EEG-Based Emotion Recognition Using Spatial-Temporal Graph-Aware Network With Channel Selection
The study presents an EEG emotion recognition framework that couples discriminative channel selection with hierarchical spatial-temporal modeling. Wavelet coherence and mutual information adaptively select informative channels across frequency bands, and a spatial-temporal graph-aware network (STG-Net) models inter-channel spatial relations and the temporal evolution of emotional states before fusing frequency-spatial-temporal features for classification. The authors report better recognition accuracy and model efficiency than state-of-the-art methods.