Development of EEG based Emotion Classifier

Abstract

Analysis and study of abstract human relations have always posed a daunting challenge for technocrats engaged in the field of psychometric analysis. The study on emotion recognition is all the more demanding as it involves integration of abstract phenomenon of emotion causation and emotion appraisal through physiological and brain signals. Emotion is most commonly defined as short and intense reaction of humans occurring on account of a stimulus. Occurrence of emotion may bring a noticeable change in physiological parameters such as respiration rate, heart rate, Galvanic Skin Resistance (GSR), body temperature and ElectroEncephaloGram (EEG) etc. Changes in physical parameters such as color of the skin, eye gaze, eye blink rate and shape of the face are also perceived. The study of complex human emotions for developing an affective Brain Computer Interface (BCI) has for long been an area attracting biomedical scientists. Moreover emotion recognition plays an important role for all the personnel involved in mission critical tasks like for pilots, nuclear plant operators and air traffic controllers etc. The challenge to develop an affective BCI demanded understanding of emotions psychologically, physiologically as well as analysis from engineer s point of view. To make the analysis and classification of emotions possible, emotions have been represented in a 2-dimensional or 3-dimensional space represented by arousal and valence domains or arousal, valence and dominance domains respectively. Interestingly, the classification of emotions along any of the domains is possible by utilizing the orthogonal nature of emotions. One of the effective ways to classify emotions is by use of Event Related Potential (ERP) of EEG signals. This requires projection of emotion evoking stimulus on one computer system while simultaneously putting a mark on another computer system acquiring EEG. It is generally achieved by using costly modules to synchronize stimulus presentation system with the data acquisition system.