Multi-channel EEG recordings during a sustained-attention driving task

Cao, Zehong; Chuang, Chun-Hsiang; King, Jung-Kai; Lin, Chin-Teng · 2019 · Crossref

DOI: 10.1038/s41597-019-0027-4

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Summary

This paper describes a publicly available dataset of multi-channel electroencephalography (EEG) recordings collected during a 90-minute sustained-attention driving task. The study addresses the need for high-quality neural data to develop computational methods for detecting driving fatigue and drowsiness, which are critical risk factors in road traffic accidents. The dataset is intended to support the neuroscience and brain-computer interface communities in analyzing brain cortical dynamics and behavioral performance under cognitive stress. The experimental design involved 27 participants (ages 22–28) who completed a total of 62 sessions in a virtual-reality (VR) driving simulator mounted on a six-degree-of-freedom Stewart motion platform. The simulation depicted a night-time drive on a straight, four-lane highway with no other traffic. Participants were instructed to keep the vehicle centered in the lane. Randomly induced lane-departure events caused the car to drift left or right; participants responded by steering the wheel to return to the center lane. Each trial consisted of a deviation onset, response onset, and response offset, with subsequent trials occurring 5–10 seconds later. EEG data were recorded simultaneously using a 32-channel system (30 active electrodes plus 2 mastoid references) at a sampling rate of 500 Hz. The dataset includes both raw and pre-processed files, with pre-processing involving 1-Hz high-pass and 50-Hz low-pass filtering and artifact rejection. The dataset comprises 81,576 total events across all sessions, with reaction times (RT) serving as an objective behavioral measure of fatigue. RTs were categorized into optimal, suboptimal, and poor performance groups to characterize varying levels of driver arousal. Technical validation confirmed the reliability of the method through consistent findings with partner institutions, including the University of California at San Diego. The data are accessible via figshare, accompanied by MATLAB code and tutorials for analysis using the EEGLAB toolbox. The significance of this work lies in providing a standardized, large-scale resource for investigating the neurocognitive signatures of sustained attention and fatigue. By linking simultaneous EEG and behavioral data, the dataset enables the development of novel neural processing methodologies and real-time neuroergonomic systems. These tools aim to enhance situational awareness and decision-making for drivers, ultimately improving human-system performance and road safety. The public availability of both raw and pre-processed data, along with detailed usage notes, facilitates reproducibility and further research into drowsiness prediction and cognitive state assessment.

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Topics

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• drowsiness detection algorithms
• vigilance
• drowsiness
• neuro workload indices

Information type

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• Empirical Findings: physiological data
• Methodological Resource: tool software, dataset resource