DS004118#

BCIT Calibration Driving

Access recordings and metadata through EEGDash.

Citation: Jonathan Touryan (data and curation), Greg Apker (data), Brent Lance (data), Scott Kerick (data), Anthony Ries (data), Kaleb McDowell (data), Tony Johnson (curation), Kay Robbins (curation) (2022). BCIT Calibration Driving. 10.18112/openneuro.ds004118.v1.0.1

Modality: eeg Subjects: 156 Recordings: 2294 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004118

dataset = DS004118(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 = DS004118(cache_dir="./data", subject="01")

Advanced query

dataset = DS004118(
    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{ds004118,
  title = {BCIT Calibration Driving},
  author = {Jonathan Touryan (data and curation) and Greg Apker (data) and Brent Lance (data) and Scott Kerick (data) and Anthony Ries (data) and Kaleb McDowell (data) and Tony Johnson (curation) and Kay Robbins (curation)},
  doi = {10.18112/openneuro.ds004118.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004118.v1.0.1},
}

About This Dataset#

BCIT Calibration Driving

Introduction

Overview: The Calibration Driving study was intended to provide calibration data for applying fatigue-based driver performance prediction algorithms. Calibration data sets were designed to be

View full README

BCIT Calibration Driving

Introduction

Overview: The Calibration Driving study was intended to provide calibration data for applying fatigue-based driver performance prediction algorithms. Calibration data sets were designed to be the first component of every recording session within the BCIT program, which featured multiple studies investigating fatigue.

Collectively, the Calibration Driving recordings comprise a ‘virtual’ study, in which driving performance at the calibration level can be analyzed. When analyzed with other same-subject data, involving much longer tasks, the calibration data sets can be used as the basis for non-fatigue state performance.

Further information is available on request from cancta.net_.

The task was performed using identical systems at three different sites:

Army Research Laboratory, Aberdeen MD (T1)
Teledyne Corporation, Durham, NC (T2)
Science Applications International Corporation (SAIC), Louisville, CO (T3)

All sites used identical driving simulator setups.

The data collected at site T1 used a 64-channel Biosemi EEG headset as did the data collected at site T2, while site T3 used a 256-channel Biosemi EEG headset.

Data from site T1 has legacy subject IDs in the range 1000 to 1999. Data from site T2 has legacy subject IDs in the range 2000 to 2999. Data from site T3 has legacy subject IDs in the range 3000 to 3999. Legacy subject IDs are unique across the entire BCIT program.

Methods

Subjects: Subjects at Aberdeen Proving Grounds were recruited, on a voluntary basis from among the scientists and engineers working at APG.

Subjects recruited by Teledyne and SAIC were found via advertising and community outreach efforts, and primarily consisted of local college students.

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 256 (+8) channel systems with 4 eye and 2 mastoid channels recorded; SR=1024 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: The Calibration study featured a 15-minute trial, requiring the driver to control the steering of a simulated vehicle on a long, straight road in a visually sparse environment.

With the vehicle speed controlled by the driving simulator, the only task for the subject was to maintain the vehicle position in the center of the lane. 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: None.

Dependent variables: Reaction times to perturbations, continuous performance based on vehicle log (steering wheel angle, lane position, heading error, etc.), 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 Locations: Army Research Laboratory, Aberdeen MD (site T1); Teledyne Corporation, Durham, NC (site T2); Science Applications International Corporation (SAIC), Louisville, CO (site T3).

Note: This 15-minute task was performed prior to every run in the BCIT experimental series. Thus, the runs have corresponding runs in one or more of BCIT Advanced Guard Duty (ds004106), BCIT Basic Guard Duty (ds004119), BCIT Baseline Driving (ds004120), BCIT Mind Wandering (ds004121), BCIT Speed Control (ds004122) and Traffic Complexity (ds004123) that were conducted on the same subject during the same session. The Calibration Driving run was always conducted first.

Dataset Information#

Dataset ID	`DS004118`
Title	BCIT Calibration Driving
Year	2022
Authors	Jonathan Touryan (data and curation), Greg Apker (data), Brent Lance (data), Scott Kerick (data), Anthony Ries (data), Kaleb McDowell (data), Tony Johnson (curation), Kay Robbins (curation)
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004118.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: 156
Recordings: 2294
Tasks: 1

Channels & sampling rate

Channels: 256 (128), 266 (128), 64 (119), 74 (119)
Sampling rate (Hz): 1024.0 (452), 2048.0 (42)
Duration (hours): 0.0

API Reference#

Use the DS004118 class to access this dataset programmatically.

class eegdash.dataset.DS004118(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004118. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 156; recordings: 247; 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/ds004118 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004118

Examples

>>> from eegdash.dataset import DS004118
>>> dataset = DS004118(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