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RPGCN-GDA: Regionally Progressive Graph Convolutional Network with Gender-Sensitive Domain Adaptation for EEG Emotion Recognition
  • Yefei Huang 1,   
  • Wei Zhong 2,*,   
  • Shuzhan Hu 3,*,   
  • Fei Hu 2,   
  • Long Ye 2,   
  • Qin Zhang 2

Received: 30 Aug 2025 | Revised: 09 Oct 2025 | Accepted: 23 Oct 2025 | Published: 05 Nov 2025

Abstract

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.

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DOI
10.53941/tai.2025.100018
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Volume 1, Issue 1
How to Cite
Huang, Y.; Zhong, W.; Hu, S.; Hu, F.; Ye, L.; Zhang, Q. RPGCN-GDA: Regionally Progressive Graph Convolutional Network with Gender-Sensitive Domain Adaptation for EEG Emotion Recognition. Transactions on Artificial Intelligence 2025, 1 (1), 265–281. https://doi.org/10.53941/tai.2025.100018.
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