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

Abstract

Numerous studies have demonstrated that gender-specific emotional pat- terns 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 ad- dress 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 progres- sive 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.

Keywords

EEG emotion recognition / gender differences / graph convolutional network / domain adaptation

Author Contributions

Y.H. and W.Z.: conceptualization, methodology, software; Y.H.: data curation, writing—original draft preparation; Y.H. and S.H.: visualization, investigation; S.H. and F.H.: supervision, validation; L.Y. and Q.Z.: writing—reviewing and editing; Q.Z. and S.H.: funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This work is supported by the National Natural Science Foundation of China under Grant Nos. 62271455 and 62501545.

Data Availability Statement

The publicly available datasets of SEED and SEED-IV can be found here: https://bcmi.sjtu.edu.cn/home/seed/index.html and we have obtained the permission to use the datasets in our research.

Conflicts of Interest

The authors declare no conflict of interest.

Use of AI and AI-assisted Technologies

No AI tools were utilized for this paper.

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