MEEG-Transformer: Transformer Network based on Multi-domain EEG for Emotion Recognition

Emotion recognition is a trending topic for research in the area of the brain computer interface (BCI). As an effective signal source, EEG(Electroencephalogram) is widely used in emotion recognition tasks, from which multiple features can be extracted in different domains, such as time domain and frequency domain. However, how to make full use of multiple domain features has become a challenge. In this study, we propose a transformer network for emotion recognition based on Multi-domain EEG features, named MEEG-Transformer. MEEG-Transformer can effectively capture the spatial information with the convolution layer, mine unique information within each domain, and explore the complementary information between features from different domains using self-attention mechanism. Specifically, we extract the features of time domain, frequency domain and wavelet domain respectively, construct the two-dimensional feature matrix of three domains based on the 10-20 system, and merge the three matrices into multi-domain EEG features. Using the DEAP dataset to perform experiments, the proposed model achieves 96.8% and 96.0% recognition accuracy in the arousal and valence dimensions respectively. It is indicated that the proposed method has a strong inspiration for emotion recognition tasks.