Emotion Classification based on Bio-Signals Emotion Recognition using Machine Learning Algorithms

Emotions are complex processes involving multiple response channels, including physiological systems, facial expressions and voices. Bio-signals reflect sequences of neural activity, which result in changes in autonomic and neuroendocrine systems induced by emotional events. Therefore in human-computer interaction researches, one of the most current interesting topics in emotion recognition is to recognize human's feeling using bio-signals. The aim of this study is to classify emotions (joy, sadness, anger, fear, surprise, and neutral) that human have often experienced in real life from multichannel bio-signals using machine learning algorithms. We have measured physiological responses of three-hundred participants for acquisition of bio-signals such as electrodermal activity, electrocardiograph, skin temperature, and photoplethysmograph during six emotions induction. Also, for emotion classification, we have extracted eighteen features from the signals and performed emotion classification using four algorithms, linear discriminant analysis, Naive Bayes, classification and regression tree and support vector machine. The used algorithms were evaluated by only training, 10-fold cross-validation and repeated random sub-sampling validation. We have obtained recognition accuracy from 56.4 to 100% for only training and 39.2 to 53.9% for testing. Also, the result for testing showed that an accuracy of emotion recognition by Naive Bayes was highest (53.9%) and lowest by support vector machine (39.2%). This means that Naive Bayes is the best emotion recognition algorithm for basic emotions. This result can be helpful to provide the basis for the emotion recognition technique in human-computer interaction.