Multilayer perceptron for EEG signal classification during listening to emotional music

Abstract

In this study an electroencephalography (EEG) signal-based emotion classification algorithm was investigated. Several excerpts of emotional music were used as stimulus for elicitation of emotion-specific EEG signal. Besides, the hemispheric asymmetry alpha power indices of brain activation were extracted as feature vector for training multilayer perceptron classifier (MLP) in order to learn four targeted emotion categories, including joy, angry, sadness, and pleasure. The results demonstrated that the average classification accuracy of MLP could be 69.69% in five subjects for four emotional categories, which is much higher than chance probability of 25%.