SEED-MYA: A Novel Myanmar Multimodal Dataset for Enhancing Emotion Recognition

This paper introduces a novel Myanmar multimodal dataset called SEED-MYA, which is the first culturally and linguistically tailored multimodal emotion recognition dataset for the Burmese speakers. The SEED-MYA dataset consists of EEG and eye movement data collected using Myanmar video stimuli, addressing the underrepresentation of minority cultures in emotion recognition research. To investigate the fundamental characteristics of emotion recognition based on EEG and eye movement data from Myanmar participants, and to validate the quality and effectiveness of the SEED-MYA dataset, we implement the Multimodal Adaptive Emotion Transformer with Cross-Modal Attention (MAET-CMA) as a benchmarking tool. From our experiments on SEED-MYA, we have three main findings: (a) beta and gamma bands play a critical role in distinguishing positive, neutral, and negative emotional states; (b) combining EEG and eye movement data significantly enhances emotion recognition accuracy, with MAET-CMA achieving a maximum accuracy of 91.78%; and (c) EEG signals excel in recognizing negative and positive emotional states, while eye movement data are particularly effective at differentiating neutral emotion in our experimental setup. Our neural activity analysis reveals distinct patterns of activation in temporal, parietal, and prefrontal regions, providing insights into potential culture-related neural responses. We further compare our findings with established benchmarks from Chinese participants (SEED dataset) to explore cultural similarities and differences in emotion recognition. This analysis is structured into three components: band-level EEG comparisons, modality-specific performance analysis, and neural activity differences, providing a multi-level analysis of cultural effects. While some similarities in emotion recognition are observed across two cultures, our cross-cultural performance comparison between SEED and SEED-MYA further indicates that the Chinese dataset generalizes better as a test set. These findings underscore the importance of incorporating culturally diverse datasets in the development of globally applicable emotion recognition systems.