Multi-Source Multi-Target Domain Similarity Network for Cross-Cultural EEG Emotion Recognition

Abstract

The significant variations in emotional patterns across different cultures pose a major challenge for cross-cultural electroencephalogram (EEG) emotion recognition. Moreover, this task must address not only differences in feature distributions among different cultures but also among individuals within the same culture. Therefore, we propose a novel domain adaptation approach, the Multi-Source Multi-Target Domain Similarity Network (MSMTDS), which treats each subject as an individual source domain or target domain. Based on the assessment of similarities across both cross-cultural and intra-cultural dimensions, the model dynamically adjusts the weights assigned to each domain, allowing similar domains to play a dominant role in training while reducing the adverse impact of dissimilar domains. Additionally, given the scarcity of EEG data, MSMTDS fully leverages all available data to maximize performance. Extensive experiments on the SEED EEG emotion datasets from three distinct cultures (China, France, and Germany) demonstrate the effectiveness of our approach, achieving state-of-the-art results.