Abstract
EEG signals are closely related to mood changes, and the study of EEG signals can accurately reflect the changes of human emotions. In this paper, EEG signals were classified and recognized. Video emotion inducing materials were selected to induce subjects to produce happy, neutral and sad emotions, and EEG signals were collected at the same time. Wavelet packet transform, power spectral density and approximate entropy features were extracted, and then emotion classification was carried out by using width learning classification method, and then the classification results were analyzed and compared. The results show that the performance of differential entropy in single feature classification is up to 78.3%, while the accuracy of differential entropy and wavelet packet transform for feature fusion is up to 81.5%.
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