Research on emotion classification based on EEG

Research shows that human emotion production is closely related to the activity correlation of cerebral cortex, so the research of emotion classification by EEG provides a reliable basis. The feature extraction and classification application of EEG have made rapid development, so we combine EEG with emotion to study emotion classification. However, there are differences between EEG signals of different subjects, which have a certain impact on emotion classification. How to ensure the high accuracy and robustness of recognition is a problem. In view of this problem, the spectrum analysis method is used to extract features to study different subjects in different states. The extracted features are classified into emotion by discriminant analysis algorithm, and the classification effect is satisfactory. There are many methods involved in feature extraction and the space is long, different feature extraction methods will be compared later, so as to improve the robustness and efficiency of emotional classification of EEG signals.