Attention-based multi-semantic dynamical graph convolutional network for eeg-based fatigue detection

• Published in: Frontiers in neuroscience 2023-11, Vol.17, p.1275065-1275065
• Main Authors: Liu, Haojie, Liu, Quan, Cai, Mincheng, Chen, Kun, Ma, Li, Meng, Wei, Zhou, Zude, Ai, Qingsong
• Format: Article
• Language: English
• Subjects: Artificial intelligence; channel attention mechanism; Classification; Deep learning; driving fatigue detection; EEG; Electrodes; Electroencephalography; Entropy; Fatigue; graph convolutional network; Methods; Neural networks; Physiology; spatial attention mechanism; Traffic; Wavelet transforms
• ISSN: 1662-4548; 1662-453X; 1662-453X
• DOI: 10.3389/fnins.2023.1275065

Establishing a driving fatigue monitoring system is of utmost importance as severe fatigue may lead to unimaginable consequences. Fatigue detection methods based on physiological information have the advantages of reliable and accurate. Among various physiological signals, EEG signals are considered to be the most direct and promising ones. However, most traditional methods overlook the functional connectivity of the brain and fail to meet real-time requirements. To this end, we propose a novel detection model called Attention-Based Multi-Semantic Dynamical Graph Convolutional Network (AMD-GCN). AMD-GCN consists of a channel attention mechanism based on average pooling and max pooling (AM-CAM), a multi-semantic dynamical graph convolution (MD-GC), and a spatial attention mechanism based on average pooling and max pooling (AM-SAM). AM-CAM allocates weights to the input features, helping the model focus on the important information relevant to fatigue detection. MD-GC can construct intrinsic topological graphs under multi-semantic patterns, allowing GCN to better capture the dependency between physically connected or non-physically connected nodes. AM-SAM can remove redundant spatial node information from the output of MD-GC, thereby reducing interference in fatigue detection. Moreover, we concatenate the DE features extracted from 5 frequency bands and 25 frequency bands as the input of AMD-GCN. Finally, we conduct experiments on the public dataset SEED-VIG, and the accuracy of AMD-GCN model reached 89.94%, surpassing existing algorithms. The findings indicate that our proposed strategy performs more effectively for EEG-based driving fatigue detection.