Emotion recognition based on physiological signals using valence-arousal model

This paper considers two dimensional valence-arousal model. Pictorial stimuli of International Affective Picture Systems were chosen for emotion elicitation. Physiological signals like, Galvanic Skin Response, Heart Rate, Respiration Rate and Skin Temperature were measured for accessing emotional responses. The experimental procedure uses non-invasive sensors for signal collection. A group of healthy volunteers was shown four types of emotional stimuli categorized as High Valence High Arousal, High Valence Low Arousal, Low Valence High Arousal and Low Valence Low Arousal for around thirty minutes for emotion elicitation. Linear and Quadratic Discriminant Analysis are used and compared to the emotional class classification. Classification of stimuli into one of the four classes has been attempted on the basis of measurements on responses of experimental subjects. If classification is restricted within the responses of a specific individual, the classification results show high accuracy. However, if the problem is extended to entire population, the accuracy drops significantly.