EEG Emotion Recognition Based on Channel Attention for E-Healthcare Applications

Emotion recognition based on EEG is a critical issue in Brain-Computer Interface (BCI). It also plays an important role in the e-healthcare systems, especially in the detection and treatment of patients with depression by classifying the mental states. Unlike previous works that feature extraction using multiple frequency bands leads to a redundant use of information, where similar and noisy features extracted. In this paper, we attempt to overcome this limitation with the proposed architecture, Channel Attention-based Emotion Recognition Networks (CAERN). It can capture more critical and effective EEG emotional features based on the use of attention mechanisms. Further, we employ deep residual networks (ResNets) to capture richer information and alleviate gradient vanishing. We evaluate the proposed model on two datasets: DEAP database and SJTU emotion EEG database (SEED). Compared to other EEG emotion recognition networks, the proposed model yields better performance. This demonstrates that our approach is capable of capturing more effective features for EEG emotion recognition.