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

Numerous studies have demonstrated that gender-specific 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 (RPGCN-GDA). 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 tasks. The experiment results on public datasets demonstrate that the model not only excels in subject-dependent and subject-independent tasks but also shows significant advantages in handling gender-specific emotional responses, offering a promising new direction for developing higher gender-sensitive emotion recognition systems.