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)


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.


EEG Phase Music Tensor 



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).


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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

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