DS004105: eeg dataset, 17 subjects#
BCIT Auditory Cueing
Citation: Javier Garcia (data), Justin Brooks (data), Scott Kerick (data), Tony Johnson (data and curation), Tim Mullen (data), Jean Vettel (data), Jonathan Touryan (curation), Kay Robbins (curation) (20). BCIT Auditory Cueing. 10.18112/openneuro.ds004105.v1.0.0
17-participant EEG dataset — BCIT Auditory Cueing.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004105
dataset = DS004105(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004105(cache_dir="./data", subject="01")
Advanced query
dataset = DS004105(
cache_dir="./data",
query={"subject": {"$in": ["01", "02"]}},
)
Iterate recordings
for rec in dataset:
print(rec.subject, rec.raw.info['sfreq'])
If you use this dataset in your research, please cite the original authors.
BibTeX
@dataset{ds004105,
title = {BCIT Auditory Cueing},
author = {Javier Garcia (data) and Justin Brooks (data) and Scott Kerick (data) and Tony Johnson (data and curation) and Tim Mullen (data) and Jean Vettel (data) and Jonathan Touryan (curation) and Kay Robbins (curation)},
doi = {10.18112/openneuro.ds004105.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004105.v1.0.0},
}
About This Dataset#
Overview: Subjects in the Auditory Cueing study performed a long-duration simulated driving task with
perturbations and audio stimuli in a visually sparse environment.
The purpose of this effort was to supplement and extend the related driving research to collect
prolonged time-on-task measurements of subjects performing a driving task in a simulated environment in order to assess fatigue-based performance through novel biomarkers.
Introduction
Similar to the Baseline Driving study, the Auditory Cueing study was intended to identify periods of driver fatigue via predictive algorithms formulated from the analysis of driver EEG data, in comparison to the objective performance measures, and in contrast with the (non-fatigued) Calibration driving session for the subject. Auditory Cueing extended the Baseline Driving paradigm by adding predictive and non-predictive (random) pre-perturbation onset audio cues and increasing the frequency and magnitude of perturbation events vs. baseline driving.
Further information is available on request from cancta.net.
View full README
Introduction
Similar to the Baseline Driving study, the Auditory Cueing study was intended to identify periods of driver fatigue via predictive algorithms formulated from the analysis of driver EEG data, in comparison to the objective performance measures, and in contrast with the (non-fatigued) Calibration driving session for the subject. Auditory Cueing extended the Baseline Driving paradigm by adding predictive and non-predictive (random) pre-perturbation onset audio cues and increasing the frequency and magnitude of perturbation events vs. baseline driving.
Further information is available on request from cancta.net.
Methods
Subjects: Volunteers from the local community recruited through advertisements. Apparatus: Driving simulator with steering wheel and brake / foot pedals (Real Time Technologies; Dearborn, MI); Video Refresh Rate (VRR) = 900 Hz; Vehicle data log file Sampling Rate (SR) = 100 Hz); EEG (BioSemi 64 (+8) channel systems with 4 eye and 2 mastoid channels recorded; SR=2048 Hz); Eye Tracking (Sensomotoric Instruments (SMI); REDEYE250). Initial setup: Upon arrival to the lab, subjects were given an introduction to the primary study for which they were recruited and provided informed consent and provided demographics information.
This was followed by a practice session, to acclimate the subject to the driving simulator. The driving practice task lasted 10-15 min, until asymptotic performance in steering and speed control was demonstrated and lack of motion sickness was reported.
Subjects were then outfitted and prepped for eye tracking and EEG acquisition. Task organization within the study: Subjects always began recording sessions by performing a Calibration Driving task, which was a 15-minute drive where the subject controlled only the steering (and speed was controlled by the simulator). Following this, subjects would perform Auditory Cueing condition A and Auditory Cueing condition B, with counter-balancing used across subjects as to which of them came first. This study only contains the Auditory Cueing portion of the study. Auditory cueing task details: Auditory Cueing A was 45 minutes of continuous driving, with subjects responsible for steering and maintaining speed, while a tone was played periodically at random.
Auditory Cueing B was similar, but the tones were correlated with the onset of a perturbation event. Both driving tasks were conducted on the same simulated long, straight road.
In each case, the subject was instructed to stay within the boundaries of the right-most lane, and to drive at the posted speed limits.
The vehicle was periodically subject to lateral perturbing forces, which could be applied to either side of the vehicle, pushing the vehicle out of the center of the lane; and the subject was instructed to execute corrective steering actions to return the vehicle to the center of the lane. Independent variables: Auditory Cue (randomly presented before perturbation vs. predictive) Dependent variables: Reaction times to perturbations, continuous performance based on vehicle log (steering wheel angle, lane position, heading error, etc.), reaction times to target vehicles (police), Task-Induced Fatigue Scale (TIFS), Karolinska Sleepiness Scale (KSS), Visual Analog Scale of Fatigue (VAS-F).
Note: Questionnaire data is available upon request from cancta.net. Additional data acquired: Participant Enrollment Questionnaire, Subject Questionnaire for Current Session, Simulator Sickness Questionnaire. Experimental Location: Teledyne Corporation, Durham, NC. Note: This dataset has a corresponding dataset in the BCIT Calibration Driving ds004118 which has the 15 minute driving task performed prior to this one.
Cohort#
Dataset Statistics#
Channel counts: 74 ch (n=34 recordings)
Sampling frequencies: 1024.0 Hz (n=34 recordings)
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · ses-01 · task-DriveRandomSound · run-1
Showing one representative recording out of
17 subjects and 34 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<url>) to inspect it.
Electrode layout — EEG · 64 sensors — 64 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
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.
Full dataset metadata table
Dataset ID |
|
Title |
BCIT Auditory Cueing |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Javier Garcia (data), Justin Brooks (data), Scott Kerick (data), Tony Johnson (data and curation), Tim Mullen (data), Jean Vettel (data), Jonathan Touryan (curation), Kay Robbins (curation) |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004105,
title = {BCIT Auditory Cueing},
author = {Javier Garcia (data) and Justin Brooks (data) and Scott Kerick (data) and Tony Johnson (data and curation) and Tim Mullen (data) and Jean Vettel (data) and Jonathan Touryan (curation) and Kay Robbins (curation)},
doi = {10.18112/openneuro.ds004105.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004105.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004105 · Garcia2022eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004105(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
BCIT Auditory Cueing
- Study:
ds004105(OpenNeuro)- Author (year):
Garcia2022- Canonical:
—
Also importable as:
DS004105,Garcia2022.Modality:
eeg; Experiment type:Attention; Subject type:Healthy. Subjects: 17; recordings: 34; 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/ds004105 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004105 DOI: https://doi.org/10.18112/openneuro.ds004105.v1.0.0 NEMAR citation count: 0
Examples
>>> from eegdash.dataset import DS004105 >>> dataset = DS004105(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.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds004105").huggingface
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 ds004105 to reproduce the tutorial on this dataset.
Citation
Javier Garcia (data), Justin Brooks (data), Scott Kerick (data), Tony Johnson (data and curation), Tim Mullen (data), … (20). BCIT Auditory Cueing. 10.18112/openneuro.ds004105.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004105.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset