Research on the Classification and Channel Selection of Emotional EEG

Abstract

The emotion affect every aspect of our daily lives. Because of the high temporal resolution and low cost, EEG is widely used in the fields of emotion recognition. This paper studies the effects of different EEG feature combinations and channel selections on emotion recognition. Three effective features including differential entropy (DE), 1st difference (1st) and fractal dimension (FD) were extracted from the EEG signals, and their performances in the three situations of four emotion classifications, two emotions classification on valence and two emotions classification on arousal were calculated and compared by SVM. Two channel selection methods, including the mean relief channel selection algorithm and the common channel selection algorithm, were used to select the best channel. The results showed that when selecting the top 10 channels, the accuracy of the four emotional states classification rate was approximately 95%. This is significant for reducing the number of electrodes and reducing the complexity of brain-computer interface applications in the future.