Investigation of Human Emotion Pattern Based on EEG Signal Using Wavelet Families and Correlation Feature Selection

Emotions is one of the advantages given by God to human beings compared to other living creatures. Emotions have an important role in human life. Many studies have been conducted to recognize human emotions using physiological measurements, one of which is Electroencephalograph (EEG). However, the previous researches have not discussed the types of wavelet families that have the best performance and canals that are optimal in the introduction of human emotions. In this paper, the power features of several types of wavelet families, namely Daubechies, symlets, and coiflets with the Correlation Feature Selection (CFS) method to select the best features of alpha, beta, gamma and theta frequencies. According to the results, coiflet is a method of the wavelet family that has the best accuracy value in emotional recognition. The use of the CFS feature selection can improve the accuracy of the results from 81% to 93%, and the five most dominant channels in the power features of alpha and gamma band on T8, T7, C5, CP5, and TP7. Hence, it can be concluded that the temporal of the left brain is more dominant in recognition of human emotions.Emotions is one of the advantages given by God to human beings compared to other living creatures. Emotions have an important role in human life. Many studies have been conducted to recognize human emotions using physiological measurements, one of which is Electroencephalograph (EEG). However, the previous researches have not discussed the types of wavelet families that have the best performance and canals that are optimal in the introduction of human emotions. In this paper, the power features of several types of wavelet families, namely Daubechies, symlets, and coiflets with the Correlation Feature Selection (CFS) method to select the best features of alpha, beta, gamma and theta frequencies. According to the results, coiflet is a method of the wavelet family that has the best accuracy value in emotional recognition. The use of the CFS feature selection can improve the accuracy of the results from 81% to 93%, and the five most dominant channels in the power features of alpha and gamma band on T8, T7, C5, CP5, and TP7. Hence, it can be concluded that the temporal of the left brain is more dominant in recognition of human emotions

EEG-based motion sickness classification system with genetic feature selection

People tend to get motion sickness on a moving boat, train, airplane, car, or amusement park rides. Many previous studies indicated that motion sickness sometimes led to traffic accidents, so it becomes an important issue in our daily life. In this study, we designed a VR-based motion-sickness platform with a 32-channel EEG system and a joystick which is used to report the motion sickness level (MSL) in real time during experiments. The results show it is feasible to estimate subject's MSL based on re-sampling frequency band proved by the high test accuracy. A comparison between general prediction models (such as LDA, QDA, KNN) and IBCGA shows that the IBCGA can be effectively increase the accuracy. In this paper, an extended-IBCGA (e-IBCGA) is proposed and it provides more accuracy than the prior-art research. The test results show that e-IBCGA increases at least 10% to 20% test accuracy in 6 subjects. © 2013 IEEE