A personalized music recommendation system based on electroencephalography feedback

Abstract

Numerous domestic and foreign studies have demonstrated that music can relieve stress and that listening to music is one method of stress relief used presently. Although stress-relief music is available on the market, various music genres produce distinct effects on people. Clinical findings have indicated that approximately 30 % of people listen to inappropriate music genres for relaxation and, consequently, their stress level increases. Therefore, to achieve the effect of stress relief, choosing the appropriate music genre is crucial. For example, a 70-year-old woman living in a military community since childhood might not consider general stress-relief music to be helpful in relieving stress, but when patriotic songs are played, her autonomic nervous system automatically relaxes because of her familiarity with the music style. Therefore, people have dissimilar needs regarding stress-relief music. In this paper, we proposed a personalized stress-relieving music recommendation system based on electroencephalography (EEG) feedback. The system structure comprises the following features: (a) automated music categorization, in which a new clustering algorithm, K-MeansH, is employed to precluster music and improve processing time; (b) the access and analysis of users’ EEG data to identify perceived stress-relieving music; and (c) personalized recommendations based on collaborative filtering and provided according to personal preferences. Experimental results indicated that the overall clustering effect of K-MeansH surpassed that of K-Means and K-Medoids by approximately 71 and 57 %, respectively. In terms of accuracy, K-MeansH also surpassed K-Means and K-Medoids.