The Research Of EEG Signal In Emotion Recognition

Affective computing is one of the key technologies to achieve high-level huma-computer interaction, and a research direction which is the increasing attention to the field of artificial intelligence. Emotion recognition is an integral part of affective computing, including voice, facial expressions, text, gesture, and physiological signal recognition, etc, which the emotion recognition of physiological signals is most difficult.The main object of emotion recognition based on physiological signals is different from emotional state of the EEG, EMG, ECG, skin conductance, skin resistance, skin temperature, optical pulse and respiration signals.The United States MIT Media Lab Affective Computing Research Group led by Professor Picard is to analyze electromyography (EMG), respiration (RSP), GSR (SC) and blood volume pulse (BVP). They are the first to extract features from these physiological signals to research emotion recognition and testified that it is feasible to recognize emotion from physiological signals.EEG is the hundreds of millions of neurons within the brain activity in a comprehensive reflection of the cerebral cortex can be a direct reflection of brain activity. The different state of mind and a variety of emotional changes in the cerebral cortex in different locations reflect different brain signals, therefore, the brain signals are rich in useful information, how to deal effectively with these brain signals and extract features from them is very important to the emotional stae recognition.However, because the brain produces electrical signals is a very complex mechanism of non-stationary random signal, its data collection process is quite complicated and vulnerable to the external environment and the ECG, EMG and other physiological signals, the EEG at home and abroad used to identify the research of emotional state recognition is very few compared to other physiological signals(such as ECG, EMG, SC,etc.).From Germany, Augsburg University Institute of Computing Scienct physiological signal data collected, we can see that they did not collect EEG data.This paper studied the research of EEG signal in emotion recognition based on the previous research results which are based on other physiological signals in emotion recognition, the main research contents are as follows:(1) Cllection of EEG data:our laboratory through the use of the MP150 multi-channel physiological recorder which is provided by the United States Biopac Company to collected six physiological signal data, including electroencephalography (EEG), electromyography (EMG), electrocardiogram (ECG), blood volume pulse (BVP), skin electrical response (GSR) and respiration. We collected physiological signals data from the total of 244 subjects, all the subjects were all from the Southwest University freshman in school students;(2) Original EEG preprocessing:this paper will put theβ-wave rhythm of EEG as the object of emotion recognition by analyzing the characteristics of the four basic rhythms, use wavelet packet transform to extract signals from theβ-wave rhythm of brain, and analysis its power spectrum to testified that it is feasible to recognize emotion from physiological signals with theβ-wave rhythm of EEG;(3) Extraction of EEG features:theβ-wave rhythm, which is extracted form EEG by the use of wavelet packet transform, and its wavelet packet nodes of wavelet packet decomposition coefficients are used to calculate the the various statistics of EEG characteristics as the original features.Anger, disgust, fear, sadness, joy and surprise are corresponding to six kinds of categories of emotion, the sample number of anger is 228, the sample number of disgust is 96, the sample number of fear is 230, the sample number of sad is 238, the sample number of joy 206, the sample number of surprise is 191, the total sample number is 1433;(4) Selection of EEG features:the tabu search algorithm is used to select the features of affective physiological signals, the fisher classifier is used as an emotional pattern classifier and its classification accuracy is used to evaluate the feature combination, that helps to retain the feature combination which is helpful to increase the accuracy rate and excluding the feature combination which declines the accuracy rate, in order to assess the strengths and weaknesses of the feature combination. In this way, we select features from EEG signals in order to identify the emotions effectively;(5) Finally, papers make the experimental simulation of emotional state recognition in the way of one-to-one and one-to-many.Papers extracted P-wave rhythm from EEG signals, and extracted related features from the P-wave, and then using tabu search algorithm for feature selection, in the end made the the experimental simulation of emotional state recognition in the way of one-to-one and one-to-many. Analysis the simulation results from the experiment, we has been relatively satisfied with the emotional stae recognition effect and got some feature set which has a greater contribution to identify a specific emotion.lt testified that the EEG features we extracted for emotion recognition got a certain recognition effect and it is feasible to recognize emotion with EEG signals.