A novel consciousness emotion recognition method using ERP components and MMSE

Objective. Electroencephalogram (EEG) based emotion recognition mainly extracts traditional features from time domain and frequency domain, and the classification accuracy is often low for the complex nature of EEG signals. However, to the best of our knowledge, the fusion of event-related potential (ERP) components and traditional features is not employed in emotion recognition, and the ERP components are only identified and analyzed by the psychology professionals, which is time-consuming and laborious. Approach. In order to recognize the consciousness and unconsciousness emotions, we propose a novel consciousness emotion recognition method using ERP components and modified multi-scale sample entropy (MMSE). Firstly, ERP components such as N200, P300 and N300 are automatically identified and extracted based on shapelet technique. Secondly, variational mode decomposition and wavelet packet decomposition are utilized to process EEG signals for obtaining different levels of emotional variational mode function (VMF), namely VMF beta+gamma, and then nonlinear feature MMSE of each VMF beta+gamma are extracted. At last, ERP components and nonlinear feature MMSE are fused to generate a new feature vector, which is fed into random forest to classify the consciousness and unconsciousness emotions. Main results. Experimental results demonstrate that the average classification accuracy of our proposed method reach 94.42%, 94.88%, and 94.95% for happiness, horror and anger, respectively. Significance. Our study indicates that the fusion of ERP components and nonlinear feature MMSE is more effective for the consciousness and unconsciousness emotions recognition, which provides a new research direction and method for the study of nonlinear time series.

Automated summary

The study proposes a novel method utilizing ERP components and nonlinear feature MMSE to recognize consciousness and unconsciousness emotions, achieving high classification accuracy in experimental results. This suggests that the fusion of ERP components and nonlinear feature MMSE is effective for emotion recognition and provides a new research direction for nonlinear time series studies.