MS-FRAN: A Novel Multi-Source Domain Adaptation Method for EEG-Based Emotion Recognition.

Abstract

Electroencephalogram (EEG), an important physiological signal, has been widely applied in many fields. Among them, EEG-based emotion recognition has gradually become a research hotspot. However, the large distribution differences and variations of EEG signals across subjects make the current research on emotion recognition stuck in a dilemma. In order to resolve this problem, we choose the domain adaptation methodology to explore the inspiration for solutions. In this paper, a Multi-Source Feature Representation and Alignment Network (MS-FRAN) is proposed, in which a new feature learning module named Wide Feature Extractor (WFE) has been designed. The MS-FRAN adopts and improves the strategy of existing multi-source domain adaptation, which possesses two main benefits. For one thing, it can align the distribution of each pair of source and target domains. For another, it can also reduce the distributional differences among the multiple source domains. To demonstrate the effectiveness of MS-FRAN, we have carried out cross-subject experiments on two public benchmark datasets, SEED and DEAP. Experimental results have shown that our method outperforms the related competitive approaches with greater performance for EEG-based emotion recognition.