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Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

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Phase lag index-based graph attention networks for detecting driving fatigue.

Zhongmin Wang¹, Yupeng Zhao¹, Yan He¹

¹School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China.

The Review of Scientific Instruments

|October 2, 2021

Summary

Detecting driving fatigue is crucial. This study introduces a novel method using electroencephalogram (EEG) signals and a graph attention network (GAT) to identify fatigue, showing enhanced brain network connectivity during tired states.

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Area of Science:

Neuroscience
Biomedical Engineering
Artificial Intelligence

Background:

Driving fatigue poses significant safety risks.
Understanding brain network alterations during fatigue is essential for developing effective monitoring systems.
Electroencephalogram (EEG) signals offer a viable measure of brain activity.

Purpose of the Study:

To propose and validate a novel method for detecting driving fatigue using EEG signals.
To investigate the patterns of functional brain connectivity associated with driving fatigue.
To leverage graph attention networks (GAT) for improved fatigue detection accuracy.

Main Methods:

Utilizing the phase lag index (PLI) to construct functional brain networks from multi-channel EEG data.
Modeling EEG time-frequency features as graph data.
Employing a graph attention network (GAT) for fatigue state recognition, incorporating an attention mechanism for adaptive feature weighting.

Main Results:

The proposed PLI-GAT method achieved an accuracy of 85.53% in recognizing driving fatigue states on the SEED-VIG dataset.
Analysis revealed significantly enhanced functional connectivity among different brain channels during fatigue.
The GAT model demonstrated superior expressiveness compared to traditional graph neural networks.

Conclusions:

The PLI-GAT method provides an effective approach for driving fatigue detection using EEG signals.
Altered functional brain connectivity is a key indicator of driving fatigue.
Graph attention networks show promise for advanced brain-computer interface applications in monitoring cognitive states.