EEG Emotion Recognition Based on Optimal Feature Selection

Abstract

Aiming at the problems of dimensional disasters and low classification accuracy caused by too many features extracted in the emotion recognition process, an EEG emotion recognition method with optimized feature selection is proposed. The individual rhythmic signals of the EEG are obtained by wavelet packet decomposition and the sample entropy, energy and power spectral density are extracted as EEG features. A discrete binarization of the feature matrix using the Beetle Antennae Search (BAS) algorithm, while introducing a subset of features into the objective function and searching for the optimal subset of features. Finally, the SVM classifier is used for classification. The experimental results show that it achieves 89.72% accuracy on the DEAP dataset and significantly reduces the original feature dimension compared with the traditional feature selection method, which has good application prospect.