EEG-Based Driver Fatigue Detection Using Spatio-Temporal Fusion Network With Brain Region Partitioning Strategy

Detecting driver fatigue is critical for ensuring traffic safety. Electroencephalography (EEG) is the golden standard for brain activity measurement and is considered a good indicator of detecting driver fatigue. However, the current driver fatigue detection algorithm has limitations in mining and fusing the spatiotemporal characteristics of EEG signals. In this paper, we propose a multi-branch deep learning network named spatio-temporal fusion network with brain region partitioning strategy (STFN-BRPS) to improve the accuracy and robustness of driver fatigue recognition. Initially, we develop a recurrent multi-scale convolution module (RMSCM) comprising a multi-scale convolution sub-module, a CNN-Bi-LSTM sub-module, and a residual structure branch. RMSCM effectively extracts highly discriminative long short-term temporal feature information. Secondly, we propose a dynamic graph convolution module and a spatial graph edges’ importance weight assignment method based on brain region partitioning strategy, which can acquire intrinsic spatial feature information between electrodes. Thirdly, we design a feature fusion module (FFM) that utilizes channel attention to fuse long short-term temporal and spatial features. FFM learns and prioritizes the significance and relevance of each channel in the fused features. Finally, the fused spatio-temporal features are passed into the classification module to obtain the predicted driver fatigue state. Extensive comparison and ablation studies are conducted on EEG signals collected from real-world driving scenarios. The results demonstrate that the proposed STFN-BRPS model delivers superior classification performance compared to the mainstream methods. This study establishes a benchmark for EEG-based driver fatigue detection and related deep-learning modeling work.