Research On Deep Learning Emotion Recognition Based On EEG Physiological Big Data

Machine automatic emotion recognition has attracted more and more attention due to its potential application in human-computer interaction.At present,emotion recognition can be realized through a variety of information,such as facial expressions,voice intonation,body posture,and various physiological signals of the human body.In comparison,cortical electroencephalography(EEG),as a physiological signal of the central nervous system,regulates the secretion of emotion-related transmitters and the limbic system of the brain,and can reflect changes in human emotional state objectively.Extracting effective features from EEG signals and performing emotion recognition are of great significance in human-computer interaction,mental and psychological disease monitoring.This paper focuses on the emotion dimension model construct and the key links of EEG emotion recognition,such as feature extraction and classification.In order to explore the effectiveness of different method and model for EEG emotion recognition,signal processing method are used to extract features and Deep stacked auto-encoder(Deep-SAE)method for automatically generating feature respectively,and the classification method is designed with stacked auto-encoder deep learning algorithm and Long short-term memory(LSTM)recurrent network.The main research contents of this paper are as follows:(1)The dimensional emotional model was built.Based on the DEAP dimensional emotional physiology dataset,the characteristics of emotional EEG signals were extracted from different perspectives.The power spectral densities of ?,?,? and ? rhythms were extracted by AR(Autoregressive)model power spectrum estimation method.Wavelet packet decomposition was used to extract the wavelet packet coefficients and energy ratio time-frequency characteristics of the EEG.The characteristics of sample entropy and wavelet packet entropy of EEG were extracted by nonlinear analysis.(2)A stacked auto-encoder neural network deep learning algorithm was designed,and machine emotion recognition was performed on the extracted EEG features from the two emotional dimensions of valence and arousal.The simulation results of this method on the DEAP dataset show that: in the valence dimension,the average accuracy of emotion recognition reaches80.3%;In the arousal dimension,the average accuracy of recognition reaches81.5%,obtained a high accuracy of emotion recognition.On this basis,this paper analyzed the influence on results of emotion recognition in three aspects,including EEG feature combination,EEG data balance,and the dimensional emotion label threshold.(3)In order to automatically mine the emotional feature information from EEG signals in the emotional recognition of physiological signals.A Deep-SAE method was proposed,with this method,the deep emotional information can be automatic decoded directly from the EEG data,and the feature sequence can be generated consequently.Through the simulation test of multi-channel EEG signal,the emotion characteristic information of multi-channel EEG data is obtained using the method of encoding and decoding.(4)Aiming at the problem of poor generalization ability caused by classification algorithms in EEG emotion recognition.The temporal characteristics of EEG signals was considered,and the time feature sequence was generated from the EEG emotion features.Then,a long short-term memory recurrent network was designed for model training,cross-validation and testing.The performance of the model was evaluated through some indicators,including accuracy,precision,recall,and F1-Score.The simulation results show: in the valence dimension,the average accuracy of EEG emotion recognition and F1-Score reaches 77.4% and 0.804,respectively.In the arousal dimension,the average recognition accuracy and F1-Score reaches 73.7% and 0.775,respectively.Compared with other methods,this model has better generalization ability in emotion recognition.This paper provided a new idea for EEG signal automatic emotion recognition in the dimensional emotion models construction,feature extraction and classification recognition methods.Research results has important scientific and application value in emotional robot,medical health,psychology,brain-computer interaction,situated learning and multimedia game development.