A dual-branch GNN with a prior-injected Transformer and contrastive learning classifies EEG emotions from within- and cross-frequency coupling networks, reaching 97.88% accuracy on SEED.
This approach fully leverages b oth WFC and CFC information of emotional brain states, thereby laying a foundation for accurate emotion classification
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DB-GNN: Dual-Branch Graph Neural Network with Multi-Level Contrastive Learning for Jointly Identifying Within- and Cross-Frequency Coupled Brain Networks
A dual-branch GNN with a prior-injected Transformer and contrastive learning classifies EEG emotions from within- and cross-frequency coupling networks, reaching 97.88% accuracy on SEED.