EEG datasets with different levels of fatigue for personal identification
- Citation Author(s):
- Submitted by:
- Haixian Wang
- Last updated:
- Tue, 05/02/2023 - 08:52
Dataset I: This is the original EEG data of twelve healthy subjects for driver fatigue detection. Due to personal privacy, the digital number represents different participants. The .cnt files were created by a 40-channel Neuroscan amplifier, including the EEG data in two states in the process of driving.
Dataset II: This project adopted an event-related lane-departure paradigm in a virtual-reality (VR) dynamic driving simulator to quantitatively measure brain EEG dynamics along with the fluctuation of task performance throughout the experiment.
All subjects were required to have driving license. None of the participants had a history of psychological disorders. All participants were instructed to sustain their attention to perform the task during the experiment, and the 32-ch EEG signals and the vehicle position were recorded simultaneously.
Prior to the experiment, all participants completed a consent form stating their clear understanding of the experimental protocol which had been approved by Institutional Review Broad of Taipei Veterans General Hospital, Taiwan.
All subjects participated in the sustained-attention driving experiment for 1.5 hours in the afternoon (13:00-14:00) after lunch, and all of them were asked to keep their attention focused on driving during the entire period. There was no break or resting session. At the beginning of the experiment (without any recordings), a five-minute pre-test was performed to ensure that every subject understood the instructions and they did not suffer from simulator-induced nausea. To investigate the effect of kinesthesia on brain activity in the sustained-attention driving task, each subject was asked to participate at least two driving sessions on different days. Each session lasted for about 90 min. One was the driving session with a fixed-based simulator but with no kinesthetic feedback, so subject had to monitor the vehicle deviation visually from the virtual scene.The other driving session involved a motion-based simulator with a six degree-of-freedom Stewart platform to simulate the dynamic response of the vehicle to the deviation event or steering. The visual and kinesthetic inputs together aroused the subject to attend to the deviation event and take action to correct the driving trajectory Data Requirement.
A wired EEG cap with 32 Ag/AgCl electrodes, including 30 EEG electrodes and two reference electrodes (opposite lateral mastoids) was used to record the electrical activity of the brain from the scalp during the driving task. The EEG electrodes were placed according to a modified international 10-20 system. The contact impedance between all electrodes and the skin was kept <5kΩ. The EEG recordings and the vehicle trajectory amplified by Scan SynAmps2 Express system (Compumedics Ltd., VIC, Australia) were digitized at 500 Hz (resolution: 16 bits) simultaneously.
- Dataset II.zip (15.27 GB)
- Dataset I.zip (522.66 MB)
|Driver fatigue detection through mutiple entropy fusion analysis in an EEG-based system.pdf||44.31 MB|