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. 2013 Apr 20;29(5):581–587. doi: 10.1007/s12264-013-1334-y

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

Dan Wu 1, Chao-Yi Li 1,2, De-Zhong Yao 1,✉
PMCID: PMC5561954  PMID: 23604597

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

References

  • [1].Adrian ED, Matthews BHC. The Berger rhythm: potential changes from the occipital lobes in man. Brain. 1934;57(4):355. doi: 10.1093/brain/57.4.355. [DOI] [PubMed] [Google Scholar]
  • [2].Rosenboom D. Biofeedback and the Arts, Results of Early Experiments. Vancouver: Aesthetic Research Centre of Canada; 1976. pp. 27–109. [Google Scholar]
  • [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. pp. 26–101. [Google Scholar]
  • [4].Hermann T. Sonification for exploratory data analysis. Bielefeld: Bielefeld University; 2002. pp. 31–44. [Google Scholar]
  • [5].Hinterberger T, Baier G. Parametric orchestral sonification of EEG in real time. IEEE Multimedia. 2005;12(2):70–79. doi: 10.1109/MMUL.2005.36. [DOI] [Google Scholar]
  • [6].Wu D, Li C, Yao D. Scale-free music of the brain. PLoS One. 2009;4(6):e5915. doi: 10.1371/journal.pone.0005915. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [7].Baier G, Hermann T, Stephani U. Event-based sonification of EEG rhythms in real time. Clin Neurophysiol. 2007;118(6):1377–1386. doi: 10.1016/j.clinph.2007.01.025. [DOI] [PubMed] [Google Scholar]
  • [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):e49773. doi: 10.1371/journal.pone.0049773. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [9].Wu D, Li C, Yin Y, Zhou C, Yao D. Comput Intell Neurosci. 2010. Music composition from the brain signal: representing the mental state by music. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [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. doi: 10.1504/IJART.2010.032562. [DOI] [Google Scholar]
  • [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. doi: 10.1007/11893295_11. [DOI] [Google Scholar]
  • [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. pp. 1–57. [Google Scholar]
  • [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. doi: 10.1258/la.2010.009081. [DOI] [PubMed] [Google Scholar]
  • [14].Yao D. A method to standardize a reference of scalp EEG recordings to a point at infinity. Physiol Meas. 2001;22:693. doi: 10.1088/0967-3334/22/4/305. [DOI] [PubMed] [Google Scholar]
  • [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. doi: 10.1016/j.clinph.2010.03.056. [DOI] [PubMed] [Google Scholar]
  • [16].Fechner GT. Elements of psychophysics. In: Howes DH, Boring EG, editors. Elements of Psychophysics (trans. Adler HE) New York: Holt, Rinehart and Winston; 1966. pp. 113–170. [Google Scholar]
  • [17].Schoenberg A. Theory of Harmony. Ewing: University of California Press; 1983. pp. 18–286. [Google Scholar]
  • [18].Armitage R. The distribution of EEG frequencies in REM and NREM sleep stages in healthy young adults. Sleep. 1995;18(5):334–341. doi: 10.1093/sleep/18.5.334. [DOI] [PubMed] [Google Scholar]
  • [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. doi: 10.1016/j.brainres.2010.09.090. [DOI] [PubMed] [Google Scholar]

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