Emotion Analysis Through EEG and Peripheral Physiological Signals Using KNN Classifier

Abstract

Emotions are the characteristics of human beings which are triggered by the mood, temperament or motivation of an individual. Emotions are nothing but the response to the stimuli that are experienced by the brain. Any changes in one’s emotional state results in changes in electrical signals generated by the brain. The emotions can be explicit or implicit, i.e. either emotion may be expressed or remain unexpressed by the individual. As these emotions are experienced by the individual as the result of the brain stimulus, we can observe Electroencephalogram (EEG) signal to classify the emotions. Some of the physiological signals may also be taken into account as any change in emotional state result in some physiological changes. For the analysis, we have used the standard DEAP dataset for emotion analysis. In the dataset, the 32 test subjects are shown with 40 different 1-minute music videos and the EEG and other physiological signals are recorded. On the basis of the Self-Assessment Manikins (SAM), we classify the emotion state in the valence arousal plane. The K-Nearest Neighbour classifier is used to classify the multi-class emotions as higher/lower levels of the valence arousal plane. The comparison of KNN with other classifiers depicts that KNN has produced best average accuracy of 87.1%.