Classification of familiarity based on cross-correlation features between EEG and music

An approach to recognize the familiarity of a listener with music using both the electroencephalogram (EEG) signals and the music signal is proposed in this paper. Eight participants listened to melodies produced by piano sounds as simple natural stimuli. We classified the familiarity of each participant using cross-correlation values between EEG and the envelope of the music signal as features of the support vector machine (SVM) or neural network used. Here, we report that the maximum classification accuracy was 100% obtained by the SVM. These results suggest that the familiarity of music can be classified by cross-correlation values. The proposed approach can be used to recognize high-level brain states such as familiarity, preference, and emotion.