An Approach to EEG based Emotion Identification by SVM classifier

Abstract

Emotions are the requisites in our day-to-day life. Emotions are the psychophysiology states that are coupled with thoughts, feelings, behavioral responses, and a degree of satisfaction or dissatisfaction. There are various methods for achieving psychophysiology data from human beings such as Electroencephalography (EEG), Electrocardiography (ECG), Photoplethysmogram (PPG), blood volume pulse (BVP). In this paper, we have mainly focussed on EEG device for getting this data. Electroencephalography (EEG) is an electrophysiological monitoring method to note the electrical activity of the brain by the electrodes that are placed on the scalp. With the help of the deep dataset, we trained the Support Vector Machine (SVM), which is a classifier. The raw EEG data should be further processed to reduce the artifacts and features are selected to give the input to the SVM classifier. The outputs are in the form of valency and arousal values. The acquired results are having an accuracy of 83% in the detection of emotions.