Detection of Music Preferences using Cerebral Blood Flow Signals.

Music has been used for entertainment as well as for medical treatments that remedies physical conditions or psychiatric disorders. It is reported that better outcome can be expected when the music was selected along with personal preferences for these therapies. However it is difficult to find out personalized favorite music if the patient has difficulty in verbal communication due to ageing or developmental disorders. Therefore, this study aimed to develop an objective classification method of music preference based on cortical hemodynamic activities upon listening to the music clips. Fifteen healthy young adults listened their favorite and non-favorite music with their cortical activity measured with 38-channel functional near infrared spectroscopy (fNIRS). Eleven features were extracted from the time-courses of fNIRS signals from the left primary auditory area, the superior temporal gyrus, and the subcentral area. One- to four- dimensional features were individually selected using a leave-one-out cross-validation with two classification algorithms of Fisher linear discriminant analysis (LDA) and support vector machine. The best mean accuracy rate of 92.2 ± 1.7% was obtained when an LDA classifier with four features derived from oxy-hemoglobin signals were adopted showing that our proposed method is valid to classify individual music preference.