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Music Recommendation System for Personalized Brain Music Training Research with Jade Solution Company

  • Received : 2017.03.10
  • Accepted : 2017.04.05
  • Published : 2017.06.30

Abstract

According to a recent survey, most elementary and secondary school students nationwide are stressed out by their academic records. Furthermore most of high school students in Korea have to study under the great duress. Some of them who can't overcome the academic stress finalize their life by suiciding. A study has found that it is one of the leading causes of stimulating the thought of committing suicide in Korean high school students. So it is necessary to reduce the high school student's suicide rate. Main content of this research is to implement a personalized music recommendation system. Music therapy can help the student deal with the stress, anxiety and depression problems. Proposed system works as a therapist. The music choice and duration of the music is adjusted based on the student's current emotion recognized automatically from EEG. If the happy emotion is not induced by the current music, the system would automatically switch to another one until he or she feel happy. Proposed system is personalized brain music treatment that is making a brain training application running on smart phone or pad. That overcomes the critical problems of time and space constraints of existing brain training program. By using this brain training program, student can manage the stress easily without the help of expert.

Keywords

References

  1. H.O. Won, "The Effects of the Neurofeedback Programs on the Function and Stress of High School Students", Journal of Child Care, Vol. 3, No. 14, pp. 315-323, 2008.
  2. P.N. Friel, "EEG Biofeedback in the Treatment of Attention Deficit/Hyperactivity Disorder", Alternative Medicine Review, Vol. 12, No. 2, 2007.
  3. M. Galina, Brain Music Treatment Introduction to QEEG and Neurofeedback, 2nd Edition, Elsevier Press, 2009.
  4. G. John, A theory of alpha/theta neurofeedback, creative performance enhancement, long distance functional connectivity and psychological integration. http://www0.cs.ucl.ac.uk/.../Gruzelier%20CogProc.pdf.
  5. J.H. Cho, "A Study of Alpha Brain Wave Characteristics from MRI Scanning in Patients with Anxiety Disorder", Journal of the Korean Physical Society, Vol. 59, No. 4, pp. 2861-2868, 2011. https://doi.org/10.3938/jkps.59.2861
  6. H. Eric, "Brain Training Against Stress", Stress report, version 4.2, 2005.
  7. M. Rollin, "The Effects of Different Types of Music on Mood, Tension, and Mental Clarity", International Journal of Alternative Therapies, Vol. 4, No. 1, pp. 75-84, 1998.
  8. L. Paul, "Social Tagging and Music Information Retrieval", Journal of New Music Research, Vol. 37, No.2, pp. 101-114, June 2008. https://doi.org/10.1080/09298210802479284
  9. C.P. Fran and C. Daniel, "A Taxonomy of Musical Genres", in Content-Based Multimedia Information Retrieval Access Conference (RIAO), April 2000.
  10. S. Neel, "Recommender Systems at the Long Tail", in ACM conference on Recommender systems, pp. 1-5, 2011.
  11. B.M. Sarwar, G. Karypis, J.A. Konstan, and J. Riedl, "Item-based Collaborative Filtering Recommendation Algorithms", in 10th International World Wide Web Conference,2001.
  12. K. Miyahara, and M.J. Pazzani, "Improvement of Collaborative Filtering with the Simple Bayesian Classifier", Information Processing Society of Japan, Vol. 43, No. 11, 2002.
  13. P. Melville, R.J. Mooney, and R. Nagarajan, "Content Boosted Collaborative Filtering for Improved Recommendations", Proceedings of the 18th National Conference of Artificial Intelligence, 2002.
  14. R. Greiner, X. Su, B. Shen, and W. Zhou "Structural Extension to Logistic Regression: Discriminative Parameter Learning of Belief Net Classifiers", Machine Learning, Vol. 59, No. 3, pp. 297-322, 2005. https://doi.org/10.1007/s10994-005-0469-0
  15. X. Su, and T.M. Khoshgoftaar, "Collaborative Filtering for Multi-class Data Using Belief Net Algorithms", the 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp. 497-504, 2006.
  16. W. Jun, A.P. De Vries, and M.J.T. Reinders, "Unifying User-based and Item based Collaborative Filtering Approaches by Similarity Fusion Categories", in Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 501-508, 2006.