Emotion classification during music listening from forehead biosignals
Emotion recognition systems are helpful in humanmachine interactions and clinical applications. This paper investigates the feasibility of using 3-channel forehead biosignals (left temporalis, frontalis, and right temporalis channel) as informative channels for emotion recognition during music listening. Classification of four emotional states (positive valence/low arousal, positive valence/high arousal, negative valence/high arousal, and negative valence/low arousal) in arousalvalence space was performed by employing two parallel cascade-forward neural networks 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 averaged classification accuracy of 87.05% was achieved, corresponding to average valence classification accuracy of 93.66% and average arousal classification accuracy of 93.29%.
forehead biosignals emotion recognition valence arousal