DGC-Link: Dual-Gate Chebyshev Linkage Network on EEG Emotion Recognition

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.