Discriminating Surprise and Anger from EEG and Eye Movements with a Graph Network.

Emotion recognition based on EEG and eye movement signals has been studied extensively due to the reliability and stability of signals. The separability of four basic emotions, happy, sad, disgust and fear, has been systematically studied in the existing work. However, there is less research on the emotions of anger and surprise since they are more difficult to be elicited in lab settings. This paper investigates the discrimination ability of EEG and eye movement signals for surprise and anger. To this end, we design a stimulus paradigm that can effectively elicit surprise and anger. We propose a novel Graph Convolutional Network with Channel Attention (GCNCA) to classify three emotions, anger, surprise and neutrality. Experimental results indicate that: a) the proposed GCNCA model has an excellent classification accuracy of 86.47% using EEG and 84.22% using eye movement signals, which are better than other baseline methods; b) EEG and eye movements have a good ability to discriminate surprise and anger, while EEG performs better than eye movements; c) the high-frequency bands of EEG are more distinguishable on classifying surprise and anger than the low-frequency bands; d) there are some differences in neural patterns between surprise and anger, meanwhile critical channels and channel connections of EEG are found.