Adaptive Graph Neural Networks with Multi-Scale Attention for EEG-Based Emotion Recognition

Electroencephalogram (EEG) based emotion recognition plays a vital role in affective computing and braincomputer interface applications. In this paper, we propose AMGCN, a novel adaptive graph neural network with multi-scale attention for robust emotion recognition from EEG signals. The model first extracts differential entropy (DE) features and maps them to a spatial 2D topological grid. A deep CNN is employed to capture local spatial-spectral representations. We introduce a multi-scale attention module that adaptively reweights EEG features across spatial, spectral, and temporal dimensions, enhancing emotion-relevant representations by jointly modeling brain-region topology, frequency, and samples. Furthermore, GCN is used to model individualized brain connectivity via a learnable adjacency matrix. Experimental results on the DEAP dataset demonstrate that AM-GCN achieves $\mathbf{9 5. 1 8 \%}$ accuracy for valence and 94.86 % for arousal, outperforming several state-of-the-art methods. These results highlight the model's ability to integrate spatial, spectral, and relational features, offering both high accuracy and interpretability.