Research On Channel Selection And Emotion Classification Of EEG

As a senior function of human brain,emotion has a great influence on human study,work,and all aspects of life.Recognizing human emotion correctly can make artificial intelligence serve human being better.Therefore,emotion recognition has been a very important research direction in the field of artificial intelligence.Up till now,emotion recognition mainly focuses on people’s external behavior characteristic and objective physiological signal.Compared with the external behavior characteristic,the objective physiological signal has better spontaneity and objectivity.As a physiological signal,EEG signal which is generated by the central nervous system,has most closely contact with human emotion.The experiments based on emotion recognition of the EEG signal is often implemented by the full channels EEG signals,and the full channels acquisition of EEG signal has limited the development of subsequent portable equipment.In addition,most emotion recognition classification algorithms use shallow classifier,which leads to the bottleneck of recognition rate.In this paper,based on the above two existent issues,around the brain electrical signal preprocessing,feature extraction,channel selection,and classification algorithm improvement,several aspects of research is proposed as follows:EEG signal is a kind of small-signal,and always include artifacts and noise jamming.In this paper,firstly,the noise signal is removed by preprocessing in order to get the pure EEG signal.To solve the problem that EEG signal has non-stationary characteristics,992 dimensions features are obtained by combining feature extraction method of wavelet transform and information entropy.Aiming at the problems of the poor portability of experimental equipment and complex data processing generated by the full channels EEG acquisition experiment,BP plus DEMATEL EEG channel selection method is proposed to select the optimal combination of EEG channel,and the SVM is adopted to verify the feasibility of the algorithm.The proposed channel selection method declines down the original 62 channels EEG data to 8 channels,greatly reduces the number of the actual EEG channels acquisition.In addition,the position where brain regions is associated with emotion most closely can be obtained,which laid the foundation for the subsequent development of portable wearable devices.To solve the problem that EEG signals emotion recognition rate based on the shallow classifier is low,this paper presents a new type of EEG signal recognition algorithm by combining deep learning and neural network.The algorithm uses two double-layer RBM to deeply mine the EEG data and utilizes GRNN for data classification,and achieves a recognition rate of about 87.1%.A large number o f comparative experiments make sure that the algorithm for the EEG signal has strong anti-interference and high recognition rate.In addition,dimensionality reduction methods also have great influence on the accuracy of emotion recognition.In this paper compares dimension reduction methods such as PCA and m RMR to illustrate the characteristics of them by using theoretical deduction and experimental analysis.EEG acquisition experiments based on video evoking emotion are designed by our research laboratory.The emotion classification algorithm is used to classify the collected EEG data,and experimental results testify the effectiveness of the algorithm.