Emotion Recognition and Channel Selection Based on EEG Signal

As an important research direction in the field of artificial intelligence, emotion recognition has become a hot topic in current research. Because of the use of multi-channel EEG acquisition equipment nowadays, which brings many problems in practical use and post-calculation, we have also studied channel selection. In this paper, the DEAP database is used as EEG data. The multi-feature fusion in a time domain and the composite features based on wavelet feature and information entropy are used as EEG features for emotion recognition. The average recognition accuracy reached 72.03% and 71.7% respectively. We also use the ReliefF algorithm to select EEG channels. Under the premise of a slight loss of emotional recognition preparation rate, we selected the optimal combination of 6 and 13 channels, and the brain data was reduced from 32 channels to 13 channels. It lays a foundation for the development of portable, wearable devices.