Classification of Music-Induced Emotions Based on Information Fusion of Forehead Biosignals and Electrocardiogram

The classification of four emotional states in an arousal–valence space was performed by employing two parallel support vector machines as arousal and valence classifiers and a fuzzy-rough model feature evaluation criterion and sequential forward floating selection algorithm.

Abstract

Emotion recognition systems have been developed to assess human emotional states during different experiences. In this paper, an approach is proposed for recognizing music-induced emotions through the fusion of three-channel forehead biosignals (the left temporalis, frontalis, and right temporalis channels) and an electrocardiogram. The classification of four emotional states in an arousal–valence space (positive valence/low arousal, positive valence/high arousal, negative valence/high arousal, and negative valence/low arousal) was performed by employing two parallel support vector machines as arousal and valence classifiers. The inputs of the classifiers were obtained by applying a fuzzy-rough model feature evaluation criterion and sequential forward floating selection algorithm. An average classification accuracy of 88.78 % was achieved, corresponding to an average valence classification accuracy of 94.91 % and average arousal classification accuracy of 93.63 %. The proposed emotion recognition system may be useful for interactive multimedia applications or music therapy.