Performance Analysis of Machine Learning Algorithms for Emotion State Recognition through Physiological Signal

Human-Computer-Interface HCI) has become an emerging area of research among the scientific community. The uses of machine learning algorithms are dominating the subject of data mining, to achieve the optimized result in various areas. One such area is related with emotional state classification using bio-electrical signals. The aim of the paper is to investigate the efficacy, efficiency and computational loads of different algorithms that are used in recognizing emotional state through cardiovascular physiological signals. In this paper, we have used Decision tables, Multi-layer Perceptron, C4.5 and Naïve Bayes as a subject under study, the classification is done into two domains: High Arousal and Low Arousal;