DGC-Link: Dual-Gate Chebyshev Linkage Network on EEG Emotion Recognition
Qilin Li, Tong Zhang, C. L. Philip Chen, Xiaowei Zhang, Bin Hu
Abstract
EEG emotion recognition presents several challenges, including region correlation, local and long-range node connectivity, and multi-channel patterns, necessitating advanced methods capable of effectively capturing and utilising complex EEG signal information. This paper introduces a novel method, the dual-gate Chebyshev Linkage network (DGC-Link), which comprises three main components: the Chebyshev Linkage (CL) module for extracting regional correlation features, the dual-gate module for regulating the flow of different-order information, and the deep network for extracting multi-channel features and enhancing representation capabilities. Validated on three datasets (SEED, DREAMER, and MPED) with ablation experiments demonstrating each component's effectiveness, DGC-Link achieves superior recognition performance compared to state-of-the-art methods. Notably, it achieves 96.43% accuracy on differential entropy on the SEED dataset, and 98.58%, 97.62%, and 98.01% for valence, arousal, and dominance classifications on the DREAMER dataset, along with 78.48% and 44.93% for 3-class and 7-class classifications on the MPED dataset. These results highlight DGC-Link's potential for improved performance in EEG emotion recognition.