DMG-GCN: A Dynamic Microstate-Guided Graph Convolutional Network for EEG Cognitive Workload Decoding in Air Traffic...

Complex inter-subject variability induces severe distribution shifts in the physiological features of electroencephalography (EEG) for air traffic controllers (ATCOs). These inter-subject shifts limit the generalization and interpretability of passive brain-computer interfaces (pBCIs) during cognitive workload decoding. To address this, a Dynamic Microstate-Guided Graph Convolutional Network (DMG-GCN) is proposed for robust cross-subject workload recognition. This approach utilizes a Dynamic Selective Kernel Temporal Convolutional Block (DSK-TCB) to adaptively extract multi-scale temporal-spectral dynamics, while concurrently constructing a time-evolving adjacency matrix via a Microstate-Guided Dynamic Graph Block (MG-DGB) to disentangle topological sub-networks. A spatiotemporal graph convolution module then aggregates these representations, and a temporal self-attention mechanism focuses on task-critical transition moments. Extensive experiments on simulated multi-level air traffic control tasks demonstrate that the proposed model achieves an overall average accuracy of 80.30% and an average F1-score of 78.63% in cross-subject evaluations, significantly outperforming state-of-the-art baselines. Moreover, an exploratory interpretability analysis suggests that the extracted topological sub-networks exhibit spatial patterns consistent with specific brain network reorganizations, which encompass the transition from global distributed monitoring to temporal multimodal integration and parietal-occipital parallel processing during workload regulation. The framework provides a robust and analytically transparent pBCI solution for adaptive automation in modern aviation.