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Genesis of Universality of Music: Effect of Cross Cultural Instrumental Clips

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Musicality of Human Brain through Fractal Analytics

Part of the book series: Signals and Communication Technology ((SCT))

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Abstract

Music has been present in human culture since time immemorial, some say music came even before speech. The effort to understand the wide variety of emotions evoked by music has started not long back. With the advent and rapid growth of various neurological bio-sensors we can now attempt to quantify various dimensions of emotional experience induced by music especially instrumental music—since it is free from any language barriers. In this study, we took eight (8) cross cultural instrumental clips originating mainly from Indian and Western music. A listening test comprising of 100 participants across the globe was conducted to associate each clip with its corresponding emotional valence. The participants were asked to mark each clip according to their perception of four basic emotions (joy/sorrow and anxiety/serenity) invoked by each instrumental clip. EEG study was then conducted on 20 participants to measure the response evoked by the same instrumental clips in the alpha and theta frequency regions. We took the help of latest non-linear multifractal analysis technique—MFDFA to estimate the change in multifractal spectral width (corresponding to alpha as well as theta waves) associated with each of the clips in frontal, temporal and occipital lobes. The response in the alpha domain reveals a hint in the direction of universality of music, while in theta domain we have culture specific response. Moreover, we tried to develop alpha as well as theta multifractal spectral width as a single parameter with which we can quantify the valence and arousal based effects corresponding to a particular musical clip. The results and implications are discussed in detail.

I sing the body electric…

…in the depth of my soul there is a wordless song….

—Walt Whitman

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References

  • Aftanas, L. I., & Golocheikine, S. A. (2001). Human anterior and frontal midline theta and lower alpha reflect emotionally positive state and internalized attention: high-resolution EEG investigation of meditation. Neuroscience Letters, 310(1), 57–60.

    Article  Google Scholar 

  • Akin, M., Arserim, M. A., Kiymik, M. K., & Turkoglu, I. (2001). A new approach for diagnosing epilepsy by using wavelet transform and neural networks. In Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE (Vol. 2, pp. 1596–1599). New York: IEEE.

    Google Scholar 

  • Ashkenazy, Y., Ivanov, P. C., Havlin, S., Peng, C. K., Goldberger, A. L., & Stanley, H. E. (2001). Magnitude and sign correlations in heartbeat fluctuations. Physical Review Letters, 86(9), 1900.

    Article  Google Scholar 

  • Balkwill, L. L., & Thompson, W. F. (1999). A cross-cultural investigation of the perception of emotion in music: Psychophysical and cultural cues. Music Perception, 43–64.

    Google Scholar 

  • Balkwill, L. L., Thompson, W. F., & Matsunaga, R. I. E. (2004). Recognition of emotion in Japanese, Western, and Hindustani music by Japanese listeners. Japanese Psychological Research, 46(4), 337–349.

    Article  Google Scholar 

  • Banerjee, A., Sanyal, S., Patranabis, A., Banerjee, K., Guhathakurta, T., Sengupta, Sengupta R., et al. (2016). Study on brain dynamics by non linear analysis of music induced EEG Signals. Physica A: Statistical Mechanics and Its Applications, 444, 110–120.

    Article  Google Scholar 

  • Blesić, S., Milošević, S., Stratimirović, D., & Ljubisavljević, M. (1999). Detrended fluctuation analysis of time series of a firing fusimotor neuron. Physica A: Statistical Mechanics and its Applications, 268(3), 275–282. doi:10.1016/S0378-4371(99)00110-7.

    Article  Google Scholar 

  • Blood, A. J., Zatorre, R. J., Bermudez, P., & Evans, A. C. (1999). Emotional responses to pleasant and unpleasant music correlate with activity in paralimbic brain regions. Nature Neuroscience, 2(4), 382–387.

    Article  Google Scholar 

  • Bukofzer, M. F. (2013). Music in the Baroque Era-from Monteverdi to Bach. Read Books Ltd.

    Google Scholar 

  • Chamorro-Premuzic, T., Swami, V., Terrado, A., & Furnham, A. (2009). The effects of background auditory interference and extraversion on creative and cognitive task performance. International Journal of Psychological Studies, 1(2), p2.

    Article  Google Scholar 

  • Chen, Z., Ivanov, P. C., Hu, K., & Stanley, H. E. (2002). Effect of nonstationarities on detrended fluctuation analysis. Physical Review E, 65, 041107. doi:10.1103/PhysRevE.65.041107.

    Article  Google Scholar 

  • Chen, X., Liu, A., Peng, H., & Ward, R. K. (2014). A Preliminary study of muscular artifact cancellation in single-channel EEG. Sensors, 14(10), 18370–18389.

    Article  Google Scholar 

  • Conte, E., Khrennikov, A., Federici, A., & Zbilut, J. P. (2009). Fractal fluctuations and quantum-like chaos in the brain by analysis of variability of brain waves: A new method based on a fractal variance function and random matrix theory: A link with El Naschie fractal Cantorian space–time and V. Weiss and H. Weiss golden ratio in brain. Chaos, Solitons & Fractals, 41(5), 2790–2800.

    Article  Google Scholar 

  • Davis, A., Marshak, A., Wiscombe, W., & Cahalan, R. (1994). Multifractal characterizations of nonstationarity and intermittency in geophysical fields: Observed, retrieved, or simulated. Journal of Geophysical Research: Atmospheres (1984–2012), 99(D4), 8055–8072.

    Article  Google Scholar 

  • Dutta, S., Ghosh, D., & Chatterjee, S. (2013). Multifractal detrended fluctuation analysis of human gait diseases. Frontiers in physiology, 4.

    Google Scholar 

  • Dutta, S., Ghosh, D., Samanta, S., & Dey, S. (2014). Multifractal parameters as an indication of different physiological and pathological states of the human brain. Physica A: Statistical Mechanics and its Applications, 396, 155–163.

    Google Scholar 

  • Erkkilä, J., Gold, C., Fachner, J., Ala-Ruona, E., Punkanen, M., & Vanhala, M. (2008). The effect of improvisational music therapy on the treatment of depression: Protocol for a randomised controlled trial. BMC Psychiatry, 8(1), 50.

    Article  Google Scholar 

  • Figliola, A., Serrano, E., & Rosso, O. A. (2007a). Multifractal detrented fluctuation analysis of tonic-clonic epileptic seizures. The European Physical Journal Special Topics, 143(1), 117–123.

    Article  Google Scholar 

  • Figliola, A., Serrano, E., Rostas, J. A. P., Hunter, M., & Rosso, O. A. (2007, May). Study of EEG brain maturation signals with multifractal detrended fluctuation analysis. In Nonequilibrium Statistical Mechanics and Nonlinear Physics (AIP Conference Proceedings Volume 913) (Vol. 913, pp. 190–195).

    Google Scholar 

  • Fritz, T., Jentschke, S., Gosselin, N., Sammler, D., Peretz, I., Turner, R., et al. (2009). Universal recognition of three basic emotions in music. Current Biology, 19(7), 573–576.

    Article  Google Scholar 

  • Gao, T., Wu, D., Huang, Y., & Yao, D. (2007). Detrended fluctuation analysis of the human EEG during listening to emotional music. Journal of Electronic Science and Technology, 5, 272–277.

    Google Scholar 

  • Gregory, A. H., & Varney, N. (1996). Cross-cultural comparisons in the affective response to music. Psychology of Music, 24(1), 47–52.

    Article  Google Scholar 

  • Hallam, S. (2010). The power of music: Its impact on the intellectual, social and personal development of children and young people. International Journal of Music Education, 28(3), 269–289.

    Article  Google Scholar 

  • Hamann, S., & Mao, H. (2002). Positive and negative emotional verbal stimuli elicit activity in the left amygdala. NeuroReport, 13(1), 15–19.

    Article  Google Scholar 

  • Hauser, M. D., & McDermott, J. (2003). The evolution of the music faculty: A comparative perspective. Nature Neuroscience, 6(7), 663–668.

    Article  Google Scholar 

  • Hazarika, N., Chen, J. Z., Tsoi, A. C., & Sergejew, A. (1997, July). Classification of EEG signals using the wavelet transform. In 1997 13th International Conference on Digital Signal Processing Proceedings, 1997. DSP 97 (Vol. 1, pp. 89–92). New York: IEEE.

    Google Scholar 

  • http://www.menuhin.org/.

  • Hu, K., Ivanov, P. C., Chen, Z., Carpens, P., & Stanley, H. E. (2001). Effects of trends on detrended fluctuation analysis. Physical Review E, 64, 011114. doi:10.1103/PhysRevE.64.011114.

    Article  Google Scholar 

  • Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., et al. (1998, March). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences (Vol. 454(1971), pp. 903–995). The Royal Society.

    Google Scholar 

  • Hyde, K. L., Lerch, J., Norton, A., Forgeard, M., Winner, E., Evans, A. C., et al. (2009). Musical training shapes structural brain development. The Journal of Neuroscience, 29(10), 3019–3025.

    Article  Google Scholar 

  • Ihlen, E. A. (2012). Introduction to multifractal detrended fluctuation analysis in Matlab. Frontiers in physiology, 3.

    Google Scholar 

  • Juslin, P. N., & Laukka, P. (2004). Expression, perception, and induction of musical emotions: A review and a questionnaire study of everyday listening. Journal of New Music Research, 33(3), 217–238.

    Article  Google Scholar 

  • Kantelhardt, J. W., Bunde, E. K., Henio, H. A. R., Havlin, S., & Bunde, A. (2001). Detecting long-range correlations with detrended fluctuation analysis. Physica A, 295, 441–454. doi:10.1016/S0378-4371(01)00144-3.

    Article  MATH  Google Scholar 

  • Kantelhardt, J. W., Rybski, D., Zschiegner, S. A., Braun, P., Bunde, E. K., Livina, V., et al. (2003). Multifractality of river runoff and precipitation: Comparison of fluctuation analysis and wavelet methods. Physica A, 330, 240–245. doi:10.1016/j.physa.2003.08.019.

    Article  MATH  Google Scholar 

  • Kantelhardt, J. W., Zschiegner, S. A., Bunde, E. K., Havlin, S., Bunde, A., & Stanley, H. E. (2002). Multifractal detrended fluctuation analysis of nonstationary time series. Physica A, 316, 87–114. doi:10.1016/S0378-4371(02)01383-3.

    Article  MATH  Google Scholar 

  • Karthick, N. G., Thajudin, A. V. I., & Joseph, P. K. (2006, December). Music and the EEG: A study using nonlinear methods. In International Conference on Biomedical and Pharmaceutical Engineering, 2006. ICBPE 2006 (pp. 424–427). New York: IEEE.

    Google Scholar 

  • Khalfa, S., Guye, M., Peretz, I., Chapon, F., Girard, N., Chauvel, P., et al. (2008a). Evidence of lateralized anteromedial temporal structures involvement in musical emotion processing. Neuropsychologia, 46(10), 2485–2493.

    Article  Google Scholar 

  • Khalfa, S., Schon, D., Anton, J. L., & Liégeois-Chauvel, C. (2005). Brain regions involved in the recognition of happiness and sadness in music. NeuroReport, 16(18), 1981–1984.

    Article  Google Scholar 

  • Khalfa, Stéphanie, et al. (2008b). Evidence of lateralized anteromedial temporal structures involvement in musical emotion processing. Neuropsychologia, 46(10), 2485–2493.

    Article  Google Scholar 

  • Kim, J., & André, E. (2008). Emotion recognition based on physiological changes in music listening. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(12), 2067–2083.

    Article  Google Scholar 

  • Klonowski, W. (2009). Everything you wanted to ask about EEG but were afraid to get the right answer. Nonlinear Biomedical Physics, 3(1), 2.

    Article  MathSciNet  Google Scholar 

  • Koelsch, S., Fritz, T., Müller, K., & Friederici, A. D. (2006). Investigating emotion with music: An fMRI study. Human Brain Mapping, 27(3), 239–250.

    Article  Google Scholar 

  • Kosslyn, S. M., & Pylyshyn, Z. (1994). Image and brain: The resolution of the imagery debate. Nature, 372(6503), 289.

    Article  Google Scholar 

  • Looney, D., Li, L., Rutkowski, T. M., Mandic, D. P., & Cichocki, A. (2008). Ocular artifacts removal from EEG using EMD. In Advances in Cognitive Neurodynamics ICCN 2007 (pp. 831–835). Netherlands: Springer.

    Google Scholar 

  • Loui, P. (2013). The Role of Brain Connectivity in Musical Experience. Positive Neuroscience: Oxford University Press.

    Google Scholar 

  • Maity, A. K., Pratihar, R., Mitra, A., Dey, S., Agrawal, V., Sanyal, S., et al. (2015). Multifractal detrended fluctuation analysis of alpha and theta EEG rhythms with musical stimuli. Chaos, Solitons & Fractals, 81, 52–67.

    Article  MathSciNet  Google Scholar 

  • Mathur, A., Vijayakumar, S. H., Chakrabarti, B., & Singh, N. C. (2015). Emotional responses to Hindustani raga music: The role of musical structure. Frontiers in psychology, 6.

    Google Scholar 

  • Mellet, E., Tzourio, N., Denis, M., & Mazoyer, B. (1995). A positron emission tomography study of visual and mental spatial exploration. Journal of Cognitive Neuroscience, 7(4), 433–445.

    Article  Google Scholar 

  • Miller, I. (2004). John E. Freund’s Mathematical statistics: With applications. Pearson Education India.

    Google Scholar 

  • Olson, I. R., Plotzker, A., & Ezzyat, Y. (2007). The enigmatic temporal pole: a review of findings on social and emotional processing. Brain, 130(7), 1718–1731.

    Article  Google Scholar 

  • Ossadnik, S. M., Buldyrev, S. V., Goldberger, A. L., Havlin, S., Mantegna, R. N., Peng, C. K., et al. (1994). Correlation approach to identify coding regions in DNA sequences. Biophysical Journal, 67(1), 64.

    Article  Google Scholar 

  • Ozdemir, L., & Akdemir, N. (2009). Effects of multisensory stimulation on cognition, depression and anxiety levels of mildly-affected alzheimer’s patients. Journal of the Neurological Sciences, 283(1), 211–213.

    Article  Google Scholar 

  • Peng, C. K., Buldyrev, S. V., Havlin, S., Simons, M., Stanley, H. E., & Goldberger, A. L. (1994). Mosaic organization of DNA nucleotides. Physical Review E, 49(2), 1685.

    Article  Google Scholar 

  • Peretz, I. (2006). The nature of music from a biological perspective. Cognition, 100(1), 1–32.

    Article  MathSciNet  Google Scholar 

  • Phillips, C. (2004). Does background music impact computer task performance? Usability News, 6(1), 1–4.

    MathSciNet  Google Scholar 

  • Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161.

    Article  Google Scholar 

  • Russell, J. A. (1989). Measures of emotion. In R. Plutchik & H. Kellerman (Eds.), Emotion: Theory, research, and experience (Vol. 4, pp. 83–111). Toronto: Academic.

    Google Scholar 

  • Sadegh Movahed M., Jafari G. R., Ghasemi F., Rahvar S., Reza Rahimi T. M. (2006). Multifractal detrended fluctuation analysis of sunspot time series. Journal of Statistical Mechanics, 0602:P02003. doi: 10.1088/1742-5468/2006/02/P02003.

  • Salimpoor, V. N., Benovoy, M., Longo, G., Cooperstock, J. R., & Zatorre, R. J. (2009). The rewarding aspects of music listening are related to degree of emotional arousal. PLoS ONE, 4(10), e7487.

    Article  Google Scholar 

  • Sammler, D., Grigutsch, M., Fritz, T., & Koelsch, S. (2007). Music and emotion: Electrophysiological correlates of the processing of pleasant and unpleasant music. Psychophysiology, 44(2), 293–304.

    Article  Google Scholar 

  • Scherer, K. R. (2004). Which emotions can be induced by music? What are the underlying mechanisms? And how can we measure them? Journal of New Music Research, 33(3), 239–251.

    Article  Google Scholar 

  • Schlaug, G., Norton, A., Overy, K., & Winner, E. (2005). Effects of music training on the child’s brain and cognitive development. Annals of the New York Academy of Sciences, 1060(1), 219–230.

    Article  Google Scholar 

  • Schmidt, B., & Hanslmayr, S. (2009). Resting frontal EEG alpha-asymmetry predicts the evaluation of affective musical stimuli. Neuroscience Letters, 460(3), 237–240.

    Article  Google Scholar 

  • Schmidt, L. A., & Trainor, L. J. (2001). Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions. Cognition and Emotion, 15(4), 487–500.

    Article  Google Scholar 

  • Stanley, H. E., Amaral, L. N., Goldberger, A. L., Havlin, S., Ivanov, P. C., & Peng, C. K. (1999). Statistical physics and physiology: Monofractal and multifractal approaches. Physica A: Statistical Mechanics and its Applications, 270(1), 309–324.

    Article  Google Scholar 

  • Telesca, L., Lapenna, V., & Macchiato, M. (2004). Mono- and multi-fractal investigation of scaling properties in temporal patterns of seismic sequences. Chaos, Solitons & Fractals, 19, 1–15. doi:10.1016/S0960-0779(03)00188-7.

    Article  MATH  Google Scholar 

  • Trainor, L. J., & Schmidt, L. A. (2003). Processing emotions induced by music. The Cognitive Neuroscience of Music, 310–324.

    Google Scholar 

  • Trehub, S. E. (2003). The developmental origins of musicality. Nature Neuroscience, 6(7), 669–673.

    Article  Google Scholar 

  • Voss, J. A., Good, M., Yates, B., Baun, M. M., Thompson, A., & Hertzog, M. (2004). Sedative music reduces anxiety and pain during chair rest after open-heart surgery. Pain, 112(1), 197–203.

    Article  Google Scholar 

  • Wan, C. Y., & Schlaug, G. (2010). Music making as a tool for promoting brain plasticity across the life span. The Neuroscientist, 16(5), 566–577.

    Article  Google Scholar 

  • Wieczorkowska, A. A., Datta, A. K., Sengupta, R., Dey, N., & Mukherjee, B. (2010). On search for emotion in Hindusthani vocal music. In Advances in music information retrieval (pp. 285–304). Berlin, Heidelberg: Springer.

    Google Scholar 

  • Wu, Z., & Huang, N. E. (2009). Ensemble empirical mode decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1(01), 1–41.

    Article  Google Scholar 

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Authors and Affiliations

  1. Sir C.V. Raman Centre for Physics and Music, Jadavpur University, Kolkata, West Bengal, India

    Dipak Ghosh

  2. Sir C.V. Raman Centre for Physics and Music, Jadavpur University, Kolkata, West Bengal, India

    Ranjan Sengupta

  3. Sir C.V. Raman Centre for Physics and Music, Jadavpur University, Kolkata, West Bengal, India

    Shankha Sanyal

  4. Sir C.V. Raman Centre for Physics and Music, Jadavpur University, Kolkata, West Bengal, India

    Archi Banerjee

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  1. Dipak Ghosh
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Ghosh, D., Sengupta, R., Sanyal, S., Banerjee, A. (2018). Genesis of Universality of Music: Effect of Cross Cultural Instrumental Clips. In: Musicality of Human Brain through Fractal Analytics. Signals and Communication Technology. Springer, Singapore. https://doi.org/10.1007/978-981-10-6511-8_6

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