PR-DA: Prototype Regularization Domain Adaptation for Cross-Subject EEG-Based Emotion Recognition

Rongtao Chen, Zhepei Hong, Qi You, Chuwen Xie, Jiahui Zhang, Jiahui Pan

Abstract: Electroencephalogram (EEG)-based emotion recognition holds significant potential in healthcare, traffic safety, and entertainment. However, cross-subject emotion recognition remains challenging due to individual differences and the difficulty in extracting domain-invariant features. To address these issues, this paper proposes a novel Prototype Regularization Domain Adaptation (PR-DA) framework. Experimental results on three benchmark datasets (SEED, SEED-IV, and SEED-VII) demonstrate that the proposed PR-DA framework achieves superior performance compared to state-of-the-art methods, with accuracies of 96.30%±2.87%, 86.56%±4.67%, and 50.43%±6.71 %, respectively. The proposed PR-DA framework of-fers a promising approach for cross-subject EEG-based emotion recognition. The source code is available at the following link: https://github.com/seizeall/PR-DA.