Multi-Branch Network for Cross-Subject EEG-based Emotion Recognition
Proceedings of The 13th Asian Conference on Machine Learning, PMLR 157:705-720, 2021.
In recent years, electrocardiogram (EEG)-based emotion recognition has received increasing attention in affective computing. Since the individual differences of EEG signals are large, most models are trained for specific subjects, and the generalization is poor when applied to new subjects. In this paper, we propose a Multi-Branch Network (MBN) model to solve this problem. According to the characteristics of the cross-subject data, different branch networks are designed to separate the background features and task features of the EEG signals for classification to have better model performance. Besides, there is no new-subject data needed during model training. In order to avoid the negative improvement caused by samples with significant differences to model training, a tiny amount of new-subject data is used to filter the training samples to improve the model performance further. Before training the model, the samples with significant differences from the new subject were deleted by comparing the background features between the subjects. The experimental results show that compared with Single-Branch Network (SBN) model, the accuracy of the MBN model is improved by 20.89% on the SEED dataset. Furthermore, compared with other common methods, the proposed method uses less new-subject data, which improves its practicability in practical application.