Analysis of Physiological Signals for Emotion Recognition Based on Support Vector Machine

Emotion recognition is one of the important part to develop in human-human and human-computer interaction. In this paper, we focused on the experimental paradigm and feature extraction to extract features from the physiological signals. The experimental paradigm for data acquisition used MULTI module equipment of biofeedback 2000 x-pert which combined multi-sensor such as skin conductance, skin temperature, and blood volume pulse to collect physiological signals from the subject’s fingertip of the non-dominant hand. And an approach for the emotions recognition based on physiological signals such as fear, disgust, joy, and neutrality that international affective picture system (IAPS) was used to elicit emotion. These were selected to extract the characteristic parameters, which will be used for classifying emotions. Support vector machine (SVM) is a popular technique for classifying emotion recognition and perform high accuracy for classification. The experiment results showed that the methodology by using experimental paradigm, feature extraction and especially multi-class support vector machine (MSVM) provided significant improvement in accuracy for classification emotion recognition states.