DCR-GAT: 3D Convolutional Residual Graph Attention Network for Emotion Classification

Electroencephalogram (EEG) emotion recognition has become a central research focus within brain-computer interface (BCI) studies, where efficient emotion classification models can enable intelligent emotion regulation. In this paper, we propose a novel model based on three-dimensional convolutional neural networks (3D-CNN), residual networks (ResNet), and graph attention networks (GAT), named 3DCR-GAT, for EEG emotion recognition. The main advantage of the 3DCR-GAT model is its ability to simultaneously process multi-channel EEG signals, capturing complex spatiotemporal dependencies and local features within a unified network framework. Additionally, the ResNet employs skip connections to address the vanishing gradient problem during deep network training, improving the stability and expressiveness of the model. The attention mechanism within the model dynamically adjusts feature weights, captures global features, and enhances the accuracy of EEG emotion classification. Experimental results on public dataset, demonstrate that 3DCR-GAT significantly outperforms traditional methods in classification accuracy and feature extraction capability. Ablation studies reveal that each component of the model contributes substantially to its overall performance. Specifically, our model achieve an average accuracy of 92% on SEED for multi-state emotion recognition tasks. The 3DCR-GAT model represents an advanced and effective tool for EEG emotion classification, with broad potential applications.