基于DE-MI-STFT融合特征的情绪识别方法OA

The application of EEG to identify the level of pleasure and excitement of patients is of great significance for the depression diagnosis.The current research on electroencephalogram(EEG)emotion recognition has some problems such as insufficient extraction of signal features from different brain regions and low accuracy of three-classification due to single-dimensional feature modeling.To solve the above problems,this paper proposes an EEG emotion recognition method based on DE-MI-STFT fusion features:differential entropy(DE),mutual information(MI),and short-time Fourier transform(STFT)features are extracted to construct a multi-dimensional set,which is separately modeled and trained through a deptwise separable neural networks,and then the multi-model decision results are fused via D-S evidence theory to fully utilize the complementary information of features.Three-classification experiments on the DEAP public dataset show that the classification accuracy of the proposed method in the valence and arousal dimensions is 14.3%and 13.6%higher than that of the existing optimal models,respectively.The study indicates that the proposed method can effectively capture the spatiotemporal features of EEG,significantly improve the three-classification accuracy through the fusion of multiple features and multiple models,and provide technical support for the auxiliary diagnosis of emotion-related diseases.