Advertisement

Explore the Brain Response to Naturalistic and Continuous Music Using EEG Phase Characteristics

  • Jie Li
  • Hongfei JiEmail author
  • Rong Gu
  • Lusong Hou
  • Zhicheng Zhang
  • Qiang Wu
  • Rongrong Lu
  • Maozhen Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9771)

Abstract

Although many researches attempt to extract music-related EEG activities, they usually focus on EEG amplitude characteristics. So far, there is no publication reporting naturalistic and continuous music related components based on EEG phase characteristics. In this work, we explore the brain response to long natural music using only EEG phase characteristics. Benefiting from multiway representation, the Ordered PARAFAC model decomposition, and pattern correlation analysis, related phase factors can be extracted and reveal that the alpha and theta oscillations and central and occipital area are most relevant to the music stimulus, which is consistent with not only the previous work but also the results of corresponding EEG amplitude characteristics. Moreover, phase factors can be combined to identify plausible real brain activities elicited by music. Our studies attest to the effectiveness of EEG phase characteristics in exploring the brain response to naturalistic and continuous music.

Keywords

EEG Phase Music Tensor 

Notes

Acknowledgments

The work was supported by the National Natural Science Foundation of China (Grant Nos. 61105122, 61305060), the Fundamental Research Funds for the Central Universities, Specialized Research Fund for the Doctoral Program of Higher Education (Grant no. 20130131120025), Jinan Youth Star of Science and Technology Plan (Grant no. 201406002), Science and Technology Commission of Shanghai Municipality (Grant Nos. 16JC1401300) and Shanghai sailing program (Grant No. 16YF1415300).

References

  1. 1.
    Henry, J.C.: Electroencephalography: basic principles, clinical applications, and related fields, fifth edition. Neurology 67(11), 2092 (2006)CrossRefGoogle Scholar
  2. 2.
    Reinvang, I.: Cognitive event-related potentials in neuropsychological assessment. Neuropsychol. Rev. 9(4), 231–248 (1999)CrossRefGoogle Scholar
  3. 3.
    Steven, J.: An Introduction to the Event-Related Potential Technique, vol. 18(11), p. 66 (2005). Neuroreport Brisson and Jolicur Copyright © Lippincott WilliamsGoogle Scholar
  4. 4.
    Jäncke, L., Kühnis, J., Rogenmoser, L., Elmer, S.: Time course of EEG oscillations during repeated listening of a well-known aria. Front. Hum. Neurosci. 9, 401 (2015)CrossRefGoogle Scholar
  5. 5.
    Schaefer, R.S., Farquhar, J., Blokland, Y., Sadakata, M., Desain, P.: Name that tune: decoding music from the listening brain. Neuroimage 56(2), 843–849 (2011)CrossRefGoogle Scholar
  6. 6.
    Sturm, I., Dähne, S., Blankertz, B., Curio, G.: Multi-variate EEG analysis as a novel tool to examine brain responses to naturalistic music stimuli. PLoS One 10(10) (2015)Google Scholar
  7. 7.
    Cong, F., Alluri, V., Nandi, A.K., Toiviainen, P., Rui, F., Abu-Jamous, B., et al.: Linking brain responses to naturalistic music through analysis of ongoing EEG and stimulus features. IEEE Trans. Multimedia 15(5), 1060–1069 (2013)CrossRefGoogle Scholar
  8. 8.
    Cong, F., Phan, A.H., Zhao, Q., Nandi, A.K., Alluri, V., Toiviainen, P., et al.: Analysis of ongoing EEG elicited by natural music stimuli using nonnegative tensor factorization. In: Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European, pp. 494–498. IEEE (2012)Google Scholar
  9. 9.
    Rodriguez, E., George, N., Lachaux, J.P., Martinerie, J., Renault, B., Varela, F.J.: Perception’s shadow: long-distance synchronization of human brain activity. Nature 397(6718), 430–433 (1999)CrossRefGoogle Scholar
  10. 10.
    Li, J., Zhang, L.: Phase interval value analysis for the motor imagery task in BCI. J. Circuits Syst. Comput. 18(8), 1441–1452 (2009)CrossRefGoogle Scholar
  11. 11.
    Alluri, V., Toiviainen, P., Jääskeläinen, I.P., Glerean, E., Sams, M., Brattico, E.: Large-scale brain networks emerge from dynamic processing of musical timbre, key and rhythm. Neuroimage 59(4), 3677–3689 (2012)CrossRefGoogle Scholar
  12. 12.
    Lartillot, O., Toiviainen, P.: MIR in matlab (II): a toolbox for musical feature extraction from audio. In: Proceedings of the International Conference on Music Information Retrieval 2007, pp. 237–244 (2007)Google Scholar
  13. 13.
    Bhattacharya, J., Petsche, H., Feldmann, U., Rescher, B.: EEG gamma-band phase synchronization between posterior and frontal cortex during mental rotation in humans. Neurosci. Lett. 311, 29–32 (2001)CrossRefGoogle Scholar
  14. 14.
    Mormann, F., Kreuz, T., Andrzejak, R., David, P., Lehnertz, K., Elger, C.: Epileptic seizures are preceded by a decrease in synchronization. Epilepsy Res. 53, 173–185 (2003)CrossRefGoogle Scholar
  15. 15.
    Gysels, E., Celka, P.: Phase synchronization for the recognition of mental tasks in a brain-computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 12(4), 406–415 (2005). A Publication of the IEEE Engineering in Medicine and Biology SocietyCrossRefGoogle Scholar
  16. 16.
    Cong, F., Lin, Q.H., Kuang, L.D., Gong, X.F., Astikainen, P., Ristaniemi, T.: Tensor decomposition of EEG signals: a brief review. J. Neurosci. Methods 248, 59–69 (2015)CrossRefGoogle Scholar
  17. 17.
    Ji, H., Li, J., Lu, R., Gu, R., Cao, L., Gong, X.: EEG classification for hybrid brain-computer interface using a tensor based multiclass multimodal analysis scheme. Comput. Intell. Neurosci. 2016, 1–15 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jie Li
    • 1
  • Hongfei Ji
    • 1
    Email author
  • Rong Gu
    • 1
  • Lusong Hou
    • 1
  • Zhicheng Zhang
    • 1
  • Qiang Wu
    • 2
  • Rongrong Lu
    • 3
  • Maozhen Li
    • 4
  1. 1.Department of Computer Science and TechnologyTong Ji UniversityShanghaiPeople’s Republic of China
  2. 2.School of Information Science and EngineeringShandong UniversityJinanPeople’s Republic of China
  3. 3.Department of Rehabilitation, Huashan HospitalFudan UniversityShanghaiChina
  4. 4.Department of Electronic and Computer EngineeringBrunel UniversityLondonUK

Personalised recommendations