Multi-Scale Dynamic Temporal Network With Graph Matching Domain Adaptation for Cross-Subject EEG Emotion Recognition

Electroencephalography (EEG) is a powerful and objective tool for detecting emotions, with broad applications across various domains. However, EEG-based emotion recognition faces two major challenges: (1) extracting domain-invariant features that maintain emotion-related information across variations induced by individual differences, and (2) aligning the marginal and conditional distributions of data from different individuals in the feature space. To address these challenges, we propose a novel method: the Multi-scale Dynamic Temporal Network with Graph Matching Domain Adaptation, designed for cross-subject EEG emotion recognition. Our method employs a multi-scale dynamic temporal attention module to extract robust domain-invariant features. In addition, it utilizes a domain adaptation network based on Chebyshev graph representations, which reformulates the emotion recognition task as a graph matching problem. This approach effectively aligns cross-subject data distribution. To validate the effectiveness of our method, we conducted extensive experiments on two benchmark databases (SEED and DEAP) under three different cross-validation protocols. The results demonstrate consistent reliability in subjects and sessions. Specifically, in the cross-subject single-session cross-validation task, our method achieved an accuracy of 94.69%$\pm$5.16% on the SEED dataset. On the DEAP dataset, our approach achieved accuracies of 69.45%$\pm$7.26% and 67.52%$\pm$8.28% for the Valence and Arousal dimensions, respectively. These results demonstrate that our method outperforms existing state-of-the-art approaches. Source code is available at:https://github.com/seizeall/MDTN-GMDA.