eegdash.dataset.DS004973#

An fNIRS dataset for driving risk cognition of passengers in highly automated driving scenarios (OpenNeuro ds004973). Access recordings and metadata through EEGDash.

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

Dataset Information#

Dataset ID

DS004973

Title

An fNIRS dataset for driving risk cognition of passengers in highly automated driving scenarios

Year

Unknown

Authors

Xiaofei Zhang, Qiaoya Wang, Jun Li, Xiaorong Gao, Bowen Li, Bingbing Nie, Jianqiang Wang, Ziyuan Zhou, Yingkai Yang, Hong Wang

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004973.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004973,
  title = {An fNIRS dataset for driving risk cognition of passengers in highly automated driving scenarios},
  author = {Xiaofei Zhang and Qiaoya Wang and Jun Li and Xiaorong Gao and Bowen Li and Bingbing Nie and Jianqiang Wang and Ziyuan Zhou and Yingkai Yang and Hong Wang},
  doi = {10.18112/openneuro.ds004973.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004973.v1.0.1},
}

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.ds004973.v1.0.1

Provenance

Quickstart#

Install

pip install eegdash

Load a recording

from eegdash.dataset import DS004973

dataset = DS004973(cache_dir="./data")
recording = dataset[0]
raw = recording.load()

Filter/query

dataset = DS004973(cache_dir="./data", subject="01")

dataset = DS004973(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Quality & caveats#

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

API#

class eegdash.dataset.DS004973(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004973. Modality: fnirs; Experiment type: Unknown; Subject type: Highly automated driving  vehicles. Subjects: 21; recordings: 1177; tasks: 12.

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/ds004973 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004973 DOI: https://doi.org/10.18112/openneuro.ds004973.v1.0.1

Examples

>>> from eegdash.dataset import DS004973
>>> dataset = DS004973(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#