EEG Emotion Classification and Correlation Research Based on Deep Learning

In the classification of EEG emotions, the improvement of the generalization performance of deep learning networks is particularly important for the information extraction of EEG raw signals. In this paper, we use label smoothing (LS) as a regularization method to improve the generalization performance of the network by alleviating the overfitting of the network. The method is evaluated on the DEAP (Database for Emotion Analysis using Physiological signals) dataset, and the differences in emotion classification in different brain regions are compared. The experimental results show that our method has improved the generalization performance of the network, increasing the mean accuracy by 1.34% in the arousal dimension and 2.28% in the valence dimension.