Emotion classification of EEG brain signal using SVM and KNN

Affective computing research field is growing in large scale with the frequent development of human-computer applications. These applications use information of mental or affective conditions of desired subjects to train their brain responses. Facial impressions, text physiology, vocal and other information about the subjects are used in the classification algorithms. However, the classification frameworks for EEG brain signals have been used infrequently due to the lack of a complete theoretical framework. Therefore, we present here an analysis of two different classification methods which are SVM and KNN. Four different types of emotional stimulus were presented to each subject. After preprocessing of raw EEG data, we employed Hjorth parameters for feature extraction of all EEG channels at each epoch. Five male subjects were selected in this experiment. Our results show that the emotion recognition from EEG brain signals might be possible.