A two-stream channel reconstruction and feature attention network for EEG emotion recognition

Research on human emotions based on EEG during multimedia stimuli is an emerging field that has made significant progress in EEG-based emotion classification. However, current studies often neglect the extraction of dynamic information from EEG signals and lack the exploration of local information. Moreover, many existing models are overly complex, demanding an excessive investment in training resources and time. In this paper, we propose a novel, simple two-stream channel reconstruction and feature attention network, named CRFAE-motionNet, for EEG emotion recognition. The main advantage of CRFAEmotionNet is its ability to simultaneously integrate static and dynamic information from EEG signals within a unified network. Additionally, it can extract continuous EEG temporal information through channel reconstruction and utilize feature attention to further explore local information. The proposed network was evaluated using the publicly available DEAP dataset. The experimental results indicate that the proposed CRFAEmotionNet outperforms the state-of-the-art baselines, achieving the accuracy of 98.7% for valence and 98.6% for arousal.