Positive and Negative Emotion Classification Based on Multi-channel - Long, Fangfang; Zhao, Shanguang; Wei, Xin; Ng, Siew-Cheok; Ni, Xiaoli; Chi, Aiping; Fang, Peng; Zeng, Weigang; Wei, Bokun (Frontiers in Behavioral Neuroscience, 2021)
The EEG features of different emotions were extracted based on multi-channel and forehead channels in this study. The EEG signals of 26 subjects were collected by the emotional video evoked method. The results show that the energy ratio and differential entropy of the frequency band can be used to classify positive and negative emotions effectively, and the best effect can be achieved by using an SVM classifier. When only the forehead and forehead signals are used, the highest classification accuracy can reach 66%. When the data of all channels are used, the highest accuracy of the model can reach 82%. After channel selection, the best model of this study can be obtained. The accuracy is more than 86%.