112/openneuro.ds004166.v1.0.0

Examples

>>> from eegdash.dataset import DS004166
>>> dataset = DS004166(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#