DS004973#

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

Access recordings and metadata through EEGDash.

Citation: Xiaofei Zhang, Qiaoya Wang, Jun Li, Xiaorong Gao, Bowen Li, Bingbing Nie, Jianqiang Wang, Ziyuan Zhou, Yingkai Yang, Hong Wang (2024). An fNIRS dataset for driving risk cognition of passengers in highly automated driving scenarios. 10.18112/openneuro.ds004973.v1.0.1

Modality: fnirs Subjects: 20 Recordings: 222 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004973

dataset = DS004973(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

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

Advanced query

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

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

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},
}

About This Dataset#

The fNIRS dataset focus on the prefrontal cortex activity in fourteen types of highly automated driving scenarios, which considers age, sex and driving experience factors, and contains the data of an 8-channel fNIRS device and driving scenarios. A total of 20 participants completed this driving simulator experiment，and each participant need to finish 12 tasks. Their ages range from 21 to 46 years old, with 5 females and 15 males.: Our objective is to provides the data support for finding the difference of prefrontal cortex activity between low-risk and high-risk episodes by quantifying the risk of driving scenarios. This research may provide a solution to prevent potential hazard and improve SOTIF based on brain-computer interface technology and fNIRS, in the future.

Notes

Here a total of 240 tasks which are need to be completed by 20 participants，and the data about 20 tasks is removed because the data are not recorded correctly.
We update the results of subjective evaluations about dangerous degree of VTD segment, and they are shown in the file “participants.tsv”.

How to cite?

doi:10.18112/openneuro.ds004973.v1.0.0

Dataset Information#

Dataset ID	`DS004973`
Title	An fNIRS dataset for driving risk cognition of passengers in highly automated driving scenarios
Year	2024
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

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 20
Recordings: 222
Tasks: 12

Channels & sampling rate

Channels: 16
Sampling rate (Hz): 50.0
Duration (hours): 0.0

API Reference#

Use the DS004973 class to access this dataset programmatically.

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: Attention; Subject type: Healthy. 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

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