EEGdash›OpenNeuro›DS004973

Iss. 4973 · 20 subjects · 222 recordings · CC0

Dataset Brief · An fNIRS dataset for driving risk cognition of passengers in…

DS004973: fnirs dataset, 20 subjects#

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

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

20-participant fNIRS dataset — An fNIRS dataset for driving risk cognition of passengers in highly automated driving scenarios.

Data & curation Xiaofei Zhang · Qiaoya Wang · Jun Li · Xiaorong Gao · Bowen Li · Bingbing Nie · …

fNIRS · 16 ch50 HzBIDS 1.7.012 tasksHealthyVisualAttention

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

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},
}

§ 02Study · The README

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.

doi:10.18112/openneuro.ds004973.v1.0.0

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution (n=20, range 21–46 yr, mean 29.6 yr · sex per subject not reported)

2025304045

Channel counts: 16 ch (n=222 recordings)

Sampling frequencies: 50.0 Hz (n=222 recordings)

Total recording duration: 50 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 16 ch · fNIRS · 50 Hz · 20 subjects, 222 recordings

Electrode layout — fNIRS · 10 sensors — 10 channels

NEMAR Processing Statistics#

The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.

HED event descriptors word cloud

§ 05Manifest · BIDS tree

Manifest#

File Explorer#

Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.DS004973(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Zhang2024_driving_risk_cognition

Canonical—

Importable asDS004973 · Zhang2024_driving_risk_cognition

Sourceeegdash/dataset/registry.py · [source ↗]

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

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

Study:: ds004973 (OpenNeuro)
Author (year):: Zhang2024_driving_risk_cognition
Canonical:: —

Also importable as: DS004973, Zhang2024_driving_risk_cognition.

Modality: fnirs; Experiment type: Attention; Subject type: Healthy. Subjects: 20; recordings: 222; 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: str, overwrite: bool = False, offset: int = 0)[source]#

Save datasets to files by creating one subdirectory for each dataset:

path/
    0/
        0-raw.fif | 0-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
    1/
        1-raw.fif | 1-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)

Parameters:

path (str) –

Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FacePre-bundled mirror at EEGDash/ds004973 · pull with datasets.load_dataset("EEGDash/ds004973").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS004973.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.

Load one EEG recordingResolve a single record to an MNE Raw with channels and events.

EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.

Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.

Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.

Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for ds004973 to reproduce the tutorial on this dataset.

Citation

Xiaofei Zhang, Qiaoya Wang, Jun Li, Xiaorong Gao, Bowen Li, … (n.d.). An fNIRS dataset for driving risk cognition of passengers in highly automated driving scenarios. 10.18112/openneuro.ds004973.v1.0.1

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds004973.v1.0.1.

Related & sibling datasets

DS004830FNIRS · 12 subj DS006459FNIRS · 17 subj DS006460FNIRS · 17 subj DS004841EEG · 20 subj DS007463FNIRS · 8 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.7.0

Sidecars

not yet probed

Provenance

CC0 · 10.18112/openneuro.ds004973.v1.0.1

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS004973`
Title	An fNIRS dataset for driving risk cognition of passengers in highly automated driving scenarios
Author (year)	`Zhang2024_driving_risk_cognition`
Canonical	—
Importable as	`DS004973`, `Zhang2024_driving_risk_cognition`
Year	—
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

DS004973: fnirs dataset, 20 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#