RPGCN-GDA: Regionally Progressive Graph Convolutional Network with Gender-Sensitive Domain Adaptation for EEG Emotion Recognition

Yefei Huang, Wei Zhong, Fei Hu, Long Ye, Qin Zhang

Abstract: Numerous studies have demonstrated that genderspecific emotional patterns are prevalent and can be reflected in electroencephalography (EEG) signals. However, most existing EEG-based emotion recognition models fail to fully account for these gender differences, leading to limited generalization performance. To address this problem, this paper proposes a regionally progressive graph convolutional network with gender-sensitive domain adaptation. Grounded in prior information of gender differences, the proposed model is expected to flexibly capture gender-specific connectivity patterns across functional brain regions using a progressive graph structure. By fully fusing hierarchical emotional features and adaptively adjusting distributional differences between genders, our model performs remarkable generalization capabilities in both cross-subject and cross-gender emotion recognition. This work provides a promising direction for advancing gender-sensitive emotion recognition systems.

External IDs:dblp:conf/icmcs/HuangZHYZ25