Statistical Approach for a Complex Emotion Recognition Based on EEG Features

This paper presents electroencephalogram (EEG) signals and normal distribution technique to recognize the complex emotion. In the recent years, there has been a trend towards recognizing human emotions, however not many researcher aware that human can recognize more than one emotion at one time. Thus, in this study, normal distribution is utilized to recognize the expected emotion. The feature extraction and classification were obtained using a Mel-frequency cepstral coefficients (MFCC) and multilayer perceptron (MLP). The correlation between human emotion and mood is also the essential point, since the mood can affected to the human emotion. The results show that the human emotions is strongly influenced by his initial mood.