Abstract
Electroencephalography (EEG) was performed on 10 participants using a simple acoustical stimuli i.e., a Tanpura drone. The Tanpura drone is free from any semantic content and is used with a hypothesis that it provides a specific resting environment for the listeners. The EEG data was extracted for all the frontal electrodes viz. F3, F4, F7, F8, Fp1, Fp2 and Fz. Empirical Mode Decomposition (EMD) is applied on the acquired raw EEG signal to make it noise free as far as possible. Wavelet Transform (WT) technique was used to segregate alpha and theta brain rhythms from the denoised EEG signal. Non linear analysis in the form of Multifractal Detrended Fluctuation Analysis (MFDFA) was carried out on the extracted alpha and theta time series data to study the variation of their complexity. It was found that in all the frontal electrodes alpha as well as theta complexity increases as is evident from the increase of multifractal spectral width. This study is entirely new and gives interesting data regarding neural activation of the alpha and theta brain rhythms while listening to simple acoustical stimuli. The importance of this study lies in the context of finding a baseline for human emotion quantification using multifractal spectral width as a parameter as well as in the field of cognitive music therapy.
Simplicity is the ultimate sophistication
—Leonardo da Vinci
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Banerjee, A., Sanyal, S., Sengupta, R., & Ghosh, D. (2014). Fractal analysis for assessment of complexity of electroencephalography signal due to audio stimuli. Journal of Harmonized Research in Applied Science 2(4), 300-310, ISSN 2321–7456.
Bhattacharyya, K. L., Ghosh, B. K., & Chatterjee, S. K. (1956). Observations on the vibration of the Indian plucked stringed instrument, “Tanpura”. Naturwissenschaften, 43(5), 103–104.
Braeunig, M., Sengupta, R., & Patranabis, A. (2012). On tanpura drone and brain electrical correlates. Speech, Sound and Music Processing: Embracing Research in India, 53–65.
Carterette, E. C., Jairazbhoy, N., & Vaughn, K. (1988). The role of tambura spectra in drone tunings of north indian ragas. The Journal of the Acoustical Society of America, 83(S1), S121–S121.
Carterette, E. C., Vaughn, K., & Jairazbhoy, N. A. (1989). Perceptual, acoustical, and musical aspects of the Tambūrā drone. Music Perception: An Interdisciplinary Journal, 7(2), 75–108.
Chen, Z., Ivanov, P. C., Hu, K., & Stanley, H. E. (2002). Effect of nonstationarities on detrended fluctuation analysis. Physical Review E, 65(4), 041107.
Easwaramoorthy, D., & Uthayakumar, R. (2010, October). Analysis of biomedical EEG signals using wavelet transforms and multifractal analysis. In Communication Control and Computing Technologies (ICCCCT), 2010 IEEE International Conference on (pp. 544–549). IEEE.
Fastl, H., & Zwicker, E. (2007). Psychoacoustics: Facts and Models. Springer series in information sciences. Springer.
Freund, J. E., & Miller, I. (2004). John E. Freund’s mathematical statistics: with applications. India: Pearson Education.
Gao, T. T., Wu, D., Huang, Y. L., & Yao, D. Z. (2007). Detrended fluctuation analysis of the human EEG during listening to emotional music. Journal of Electronic Science and Technology, 5(3), 272–277.
Ghosh, D., Deb, A., Lahiri, M., Patranabis, A., Santra, A. K., Sengupta, R., et al. (2007). Study on the Acoustic Characteristics of Tanpura Sound Signals. Journal of Acoustical Society of India, 34(2 & 3), 77–81.
Houtsma, A. J., & Burns, E. M. (1982). Temporal and spectral characteristics of tambura tones. The Journal of the Acoustical Society of America, 71(S1), S83–S83.
Hu, K., Ivanov, P. C., Chen, Z., Carpena, P., & Stanley, H. E. (2001). Effect of trends on detrended fluctuation analysis. Physical Review E, 64(1), 011114.
Kantelhardt, J. W., Koscielny-Bunde, E., Rego, H. H., Havlin, S., & Bunde, A. (2001). Detecting long-range correlations with detrended fluctuation analysis. Physica A: Statistical Mechanics and its Applications, 295(3), 441–454.
Kantelhardt, J. W., Zschiegner, S. A., Koscielny-Bunde, E., Havlin, S., Bunde, A., & Stanley, H. E. (2002). Multifractal detrended fluctuation analysis of nonstationary time series. Physica A: Statistical Mechanics and its Applications, 316(1), 87–114.
Karthick, N. G., Ahamed, V. T., & Paul, J. K. (2006, December). Music and the EEG: A study using nonlinear methods. In Biomedical and Pharmaceutical Engineering, 2006. ICBPE 2006. International Conference on (pp. 424–427). IEEE.
Lee, J. M., Kim, D. J., Kim, I. Y., Park, K. S., & Kim, S. I. (2002). Detrended fluctuation analysis of EEG in sleep apnea using MIT/BIH polysomnography data. Computers in Biology and Medicine, 32(1), 37–47.
Lippé, S., Natasa, K., & Anthony, R. M. (2009). Differential maturation of brain signal complexity in the human auditory and visual system. Frontiers in human neuroscience, 3.
Metzger, W. (1930). Optische Untersuchungen am Ganzfeld. Psychological Research, 13, 6–29, doi: 10.1007/BF00406757.
Mukhopadhyay, A. K., Dalui, S. K., Raychaudhury, M., Sengupta, R., Dey, N., Banerjee, B. M., Nag, D., Bhar, R., Ghosh, D., & Datta, A. K. (1998). Characterisation of materials for Indian Tanpura, Journal of Acoustical Society of India, XXVI(324), 372.
Peng, C. K., Havlin, S., Stanley, H. E., & Goldberger, A. L. (1995). Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos: An Interdisciplinary Journal of Nonlinear Science, 5(1), 82–87.
Pütz, P., Braeunig, M., & Wackermann, J. (2006). EEG correlates of multimodal ganzfeld induced hallucinatory imagery. International Journal of Psychophysiology, 61(2), 167–178.
Raman, C. V. (1921). On some Indian stringed instruments. Indian journal of physics and proceedings of the Indian Association for the Cultivation of Science 7, 29–33.
Sakharov, D. S., Davydov, V. I., & Pavlygina, R. A. (2005). Intercentral relations of the human EEG during listening to music. Human Physiology, 31(4), 392–397.
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.
Sengupta, R., Banerjee, B. M., Sengupta, S., & Nag, D. (1983). Tonal quality of the Indian Tanpura. In Proceedings of the Stockholm Music Acoustics Conference (SMAC). (333), Sweden: Royal Institute of Technology.
Sengupta, R., & Dey, N. (1988). Acoustic Signal Processing of a Tanpura String in Different Time Segments. Journal of Sangeet Research Academy, 9, 25.
Sengupta, R., Dey, N., Nag, D. & Banerjee, B. M. (1989). Study of amplitude fluctuation and multiple decay of a vibrating Tanpura string by FFT analysis. In Proceedings of National Conference on Electronics, Circuits and Systems. (2–4) Nov, India: Roorkee.
Sengupta, R., Dey, N., Banerjee, B. M., Nag, D., Datta, A. K. & Kichlu, V. K. (1995). A comparative study between the spectral structure of a composite string sound and the thick string of a Tanpura. Journal of Acoustical Society of India, XXIII.
Sengupta, R., Dey, N., Banerjee, B. M., Nag, D., Datta, A. K. & Kichlu, V. K. (1996). Some studies on spectral dynamics of Tanpura strings with relation to perception of Jwari. Journal of Acoustical Society of India, XXIV.
Sengupta, R., Dey, N., Nag, D., Datta, A. K. & Parui, S. K. (2002). Perceptual evaluation of Tanpura from the sound signals and its objective quantification using spectral features. Journal of Acoustical Society of India, 30.
Sengupta, R., Dey, N., Nag, D. & Datta, A. K. (2003). Acoustic cues for the timbral goodness of Tanpura. Journal of Acoustical Society of India, 31.
Sengupta, R., Dey, N., Nag, D., Datta, A. K. & Parui, S. K. (2004). Objective evaluation of Tanpura from the sound signals using spectral features, Journal of ITC Sangeet Research Academy, 18.
Sengupta, R., Dey, N., Datta, A. K., & Ghosh, D. (2005). Assessment of musical quality of Tanpura by fractal-dimensional analysis. Fractals, 13(03), 245–252.
Sjölander, K., & Beskow, J. (2009). Wavesurfer [Computer program] (Version 1.8. 5).
Sourina, O., Liu, Y., & Nguyen, M. K. (2012). Real-time EEG-based emotion recognition for music therapy. Journal on Multimodal User Interfaces, 5(1), 27–35.
Tsang, C. D., Trainor, L. J., Santesso, D. L., Tasker, S. L., & Schmidt, L. A. (2001). Frontal EEG responses as a function of affective musical features. Annals of the New York Academy of Sciences, 930(1), 439–442.
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Ghosh, D., Sengupta, R., Sanyal, S., Banerjee, A. (2018). Tanpura Drone and Brain Response. In: Musicality of Human Brain through Fractal Analytics. Signals and Communication Technology. Springer, Singapore. https://doi.org/10.1007/978-981-10-6511-8_5
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