Fatigue driving detection based on prefrontal electroencephalogram asymptotic hierarchical fusion network.
10.7507/1001-5515.202407083
- Author:
Jiazheng SUN
1
;
Weimin LI
2
;
Ningling ZHANG
2
;
Cai CHEN
2
;
Shengzhe WANG
1
;
Fulai PENG
2
Author Information
1. School of Railway Traffic, Shandong Jiaotong University, Jinan 250000, P. R. China.
2. Shandong Zhongke Advanced Technology Co. Ltd., Jinan 250000, P. R. China.
- Publication Type:Journal Article
- Keywords:
Asymptotic hierarchical fusion;
Convolutional neural network;
Cross-attention mechanism;
Fatigue driving detection;
Frontal electroencephalogram
- MeSH:
Humans;
Electroencephalography/methods*;
Automobile Driving;
Fatigue/diagnosis*;
Accidents, Traffic/prevention & control*;
Signal Processing, Computer-Assisted;
Neural Networks, Computer;
Algorithms;
Prefrontal Cortex/physiology*
- From:
Journal of Biomedical Engineering
2025;42(3):544-551
- CountryChina
- Language:Chinese
-
Abstract:
Fatigue driving is one of the leading causes of traffic accidents, posing a significant threat to drivers and road safety. Most existing methods focus on studying whole-brain multi-channel electroencephalogram (EEG) signals, which involve a large number of channels, complex data processing, and cumbersome wearable devices. To address this issue, this paper proposes a fatigue detection method based on frontal EEG signals and constructs a fatigue driving detection model using an asymptotic hierarchical fusion network. The model employed a hierarchical fusion strategy, integrating an attention mechanism module into the multi-level convolutional module. By utilizing both cross-attention and self-attention mechanisms, it effectively fused the hierarchical semantic features of power spectral density (PSD) and differential entropy (DE), enhancing the learning of feature dependencies and interactions. Experimental validation was conducted on the public SEED-VIG dataset. The proposed model achieved an accuracy of 89.80% using only four frontal EEG channels. Comparative experiments with existing methods demonstrate that the proposed model achieves high accuracy and superior practicality, providing valuable technical support for fatigue driving monitoring and prevention.
