Agent Based Simulation for a Sustainable Society and Multi-agent Smart Computing

Abstract

The study of emotions in human-computer interaction has increased in recent years in an attempt to address new user needs. At the same time, it is possible to record brain activity in real-time and discover patterns to relate it to emotional states. This paper describes a machine learning approach to detect emotion from brain activity, recorded as elec- troencephalograph (EEG) with the Emotic Epoc device, during auditory stimulation. First, we extract features from the EEG signals in order to characterize states of mind in the arousal-valence 2D emotion model. Us- ing these features we apply machine learning techniques to classify EEG signals into high/low arousal and positive/negative valence emotional states. The obtained classifiers may be used to categorize emotions such as happiness, anger, sadness, and calm based on EEG data