Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
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With the rapid pace of modern life, millions of people suffer from sleep problems. Music therapy, as a non-medication approach to mitigating sleep problems, has attracted increasing attention recently. However the adaptability of music therapy is limited by the time consuming task of choosing suitable music for users. Inspired by this observation, we discuss the concept of a domain specific music recommendation system, which automatically recommends music for users according to their sleep quality. The proposed system requires multidisciplinary efforts including automated sleep quality measurement and content-based music similarity measure. As a first step, we focus on the automated sleep quality measurement in this paper. An EEG-based approach is proposed to measure user's sleep quality. The advantages of our approach over standard Polysomnography (PSG) method are: 1) it measures sleep quality by recognizing three sleep categories rather than six sleep stages, thus higher accuracy can be expected; 2) three sleep categories are recognized by analyzing Electroencephalography (EEG) signal only, so the user experience is improved because he is attached with fewer sensors during sleep. We conduct experiments based on a standard data set. Our approach achieves high accuracy and shows promising potential for the music recommendation system.