Abstract: To listen to brain activity as a piece of music, we proposed the scale-free brainwave music (SFBM) technology, which could translate the scalp electroencephalogram (EEG) into music notes according to the power law of both EEG and music. In the current study, this methodology was further extended to a musical ensemble of two channels. First, EEG data from two selected channels are translated into musical instrument digital interface (MIDI) sequences, where the EEG parameters modulate the pitch, duration, and volume of each musical note. The phase synchronization index of the two channels is computed by a Hilbert transform. Then the two MIDI sequences are integrated into a chorus according to the phase synchronization index. The EEG with a high synchronization index is represented by more consonant musical intervals, while the low index is expressed by inconsonant musical intervals. The brain ensemble derived from real EEG segments illustrates differences in harmony and pitch distribution during the eyes-closed and eyes-open states. Furthermore, the scale-free phenomena exist in the brainwave ensemble. Therefore, the scale-free brain ensemble modulated by phase synchronization is a new attempt to express the EEG through an auditory and musical way, and it can be used for EEG monitoring and bio-feedback.

基于相位同步的无标度合奏脑音乐

[1]Adrian, E.D., Matthews, B.H.C., 1934. The Berger rhythm: potential changes from the occipital lobes in man. Brain, 57(4):355-385.

[2]Baier, G., Hermann, T., Stephani, U., 2007. Event-based sonification of EEG rhythms in real time. Clin. Neurophysiol., 118(6):1377-1386.

[3]Banich, M.T., Compton, R.J., 2010. Cognitive Neuroscience (3rd Ed.). Cengage Learning, Wadsworth, USA.

[4]Beggs, J.M., Plenz, D., 2003. Neuronal avalanches in neocortical circuits. J. Neurosci., 23(35):11167-11177.

[5]Chen, Y., Ding, M., Kelso, J.A.S., 1997. Long memory processes (1/f^α type) in human coordination. Phys. Rev. Lett., 79(22):4501-4504.

[6]Ciuciu, P., Varoquaux, G., Abry, P., et al., 2012. Scale-free and multifractal time dynamics of fMRI signals during rest and task. Front. Physiol., 3:186:1-186:18.

[7]Fechner, G., Adler, H.E., Howes, D.H., et al., 1966. Elements of Psychophysics. Holt, Rinehart and Winston, New York, USA.

[8]Freeman, W.J., Holmes, M.D., West, G.A., et al., 2006. Fine spatiotemporal structure of phase in human intracranial EEG. Clin. Neurophysiol., 117(6):1228-1243.

[9]Gong, P., Nikolaev, A.R., van Leeuwen, C., 2003. Scale-invariant fluctuations of the dynamical synchronization in human brain electrical activity. Neurosci. Lett., 336(1):33-36.

[10]Grigolini, P., Aquino, G., Bologna, M., et al., 2009. A theory of 1/f noise in human cognition. Phys. A, 388(19): 4192-4204.

[11]He, B.J., Zempel, J.M., Snyder, A.Z., et al., 2010. The temporal structures and functional significance of scale-free brain activity. Neuron, 66(3):353-369.

[12]Hennig, H., Fleischmann, R., Fredebohm, A., et al., 2011. The nature and perception of fluctuations in human musical rhythms. PLoS ONE, 6(10):e26457.

[13]Hermann, T., Baier, G., 2013. Sonification of the human EEG. In: Franinović, K., Serafin, S. (Eds.), Sonic Interaction Design. MIT Press, Cambridge, p.285-297.

[14]Hinterberger, T., Baier, G., 2005. Parametric orchestral sonification of EEG in real time. IEEE Multim., 12(2):70-79.

[15]Hsü, K.J., Hsü, A., 1990. Fractal geometry of music. Proc. Nat. Acad. Sci. USA, 87(3):938-941.

[16]Hsü, K.J., Hsü, A., 1991. Self-similarity of the “1/f noise” called music. Proc. Nat. Acad. Sci. USA, 88(8):3507-3509.

[17]Hwa, R.C., Ferree, T.C., 2002. Scaling properties of fluctuations in the human electroencephalogram. Phys. Rev. E, 66(2):021901.

[18]Klonowski, W., Duch, W., Perovic, A., et al., 2009. Some computational aspects of the brain computer interfaces based on inner music. Comput. Intell. Neurosci., 2009:950403.

[19]Levitin, D.J., Chordia, P., Menon, V., 2012. Musical rhythm spectra from Bach to Joplin obey a 1/f power law. Proc. Nat. Acad. Sci. USA, 109(10):3716-3720.

[20]Liu, L., Wei, J., Zhang, H., et al., 2013. A statistical physics view of pitch fluctuations in the classical music from Bach to Chopin: evidence for scaling. PLoS ONE, 8(3):e58710.

[21]Liu, X.F., Tse, C.K., Small, M., 2010. Complex network structure of musical compositions: algorithmic generation of appealing music. Phys. A, 389(1):126-132.

[22]Lowen, S.B., Liebovitch, L.S., White, J.A., 1999. Fractal ion-channel behavior generates fractal firing patterns in neuronal models. Phys. Rev. E, 59(5):5970-5980.

[23]Lu, J., Wu, D., Yang, H., et al., 2012. Scale-free brain-wave music from simultaneously EEG and fMRI recordings. PLoS ONE, 7(11):e49773.

[24]Manaris, B., Romero, J., Machado, P., et al., 2005. Zipf’s law, music classification, and aesthetics. Comput. Music J., 29(1):55-69.

[25]Miranda, E.R., 2010. Plymouth brain-computer music interfacing project: from EEG audio mixers to composition informed by cognitive neuroscience. Int. J. Arts Technol., 3(2-3):154-176.

[26]Palva, J.M., Zhigalov, A., Hirvonen, J., et al., 2013. Neuronal long-range temporal correlations and avalanche dynamics are correlated with behavioral scaling laws. Proc. Nat. Acad. Sci. USA, 110(9):3585-3590.

[27]Quiroga, R.Q., Kraskov, A., Kreuz, T., et al., 2002. Performance of different synchronization measures in real data: a case study on electroencephalographic signals. Phys. Rev. E, 65(4):041903.

[28]Roederer, J.G., 2008. The Physics and Psychophysics of Music: an Introduction (4th Ed.). Springer, New York, USA.

[29]Rosenboom, D., 1976. Biofeedback and the Arts, Results of Early Experiments (2nd Ed.). Aesthetic Research Centre of Canada, Vancouver.

[30]Rosenboom, D., 1999. Extended musical interface with the human nervous system: assessment and prospectus. Leonardo, 32(4):257.

[31]Schroeder, M., 2009. Fractals, Chaos, Power Laws: Minutes from an Infinite Paradise. Dover Publications, New York, USA.

[32]Sposobin, I., 1959. Harmony Textbook. Chen, M., translator, 2000. People’s Music Publishing House, Beijing (in Chinese).

[33]Teich, M.C., Heneghan, C., Lowen, S.B., et al., 1997. Fractal character of the neural spike train in the visual system of the cat. J. Opt. Soc. Am. A, 14(3):529-546.

[34]Tian, Y., Yao, D., 2013. Why do we need to use a zero reference? Reference influences on the ERPs of audiovisual effects. Psychophysiology, 50(12):1282-1290.

[35]Torre, K., Wagenmakers, E.J., 2009. Theories and models for 1/f^β noise in human movement science. Hum. Movement Sci., 28(3):297-318.

[36]Väljamäe, A., Steffert, T., Holland, S., et al., 2013. A review of real-time EEG sonification research. Int. Conf. on Auditory Display, p.85-93.

[37]Vialatte, F.B., Cichocki, A., 2006. 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. Proc. 13th Int. Conf. on Neural Information Processing, p.92-101.

[38]Voss, R.F., Clarke, J., 1978. “1/f noise” in music: music from 1/f noise. J. Acoust. Soc. Am., 63:258-263.

[39]WMA, 1964. WMA Declaration of Helsinki-Ethical Principles for Medical Research Involving Human Subjects. Available from http://www.wma.net/en/30publications/10policies/b3/index.html.

[40]Wu, D., Li, C., Yao, D., 2009. Scale-free music of the brain. PLoS ONE, 4(6):e5915.

[41]Wu, D., Li, C., Yin, Y., et al., 2010. Music composition from the brain signal: representing the mental state by music. Comput. Intell. Neurosci., 2010:267671.

[42]Wu, D., Shi, X., Hu, J., et al., 2011. Listen to the song of the brain in real time: the Chengdu brainwave music. Proc 8th Int. Symp. on Noninvasive Functional Source Imaging of the Brain and Heart & 8th Int. Conf. on Bioelectromagnetism, p.135-138.

[43]Wu, D., Li, C., Yao, D., 2013a. An ensemble with the Chinese pentatonic scale using electroencephalogram from both hemispheres. Neurosci. Bull., 29(5):581-587.

[44]Wu, D., Li, C., Yao, D., 2013b. Scale-free brain quartet: artistic filtering of multi-channel brainwave music. PLoS ONE, 8(5):e64046.

[45]Yao, D., 2001. A method to standardize a reference of scalp EEG recordings to a point at infinity. Physiol. Meas., 22:693.