An investigation of annotation smoothing for EEG-based continuous music-emotion recognition

As emotional responses of a human to stimuli could evolve over the course of time, continuous emotion reporting is essential for the construction of a computational model to capture the temporal evolution of the human emotions. However, continuous emotion assessment is confronting various challenges, especially when using the continuous arousal-valence space. Manipulating emotion annotation data prior to performing emotion recognition is, therefore, necessary. In this paper, we present a study of applying three different signal filtering techniques to smooth annotation data; moving average filter, Savitzky-Golay filter, and median filter. We performed experiments of arousal and valence recognition in music listening tasks employing signals from electroencephalogram (EEG). Fractal dimension approach was adopted to extract informative features from brain dynamics and emotional states were then derived by classification and regression techniques. Our empirical results suggested the promise of the moving average filter that could enhance the performance of emotion classifying and tracking.