From Micro to Meso: A Data-Driven Mesoscopic Region Division Method Based on Functional Connectivity for EEG-Based Driver Fatigue Detection

The integration of EEG signals and deep learning methods is emerging as an effective approach for brain fatigue detection, particularly utilizing Graph Neural Networks(GNNs) that excel in capturing complex electrode relationships. A significant challenge within GNNs is the construction of an effective adjacency matrix that enhances spatial information learning. Concurrently, electrode aggregation in EEG has emerged as a pivotal area of research. However, conventional partitioning methods depend on task-specific prior knowledge, limiting their generalizability across diverse tasks. To Address this issue, we propose a novel mesoscopic region division approach for EEG-based driver fatigue detection, leveraging inherent data characteristics and functional connectivity-based GNN. This method adopts a two-stage approach: initially, micro-electrodes exhibiting similar functional connectivity relationships are grouped as "mesoscopic region"; subsequently, all micro-electrodes in the same group are aggregated into virtual meso-electrodes, and the fatigue state classification is subsequently based on the functional connectivity between them. Applied to a public driver fatigue detection dataset, our approach surpasses existing state-of-the-art methods in performance. Additionally, interpretive analysis provides micro and mesoscopic insights into brain regions and neuronal connections associated with alert and fatigued states.