MCAHNN: Multi-Channel EEG Emotion Recognition Using Attention Mechanism Based on Householder Reflection

Emotions are integral to human cognition, exerting a profound influence on physiological responses, cognitive processes, and decision-making capabilities. Electroencephalography (EEG)based emotion classification provides a significant methodological approach for the exploration of emotional states. Despite its potential, most current methodologies face challenges in delineating the representational patterns across different brain regions and in effectively classifying emotions from EEG signals. In response, a novel model for emotion recognition is proposed in this paper, which utilizes a multi-channel attention mechanism, designated as MCAHNN. This model incorporates Householder Reflection to enhance the attention mechanism, facilitating the extraction of inter-channel EEG features and simulating inter-regional brain dynamics. Furthermore, 1D convolution is employed to analyze intra-channel relationships. The proposed model has been evaluated on the publicly available DEAP dataset and further tested on the SEED dataset. Experimental results confirm that the MCAHNN model achieves state-of-the-art performance, demonstrating its effectiveness in classifying emotions within multi-center datasets.Code is publicly available at https://github.com/Oreoreoreor/MCAHNN.