In cross-subject decoding across simulated multi-level air traffic control tasks, the DMG-GCN model reached 80.30% average accuracy and a 78.63% average F1-score, outperforming state-of-the-art baselines. Built by researchers at Nanjing University of Aeronautics and Astronautics and other institutions, the dynamic microstate-guided graph convolutional network targets the inter-subject variability in controllers' EEG that has held back passive brain-computer interfaces for adaptive automation.
