The selected signals are processed by a Spatial-Temporal Graph-aware Network (STG-Net) that models spatial relationships between channels through graph convolution, coupled with a temporal modeling module, and frequency-spatial-temporal features are fused for classification; compared with state-of-the-art methods, the approach achieves superior performance in both recognition accuracy and model efficiency. PMID 41697833, DOI 10.1109/jbhi.2026.3665517
EEG-Based Emotion Recognition Using Spatial-Temporal Graph-Aware Network With Channel Selection
Summary
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.
Why it matters
Band-adaptive channel selection — rather than the uniform rules used before — attacks the redundancy and compute cost that have kept real-time EEG emotion recognition out of interactive systems.
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pubmed.ncbi.nlm.nih.gov 2026-02-16