A Consumer BCI for Automated Music Evaluation Within a Popular On-Demand Music Streaming Service “Taking Listener’s Brainwaves to Extremes”

  • Fotis Kalaganis
  • Dimitrios A. Adamos
  • Nikos Laskaris
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 475)

Abstract

We investigated the possibility of a using a machine-learning scheme in conjunction with commercial wearable EEG-devices for translating listener’s subjective experience of music into scores that can be used for the automated annotation of music in popular on-demand streaming services.

Based on the established -neuroscientifically sound- concepts of brainwave frequency bands, activation asymmetry index and cross-frequency-coupling (CFC), we introduce a Brain Computer Interface (BCI) system that automatically assigns a rating score to the listened song.

Our research operated in two distinct stages: (i) a generic feature engineering stage, in which features from signal-analytics were ranked and selected based on their ability to associate music induced perturbations in brainwaves with listener’s appraisal of music. (ii) a personalization stage, during which the efficiency of extreme learning machines (ELMs) is exploited so as to translate the derived patterns into a listener’s score. Encouraging experimental results, from a pragmatic use of the system, are presented.

Keywords

EEG Music evaluation Recommendation-systems Human machine interaction Spotify 

References

  1. 1.
    Niedermeyer, E., da Silva, F.L.: Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins, New York (2005)Google Scholar
  2. 2.
    Casson, A.J., Yates, D., Smith, S., Duncan, J.S., Rodriguez-Villegas, E.: Wearable electroencephalography. IEEE Eng. Med. Biol. Mag. 29, 44–56 (2010)CrossRefGoogle Scholar
  3. 3.
    Ariely, D., Berns, G.S.: Neuromarketing: the hope and hype of neuroimaging in business. Nat. Rev. Neurosci. 11, 284–292 (2010)CrossRefGoogle Scholar
  4. 4.
    He, B., Baxter, B., Edelman, B.J., Cline, C.C., Ye, W.W.: Noninvasive brain-computer interfaces based on sensorimotor rhythms. Proc. IEEE 103(6), 907–925 (2015)Google Scholar
  5. 5.
    Wikström, P., DeFillippi, R. (eds.): Business Innovation and Disruption in the Music Industry. Edward Elgar Publishing, Cheltenham (2016)Google Scholar
  6. 6.
    Downes, L., Nunes, P.: Big bang disruption. Harvard Bus. Rev. 91, 44–56 (2013)Google Scholar
  7. 7.
    Adamos, A.D., Dimitriadis, I.S., Laskaris, A.N.: Towards the bio-personalization of music recommendation systems: a single-sensor EEG biomarker of subjective music preference. Inf. Sci. 343–344, 94–108 (2016)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Cohen, M.X.: Analyzing Neural Time Series Data: Theory and Practice. MIT Press, Cambridge (2014)Google Scholar
  9. 9.
    Altenmüller, E.: Cortical DC-potentials as electrophysiological correlates of hemispheric dominance of higher cognitive functions. Int. J. Neurosci. 47, 1–14 (1989)CrossRefGoogle Scholar
  10. 10.
    Petsche, H., Ritcher, P., von Stein, A., Etlinger, S.C., Filz, O.: EEG coherence and musical thinking. Music Percept. Interdisc. J. 11, 117–151 (1993)CrossRefGoogle Scholar
  11. 11.
    Birbaumer, N., Lutzenberger, W., Rau, H., Braun, C., Mayer-Kress, G.: Perception of music and dimensional complexity of brain activity. Int. J. Bifurcat. Chaos 6, 267 (1996)CrossRefGoogle Scholar
  12. 12.
    Hadjidimitriou, S.K., Hadjileontiadis, L.J.: Toward an EEG-based recognition of music liking using time-frequency analysis. IEEE Trans. Biomed. Eng. 59, 3498–3510 (2013)CrossRefGoogle Scholar
  13. 13.
    Schmidt, B., Hanslmayr, S.: Resting frontal EEG alpha-asymmetry predicts the evaluation of affective musical stimuli. Neurosci. Lett. 460, 237–240 (2009)CrossRefGoogle Scholar
  14. 14.
    Nakamura, S., Sadato, N., Oohashi, T., Nishina, E., Fuwamoto, Y., Yonekura, Y.: Analysis of music-brain interaction with simultaneous measurement of regional cerebral blood flow and electroencephalogram beta rhythm in human subjects. Neurosci. Lett. 275(3), 222–226 (1999)CrossRefGoogle Scholar
  15. 15.
    Bhattacharya, J., Petsche, H.: Musicians and the gamma band: a secret affair? Neuroreport 12(2), 371–374 (2001)CrossRefGoogle Scholar
  16. 16.
    Schmidt, A.L., Trainor, L.J.: Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions. Cognit. Emot. 15(4), 487–500 (2001)CrossRefGoogle Scholar
  17. 17.
    Sammler, D., Grigutsch, M., Fritz, T., Koelsch, S.: Music and emotion: electrophysiological correlates of the processing of pleasant and unpleasant music. Psychophysiology 44(2), 293–304 (2007)CrossRefGoogle Scholar
  18. 18.
    Hadjidimitriou, S.K., Hadjileontiadis, L.J.: EEG-based classification of music appraisal responses using time-frequency analysis and familiarity ratings. IEEE Trans. Affect. Comput. 4, 161–172 (2013)CrossRefGoogle Scholar
  19. 19.
    Pan, Y., Guan, C., Yu, J., Ang, K.K., Chan, T.E.: Common frequency pattern for music preference identification using frontal EEG. In: 6th International IEEE/EMBS Conference on Neural Engineering, pp. 505–508 (2013)Google Scholar
  20. 20.
    Coan, A.J., Allen, J.B.J.: Frontal EEG asymmetry as a moderator and mediator of emotion. Biol. Psychol. 67(1–2), 7–50 (2004)CrossRefGoogle Scholar
  21. 21.
    Canolty, T.R., Knight, T.R.: The functional role of cross-frequency coupling. Trends Cognit. Sci. 14(11), 506–515 (2010)CrossRefGoogle Scholar
  22. 22.
    Dimitriadis, S.I., Laskaris, N.A., Bitzidou, M.P., Tarnanas, I., Tsolaki, M.N.: A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses. Front. Neurosci. 9(350) (2015). doi: 10.3389/fnins.2015.00350
  23. 23.
    Szekely, J.G., Rizzo, L.M., Bakirov, K.N.: Measuring and testing dependence by correlation of distances. Ann. Stat. 35(6), 2769–2794 (2007)MathSciNetCrossRefMATHGoogle Scholar
  24. 24.
    Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)CrossRefGoogle Scholar
  25. 25.
    Huang, G.B.: What are extreme learning machines? Filling the gap between Frank Rosenblatt’s Dream and John von Neumann’s Puzzle. Cogn. Comput. 7, 263–278 (2015)CrossRefGoogle Scholar
  26. 26.
    Wright, M., Freed, A., Momeni, A.: Open sound control: state of the art 2003. In: NIME 2003: Proceedings of the 3rd Conference on New Interfaces for Musical Expression (2003)Google Scholar
  27. 27.
    Akhtar, M.T., Jung, T.P., Makeig, S., Cauwenberghs, G.: Recursive independent component analysis for online blind source separation. In: IEEE Internet Symposium on Circuits and Systems, vol. 6, pp. 2813–2816 (2012)Google Scholar
  28. 28.
    Want, R., Schilit, B. N., Jenson, S.: Enabling the internet of things. Computer (1), pp. 28–35 (2015)Google Scholar
  29. 29.
    Miranda, J., Makitalo, N., Garcia-Alonso, J., Berrocal, J., Mikkonen, T., Canal, C., Murillo, J.M.: From the internet of things to the internet of people. IEEE Internet Comput. 19(2), 40–47 (2016)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  • Fotis Kalaganis
    • 1
  • Dimitrios A. Adamos
    • 2
    • 3
  • Nikos Laskaris
    • 1
    • 3
  1. 1.AIIA Lab, Department of InformaticsAristotle University of ThessalonikiThessalonikiGreece
  2. 2.School of Music StudiesAristotle University of ThessalonikiThessalonikiGreece
  3. 3.Neuroinformatics GRoupAristotle University of ThessalonikiThessalonikiGreece

Personalised recommendations