Issue |
EPL
Volume 147, Number 4, August 2024
|
|
---|---|---|
Article Number | 41001 | |
Number of page(s) | 6 | |
Section | Statistical physics and networks | |
DOI | https://doi.org/10.1209/0295-5075/ad602f | |
Published online | 22 August 2024 |
A lightweight attention-based multi-frequency topology learning framework for driving fatigue detection
1 School of Artificial Intelligence, Taingang University - Tainjin 300387, China
2 School of Electrical and Information Engineering, Tianjin University - Tianjin 300072, China
3 Institute for Complex Systems and Mathematical Biology, King's College, University of Aberdeen Aberdeen AB24 3UE, UK
Received: 30 March 2024
Accepted: 8 July 2024
Driving fatigue has been one of the major causes of traffic accident. Efficient and accurate detection of driving fatigue are a legitimate public concern. In this paper, we conduct the simulated driving experiments and an EEG-based driving fatigue detection framework integrating multilayer brain network and convolutional neural network (CNN) is developed. This lightweight attention-based multi-frequency topology learning (AMFTL) framework first captures the fatigue-related multi-frequency brain topological information and then feeds it into a CNN-based topology feature extraction (TFE) module to fully explore and integrate the critical topological features. The quantitative analysis results show that there are significant differences in brain topologies between the alert and fatigue states. And experimental results show that our proposed framework achieves an average detection accuracy of 94.71% for driving fatigue, which outperforms the current state-of-the-art methods. This proposed framework is expected to open new venues for EEG-based brain state analysis, and holds promising practical application potential.
© 2024 EPLA
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