Advertisement

Neuroscience Bulletin

, Volume 29, Issue 5, pp 581–587 | Cite as

An ensemble with the chinese pentatonic scale using electroencephalogram from both hemispheres

  • Dan Wu
  • Chao-Yi Li
  • De-Zhong YaoEmail author
Original Article
First Online:

Abstract

To listen to brain activity as a piece of music, we previously proposed scale-free brainwave music (SFBM) technology, which translated the scalp electroencephalogram (EEG) into musical notes according to the power law of both the EEG and music. In this study, the methodology was further extended to ensemble music on two channels from the two hemispheres. EEG data from two channels symmetrically located on the left and right hemispheres were translated into MIDI sequences by SFBM, and the EEG parameters modulated the pitch, duration and volume of each note. Then, the two sequences were filtered into an ensemble with two voices: the pentatonic scale (traditional Chinese music) or the heptatonic scale (standard Western music). We demonstrated differences in harmony between the two scales generated at different sleep stages, with the pentatonic scale being more harmonious. The harmony intervals of this brain ensemble at various sleep stages followed the power law. Compared with the heptatonic scale, it was easier to distinguish the different stages using the pentatonic scale. These results suggested that the hemispheric ensemble can represent brain activity by variations in pitch, tempo and harmony. The ensemble with the pentatonic scale sounds more consonant, and partially reflects the relations of the two hemispheres. This can be used to distinguish the different states of brain activity and provide a new perspective on EEG analysis.

Keywords

electroencephalogram music power law ensemble Chinese pentatonic scale 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Adrian ED, Matthews BHC. The Berger rhythm: potential changes from the occipital lobes in man. Brain 1934, 57(4): 355.CrossRefGoogle Scholar
  2. [2]
    Rosenboom D. Biofeedback and the Arts, Results of Early Experiments. Vancouver: Aesthetic Research Centre of Canada, 1976: 27–109.Google Scholar
  3. [3]
    Rosenboom D. Extended Musical Interface with the Human Nervous System: Assessment and Prospectus. San Francisco: International Society for the Arts, Sciences and Technology, 1997: 26–101.Google Scholar
  4. [4]
    Hermann T. Sonification for exploratory data analysis. Bielefeld: Bielefeld University, 2002: 31–44.Google Scholar
  5. [5]
    Hinterberger T, Baier G. Parametric orchestral sonification of EEG in real time. IEEE Multimedia 2005, 12(2): 70–79.CrossRefGoogle Scholar
  6. [6]
    Wu D, Li C, Yao D. Scale-free music of the brain. PLoS One 2009, 4(6): e5915.PubMedCrossRefGoogle Scholar
  7. [7]
    Baier G, Hermann T, Stephani U. Event-based sonification of EEG rhythms in real time. Clin Neurophysiol 2007, 118(6): 1377–1386.PubMedCrossRefGoogle Scholar
  8. [8]
    Lu J, Wu D, Yang H, Luo C, Li C, Yao D. Scale-free brain-wave music from simultaneously EEG and fMRI recordings. PLoS One 2012, 7(11): e49773PubMedCrossRefGoogle Scholar
  9. [9]
    Wu D, Li C, Yin Y, Zhou C, Yao D. Music composition from the brain signal: representing the mental state by music. Comput Intell Neurosci 2010. Doi: 10.1155/2010/267671.Google Scholar
  10. [10]
    Miranda ER. Plymouth brain-computer music interfacing project: from EEG audio mixers to composition informed by cognitive neuroscience. Int J Arts Technol 2010, 3(2): 154–176.CrossRefGoogle Scholar
  11. [11]
    Vialatte F, Cichocki A. Sparse bump sonification: a new tool for multichannel eeg diagnosis of mental disorders; application to the detection of the early stage of Alzheimer’s disease. Neural Inf Process 2006, 4234: 92–101.CrossRefGoogle Scholar
  12. [12]
    Rechtschaffen A, Kales A. A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. Los Angeles: Brain Information Service, 1968: 1–57.Google Scholar
  13. [13]
    Fang G, Xia Y, Zhang C, Liu T, Yao D. Optimized single electroencephalogram channel sleep staging in rats. Lab Anim 2010, 44(4): 312–322.PubMedCrossRefGoogle Scholar
  14. [14]
    Yao D. A method to standardize a reference of scalp EEG recordings to a point at infinity. Physiol Meas 2001, 22: 693.PubMedCrossRefGoogle Scholar
  15. [15]
    Qin Y, Xu P, Yao D. A comparative study of different for EEG default mode network: The use of the infinity reference. Clin Neurophysiol 2010, 121(12): 1981–1991.PubMedCrossRefGoogle Scholar
  16. [16]
    Fechner GT. Elements of psychophysics. In: Howes DH, Boring EG (Eds.). Elements of Psychophysics (trans. Adler HE). New York: Holt, Rinehart and Winston, 1966: 113–170.Google Scholar
  17. [17]
    Schoenberg A. Theory of Harmony. Ewing: University of California Press, 1983: 18–286.Google Scholar
  18. [18]
    Armitage R. The distribution of EEG frequencies in REM and NREM sleep stages in healthy young adults. Sleep 1995, 18(5): 334–341.PubMedGoogle Scholar
  19. [19]
    Flo E, Steine I, Blagstad T, Gronli J, Pallesen S, Portas CM. Transient changes in frontal alpha asymmetry as a measure of emotional and physical distress during sleep. Brain Res 2011, 1367: 234–249.PubMedCrossRefGoogle Scholar

Copyright information

© Shanghai Institutes for Biological Sciences, CAS and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.Center for Life Sciences, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina

Personalised recommendations

Cite article

Buy options