Time-Course EEG Spectrum Evidence for Music Key Perception and Emotional Effects

  • Hongjian Bo
  • Haifeng Li
  • Lin Ma
  • Bo Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10023)


Being one of the most direct expressions of human feelings, music becomes the best tool for investigating the relationship between emotion and cognition. This paper investigated the long-term spectrum evidence of electroencephalogram (EEG) activities elicited by music keys. The EEG signals were recorded in 21 healthy adults during the entire process of music listening. There were two major music episodes and two minor ones, each lasted two minutes. Considering the spectral characteristics: (1) During two minutes of music listening, the alpha band activities recovered rapidly, which were more obvious under major music; (2) while, in the high-frequency gamma band, the activities declined gradually which were more obvious under minor music. Taken together, these results give clear evidence for the time-course difference in the music key perception.


Time-course analysis Music key perception Electroencephalogram Affective computing Brain-computer interaction 



Our thanks to supports from the National Natural Science Foundation of China (61171186, 61271345, 61671187), Fundamental Research Project of Shenzhen (JCYJ20150929143955341), Key Laboratory Opening Funding of MOE-Microsoft Key Laboratory of Natural Language Processing and Speech (HIT.KLOF.20150xx, HIT.KLOF.20160xx), and the Fundamental Research Funds for the Central Universities (HIT.NSRIF.2012047). The authors are grateful for the anonymous reviewers who made constructive comments.


  1. 1.
    Gabrielsson, A., Lindström, E.: The role of structure in the musical expression of emotions. In: Juslin, P.N. (ed.) Handbook of Music and Emotion: Theory, Research, Applications, pp. 367–400. Oxford University Press, New York (2010)Google Scholar
  2. 2.
    Steblin, R.: A History of Key Characteristics in the Eighteenth and Early Nineteenth Centuries. University of Rochester Press, Rochester (2002)Google Scholar
  3. 3.
    Hevner, K.: The affective character of the major and minor modes in music. Am. J. Psychol. 47(1), 103–118 (1935)CrossRefGoogle Scholar
  4. 4.
    Krumhansl, C.L., Kessler, E.J.: Tracing the dynamic changes in perceived tonal organization in a spatial representation of musical keys. Psychol. Rev. 89(4), 334 (1982)CrossRefGoogle Scholar
  5. 5.
    Blood, A.J., Zatorre, R.J., Bermudez, P., Evans, A.C.: Emotional responses to pleasant and unpleasant music correlate with activity in paralimbic brain regions. Nat. Neurosci. 2(4), 382–387 (1999)CrossRefGoogle Scholar
  6. 6.
    Koelsch, S., Fritz, T., Müller, K., Friederici, A.D., et al.: Investigating emotion with music: an fMRI study. Hum. Brain Mapp. 27(3), 239–250 (2006)CrossRefGoogle Scholar
  7. 7.
    Halpern, A.R., Martin, J.S., Reed, T.D.: An ERP study of major-minor classification in melodies. MUSIC PERCEPT. INTERDISC. J. 25(3), 181–191 (2008)CrossRefGoogle Scholar
  8. 8.
    Green, A.C., Bærentsen, K.B., Stødkilde-Jørgensen, H., Wallentin, M., Roepstorff, A., Vuust, P.: Music in minor activates limbic structures: a relationship with dissonance? Neuroreport 19(7), 711–715 (2008)CrossRefGoogle Scholar
  9. 9.
    Virtala, P., Berg, V., Kivioja, M., Purhonen, J., Salmenkivi, M., Paavilainen, P., Tervaniemi, M.: The preattentive processing of major vs. minor chords in the human brain: an event-related potential study. Neurosci. Lett. 487(3), 406–410 (2011)CrossRefGoogle Scholar
  10. 10.
    Poikonen, H., Alluri, V., Brattico, E., Lartillot, O., Tervaniemi, M., Huotilainen, M.: Event-related brain responses while listening to entire pieces of music. Neuroscience 312, 58–73 (2016)CrossRefGoogle Scholar
  11. 11.
    Peretz, I., Zatorre, R.J.: Brain organization for music processing. Annu. Rev. Psychol. 56, 89–114 (2005)CrossRefGoogle Scholar
  12. 12.
    Mao, M., Rau, P.-L.P.: EEG-based measurement of emotion induced by mode, rhythm, and MV of Chinese pop music. In: Rau, P.L.P. (ed.) CCD 2014. LNCS, vol. 8528, pp. 89–100. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-07308-8_9 Google Scholar
  13. 13.
    Menon, V., Levitin, D.J.: The rewards of music listening: response and physiological connectivity of the mesolimbic system. Neuroimage 28(1), 175–184 (2005)CrossRefGoogle Scholar
  14. 14.
    Morris, J.D.: Observations: Sam: the self-assessment manikin an efficient cross-cultural measurement of emotional response. J. Advertising Res. 35(6), 63–68 (1995)Google Scholar
  15. 15.
    Welch, P.: The use of fast fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 15(2), 70–73 (1967)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Lin, Y.P., Wang, C.H., Wu, T.L., Jeng, S.K., Chen, J.H.: Support vector machine for EEG signal classification during listening to emotional music. In: 2008 IEEE 10th Workshop on Multimedia Signal Processing, pp. 127–130. IEEE (2008)Google Scholar
  17. 17.
    Iwaki, T., Hayashi, M., Hori, T.: Changes in alpha band EEC activity in the frontal area after stimulation with music of different affective content. Percept. Motor Skills 84(2), 515–526 (1997)CrossRefGoogle Scholar
  18. 18.
    Schack, B., Chen, A.C., Mescha, S., Witte, H.: Instantaneous EEG coherence analysis during the stroop task. Clin. Neurophysiol. 110(8), 1410–1426 (1999)CrossRefGoogle Scholar
  19. 19.
    Fitzgibbon, S.P., Pope, K.J., Mackenzie, L., Clark, C.R., Willoughby, J.O.: Cognitive tasks augment gamma EEG power. Clin. Neurophysiol. 115(8), 1802–1809 (2004)CrossRefGoogle Scholar
  20. 20.
    Collins, T., Tillmann, B., Barrett, F.S., Delbé, C., Janata, P.: A combined model of sensory and cognitive representations underlying tonal expectations in music: from audio signals to behavior. Psychol. Rev. 121(1), 33 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  2. 2.Software College, Harbin University of Science and TechnologyHarbinChina

Personalised recommendations