Source-linked AI summary
Multi-channel EEG recordings during a sustained-attention driving task
Zehong Cao, Chun-Hsiang Chuang, Jung-Kai King, Chin-Teng Lin
TL;DR
Fatigue and drowsiness impair driving performance, while evidence linking sustained-attention behaviour with brain dynamics remains limited. The paper presents a publicly available EEG dataset collected during a 90-minute VR lane-departure driving task, supporting analysis of behavioural and neurocognitive changes related to driver arousal, fatigue, and vigilance.
Problem
Fatigue and drowsiness impair driver awareness, recognition, directional control, arousal, and information processing during sustained-attention driving.
Method
The study records 32-channel EEG, vehicle position, and event timing from 27 participants during a 90-minute VR lane-departure driving task.
Results
The publicly accessible dataset supports simultaneous analysis of brain, behavioural, sensory, and performance dynamics in sustained-attention driving.
Takeaways & Limitations
The dataset can be reused to study driver decision making, arousal, fatigue, and vigilance and to explore real-time neuroergonomic systems.
Abstract
from arXiv · showhide
We described driver behaviour and brain dynamics acquired from a 90-minute sustained-attention task in an immersive driving simulator. The data include 62 copies of 32 channel electroencephalography (EEG) data for 27 subjects that drove on a four lane highway and were asked to keep the car cruising in the centre of the lane. Lane departure events were randomly induced to make the car drift from the original cruising lane towards the left or right lane. A complete trial includes events with deviation onset, response onset, and response offset. The next trial, in which the subject has to drive back to the original cruising lane, occurs from 5 to 10 seconds after finishing the current trial. We hope that this dataset will lead to the development of novel neural processing assays that can be used to index brain cortical dynamics and detect driving fatigue and drowsiness. This publicly available dataset is beneficial to the neuroscientific and brain computer interface communities.
Title · Affiliations · Background & Summary
The paper presents a 32-channel EEG dataset collected during a sustained-attention driving task in virtual reality, motivated by fatigue-related safety risks and intended to support analyses of driver behaviour and brain dynamics. It combines simultaneous EEG and vehicle-position measurements to enable reuse for studying interactions among brain, behavioural, sensory, and performance dynamics.
- Title: The paper is titled “Multi-channel EEG recordings during a sustained-attention driving task” and is authored by Zehong Cao, Chun-Hsiang Chuang, Jung-Kai King, and Chin-Teng Lin.
- Affiliations: The authors are affiliated with the CI-BCI Lab at the University of Technology Sydney and the Brain Research Centre at National Chiao Tung University.
- Background & Summary: Fatigue and drowsiness increase crash risk by suppressing awareness, recognition, directional control, arousal, and information processing in unusual or emergency situations.
- Background & Summary: During sustained-attention driving, fatigue and drowsiness are reflected in driver behaviour and brain dynamics, motivating EEG-based investigation in naturalistic movement tasks.
- Background & Summary: The research program applies statistical modelling and data visualisation to extract neurocognitive-performance signatures and assess drivers’ neurocognitive state and performance individually.
- Background & Summary: The dataset uses an event-related lane-departure paradigm in a virtual-reality dynamic driving simulator to measure EEG dynamics and behavioural-performance fluctuations quantitatively.
- Background & Summary: The experiment simultaneously recorded 32-channel EEG signals and vehicle position from licensed participants without histories of psychological disorders.
- Background & Summary: The dataset supports research on kinaesthetic effects, mind-wandering trends, and drowsiness prediction, while enabling joint analysis of brain, behavioural, sensory, and performance dynamics.
Methods · Participants · Virtual-reality driving environment
The study recruited 27 participants for a 90-minute sustained-attention driving task and collected 62 EEG datasets. Participants drove in a controlled virtual-reality highway environment designed to emulate realistic, monotonous night-time cruising.
- Participants: The study collected 62 EEG datasets from the 27 participants during the driving task.The recordings were acquired across the participants’ experimental sessions.
- Participants: Participants maintained regular sleep and work schedules, slept approximately 8 hours nightly, and avoided late nights for one week before testing.They also avoided alcohol, caffeinated drinks, and strenuous exercise the day before experiments.
- Participants: A pre-test explained the instructions and confirmed that participants did not experience simulator-induced nausea.The study followed recommendations in the Guide for Committee of Laboratory Care and used pre-experiment screening.
- Virtual-reality driving environment: The virtual-reality setup used a dynamic driving simulator mounted on a six-degree-of-freedom Stewart motion platform.Six network-synchronized highway scenes were projected at multiple viewing angles to provide a nearly complete 360° visual field.
- Virtual-reality driving environment: The scenario depicted a visually monotonous night-time drive on a straight four-lane divided highway without other traffic.Lane width was 60 units, and the scene refresh rate emulated cruising at 100 km/hr.
Experimental paradigm · Tutorial and code availability
The study used a VR-based, event-related lane-departure paradigm to measure steering responses during simulated night-time highway driving. A 59-page tutorial and MATLAB code for EEG preprocessing and analysis are publicly available through figshare.
- Experimental paradigm: The VR-based event-related paradigm quantitatively measured subjects’ reaction times to perturbations during continuous driving.It was implemented with WorldToolKit R9 Direct and Visual C++.
- Experimental paradigm: Participants drove at night on a simulated four-lane highway and were instructed to keep the car cruising in the centre of the lane.The simulation used a VR-based driving simulator.
- Experimental paradigm: Randomly induced lane departures made the car drift from its original cruising lane toward the left or right sides.These perturbations were termed deviation onset events.
- Experimental paradigm: Participants compensated for each perturbation by steering the wheel and letting the car return to the original cruising lane.They did not control the accelerator or brake pedals during the experiment.
- Experimental paradigm: Each lane-departure event was defined as a trial comprising baseline, deviation onset, response onset, and response offset periods.EEG signals were recorded simultaneously during the task.
Data Records · Data recording and storage · EEG signals
The dataset synchronizes simulated-vehicle trajectories and event triggers with 32-channel EEG recordings. Files provide EEG signals, vehicle position, electrode metadata, and classified deviation and response events for analysis.
- Data recording and storage: The stimulus computer recorded car trajectories and event time points in a log file while sending synchronized triggers to the Neuroscan EEG acquisition system.The Neuroscan system recorded EEG data with trigger timestamps in an ev2 file; the two files contained different numbers of time points requiring integration.
- Data recording and storage: The recording setup used a Scan SynAmps2 Express system with a wired cap containing 32 Ag/AgCl electrodes.The cap included 30 EEG electrodes and two reference electrodes positioned on opposite lateral mastoids.
- Data recording and storage: Electrodes followed a modified international 10–20 placement system, with contact impedance maintained below 5 kΩ.The passage identifies the Scan SynAmps2 Express system as the EEG amplifier.
- EEG signals: Raw files with set suffixes can be loaded in MATLAB using the EEGLAB toolbox, and the EEG.data variable contains 32 EEG signals plus vehicle position.The first 32 signals correspond to the listed scalp and mastoid electrodes, while the 33rd signal represents simulated vehicle position.
- EEG signals: The first 32 channels are assigned to named electrodes spanning frontal, temporal, central, parietal, and occipital locations, including mastoid references A1 and A2.The electrode list includes Fp1 through O2, with A1 and A2 serving as mastoid references.
- EEG signals: The 33rd signal records vehicle position, describing the simulated vehicle’s location during the driving task.Vehicle position is included alongside the 32 EEG signals in EEG.data.
- EEG signals: Dataset events are classified as deviation onset, response onset, or response offset using event marks 251 or 252, 253, and 254, respectively.These event types are stored in the EEG.event.type field.
Technical Validation · Behavioural validation
Behavioural validation used EEG recordings from 27 university-affiliated adults with normal or corrected-to-normal vision and no reported psychiatric, neurological, or drug-use disorders. Signals were acquired with Ag/AgCl electrodes on a 32-channel Quik-Cap arranged using a modified international 10–20 system, with mastoid references prepared before calibration.
- Behavioural validation: 27 subjects with normal or corrected-to-normal vision participated in the EEG dataset.All participants were university students or staff recruited at National Chiao Tung University, Taiwan.
- Behavioural validation: No subjects reported psychiatric disorders, neurological disease, or drug use disorders.The passage describes these as exclusion-relevant participant characteristics.
- Behavioural validation: Participants were recruited from university students and staff at National Chiao Tung University in Taiwan.This identifies the study population and recruitment setting.
- Behavioural validation: EEG signals were recorded using Ag/AgCl electrodes attached to a 32-channel Quik-Cap.The cap was manufactured by Compumedical NeuroScan.
- Behavioural validation: Thirty electrodes followed a modified international 10–20 arrangement, while two reference electrodes were placed on the mastoid bones.The electrode layout is illustrated in Figure 4-A.
- Behavioural validation: Before calibration, mastoid-reference skin was abraded with Nuprep and disinfected using a 70% isopropyl alcohol swab.The preparation procedure is described for the reference electrodes.
EEG validation · Usage Notes
The dataset requires researcher-applied preprocessing and artifact suppression before EEG analysis, while partner-group consistency supports technical validation of arousal, fatigue, and vigilance estimates. It is publicly downloadable with EEGLAB-compatible tutorials, MATLAB codes, and analysis guidance for reuse.
- EEG validation: Stored EEG data are raw and lack trial-time filtering, baseline correction, and artifact rejection.Researchers should preprocess the recordings before analysis.
- EEG validation: A 1–50 Hz digital bandpass filter followed by down-sampling to 250 Hz is recommended to reduce noise, artifacts, and data volume.These steps are recommended before data analysis.
- EEG validation: Ocular and muscle artifacts can mask cortical signals and bias EEG analyses, making artifact suppression important.Independent component analysis (ICA) is described as a powerful artifact-suppression tool.
- EEG validation: Results shared with UCSD and DCS Corporation were consistent with their findings, supporting technical validation of arousal, fatigue, and vigilance estimates.Validation used changes in behavioral and neurocognitive performance.
- Usage Notes: The project “Multi-channel EEG recordings during a sustained-attention driving task” is publicly downloadable from figshare after signing up.Researchers can download the project to a personal computer.
- Usage Notes: EEGLAB provides MATLAB-based processing for continuous and event-related EEG, including ICA, time/frequency analysis, and artifact rejection.The toolbox includes an interactive graphical interface and tutorials for analysis.
- Usage Notes: A data-analysis tutorial and MATLAB codes are provided as references for preprocessing and analysis of the sustained-attention driving recordings.They are available through the cited figshare DOI and are intended to facilitate dataset reuse.
- Usage Notes: The suggested workflow loads an existing dataset, checks MATLAB workspace variables, extracts event epochs, and performs further analysis.The notes describe variables including sampling rate, channel locations, event types and latencies, and EEG signals.
Competing interests
The authors declare no competing financial interests.
- Z. Cao, C.H. Chuang, J.T. King, and C.T. Lin have no competing financial interests to declare.
Figure Legends · Tables · Data Citations
The data citation identifies the publicly available dataset “Multi-channel EEG recordings during a sustained-attention driving task” and provides its authorship, publication year, repository, fileset status, and DOI URL.
- Data Citations: The dataset is cited as Cao, Chuang, King, and Lin (2018).The citation lists Zehong Cao, Michael Chuang, J.T. King, and Chin-Teng Lin as authors.
- Data Citations: The cited dataset is titled “Multi-channel EEG recordings during a sustained-attention driving task.”
- Data Citations: The dataset is hosted on figshare.
- Data Citations: The citation identifies the resource as a fileset.
- Data Citations: The cited resource is available through the DOI URL https://doi.org/10.6084/m9.figshare.6427334.v2.
Figures
The figures depict the event-related lane-departure paradigm in a virtual-reality driving simulator, simultaneous EEG and behavioural recording, representative performance data, and electrode placement with controlled impedance.
- Figure 1 depicts an event-related lane-departure paradigm in a virtual-reality dynamic driving simulator.
- Figure 2 presents the event-related lane-deviation design and shows that EEG and behaviour were recorded simultaneously.
- Figure 3 provides an example of behavioural performance alongside EEG signals with associated events.
- Figure 4 shows electrode layout and reports that contact impedance between all electrodes and skin remained below 5 kΩ.