Xi'an Shiyou University

EEG attention recognition has moved away from a fixed electrode-connectivity graph. Multi-band features were previously concatenated and channel relations were handled with a preset adjacency matrix, which struggles to capture task-dependent coupling. DGCSA treats electrodes as graph nodes, updates channel topology through a learnable adjacency matrix, and adds self-attention for global dependencies, while Bayesian-optimized band weights fuse power spectral density and differential entropy. On 873 three-second EEG segments from 20 participants, three-class accuracy reached 91.67%, above SVM, CNN and LSTM baselines; mean attention scores were also higher in VR than in 2D.

EEG attention recognition has moved away from a fixed electrode-connectivity graph. Multi-band features were previously concatenated and channel relations were handled with a preset adjacency matrix, which struggles to capture task-dependent coupling. DGCSA treats electrodes as graph nodes, updates channel topology through a learnable adjacency matrix, and adds self-attention for global dependencies, while Bayesian-optimized band weights fuse power spectral density and differential entropy. On 873 three-second EEG segments from 20 participants, three-class accuracy reached 91.67%, above SVM, CNN and LSTM baselines; mean attention scores were also higher in VR than in 2D.