Electroencephalogram patterns with the presence of an unknown musical stimulus





Electroencephalography, Musical Stimulus, Time-frequency Analysis.


The cerebral activity presents different behaviors in different situations and levels of consciousness, especially under musical stimulation. Signals of the central nervous system may disclose bioelectrical patterns, since listening to rhythmic sequences activates specific brain areas. In this paper, we analyze 42 neurologically normal Brazilian individuals, submitted to musical stimulation based on a procedure consisting of three different steps, during which the volunteer is kept with closed eyes. The first step is associated with the preliminary control silence period, without any stimulus, as the volunteer remains at rest. The second step consisted of unknown music stimulation. Finally, the third step involves post-music rest. Quantitative signal analysis computes the power spectrum time variations. Results point out stronger changes in gamma and high gamma waves (30 – 100 Hz). Even though the clinical rhythms (0 – 30 Hz) change throughout the whole period of the experiment, quantitative differences at gamma and high gamma bands are remarkably greater.  Particularly, when comparing the initial silent period and the final post-stimulation silent one, bioelectrical differences are only highlighted by gamma and high gamma rhythms. In consequence, this paper points out that the EEG analysis of cognitive issues related to musical perception cannot disregard gamma and high gamma waves.


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BAJOULVAND, A., et al. Analysis of folk music preference of people from different ethnic groups using kernel-based methods on EEG signals. Applied Mathematics and Computation. 2017, 307, 62-70. https://doi.org/10.1016/j.amc.2017.02.042

BALASUBRAMANIAN, G., et al. Music induced emotion using wavelet packet decomposition—An EEG study. Biomedical Signal Processing and Control. 2018, 42, 115-128. https://doi.org/10.1016/j.bspc.2018.01.015

BANERJEE, A., et al. Study on Brain Dynamics by Non Linear Analysis of Music Induced EEG Signals. Physica A: Statistical Mechanics and its Applications. 2016, 444, 110-120. https://doi.org/10.1016/j.physa.2015.10.030

BAUER, A.K.R., KREUTZ, G. and HERRMANN, C.S. 2015. Individual musical tempo preference correlates with EEG beta rhythm. Psychophysiology. 2015, 52(4), 600-604. https://doi.org/10.1111/psyp.12375

CALABRÒ, R.S., et al. Neural correlates of consciousness: what we know and what we have to learn! Neurological Sciences. 2015, 36(4), 505-513. https://doi.org/10.1007/s10072-015-2072-x

CLARK, C.N., DOWNEY, L.E. and WARREN, J.D. Music biology: All this useful beauty. Current Biology. 2014, 24(6), 234-237. https://doi.org/10.1016/j.cub.2014.02.013

CONG, F., et al. Linking brain responses to naturalistic music through analysis of ongoing EEG and stimulus features. IEEE Transactions on Multimedia. 2013, 212(1), 1060 -1069. https://doi.org/10.1109/TMM.2013.2253452

ENGEL, J. and DA SILVA, F.L. High-frequency oscillations – Where we are and where we need to go. Progress in Neurobiology. 2012, 98 (3), 316-318. https://doi.org/10.1007/978-1-4614-4984-3

FREEMAN, W. and QUIROGA, R.Q. Imaging Brain Function With EEG: Advanced Temporal and Spatial Analysis of Electroencephalographic Signals. New York: Springer, 2013. Available from: https://doi.org/10.1007/978-1-4614-4984-3

GOMES, P., PEREIRA, T. and CONDE, J. Musical emotions in the brain-a neurophysiological study. Neurophysiology Research. 2018, 1(1), 12–20.

HERRMANN, C.S., et al. EEG oscillations: From correlation to causality. International Journal of Psychophysiology. 2016, 103, 12-21. https://doi.org/10.1016/j.ijpsycho.2015.02.003

KAY, B.P., et al. Moderating effects of music on resting state networks. Brain Research. 2012, 1447, 53-64. https://doi.org/10.1016/j.brainres.2012.01.064

KHOSROWABADI, R. and RAHMAN, A.W.B.A., 2010. Classification of EEG correlates on emotion using features from Gaussian mixtures of EEG spectrogram. In: Proceeding of the 3rd International Conference on Information and Communication Technology for the Moslem World (ICT4M). Singapore: IEEE, pp. 102-107. Available from: https://ieeexplore.ieee.org/document/5971942

KUCEWICZ, M.T., et al. Dissecting gamma frequency activity during human memory processing. Brain. 140(5), 1337-1350, 2017. https://doi.org/10.1093/brain/awx043

KUMAGAI, Y., ARVANEH, M. and TANAKA, T. Familiarity Affects Entrainment of EEG in Music Listening. Frontiers in Human Neuroscience. 2017, 11, 1-8. https://doi.org/10.3389/fnhum.2017.00384

LIN, Y.P., et al. EEG-based emotion recognition in music listening. IEEE Transactions on Biomedical Engineering. 2010, 57(7), 1798-1806. Available from: https://ieeexplore.ieee.org/document/5458075

MAITY, A.K., et al. Multifractal Detrended Fluctuation Analysis of alpha and theta EEG rhythms with musical stimuli. Chaos, Solitons and Fractals. 2015, 81, 52-67. https://doi.org/10.1016/j.chaos.2015.08.016

MARTÍNEZ-RODRIGO, A., et al. Neural Correlates of Phrase Rhythm: An EEG Study of Bipartite vs. Rondo Sonata Form. Frontiers in Neuroinformatics. 2017, 11(9), 1-9. https://doi.org/10.3389/fninf.2017.00029

NAKAMURA, S., et al. Analysis of music-brain interaction with simultaneous measurement of regional cerebral blood flow and electroencephalogram beta rhythm in human subjects. Neuroscience Letters. 1999, 275(3), 222-226. https://doi.org/10.1016/S0304-3940(99)00766-1

PAN, Y., et al., 2013. Common frequency pattern for music preference identification using frontal EEG. In: 6th International IEEE/EMBS Conference on Neural Engineering. San Diego: IEEE, pp. 505–508. Available from: https://ieeexplore.ieee.org/document/6695982

SCHAEFER, R.S., DESAIN, P. and FARQUHAR, J. Shared processing of perception and imagery of music in decomposed EEG. NeuroImage. 2013, 70, 317-326. https://doi.org/10.1016/j.chaos.2015.08.016

SCHOMER, D.L. and SILVA, F.H.L. Niedermeyer’s Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. New York: Oxford University Press, 2011.

TANDLE, A., et al., 2016. Study of valence of musical emotions and its laterality evoked by instrumental Indian classical music: An EEG study. In: International Conference on Communication and Signal Processing, ICCSP. India: IEEE, pp. 327–331. Available from: https://ieeexplore.ieee.org/document/7754149

TELENCZUK, B., et al. High-frequency EEG covaries with spike burst patterns detected in cortical neurons. Journal of Neurophysiology. 2011, 105(6), 2951-2959. https://doi.org/10.1152/jn.00327.2010

THAUT, M.H. and HOEMBERG, V. Handbook of Neurologic Music Therapy. 1st ed. New York: Oxford University Press, 2016.

VIJAYALAKSHMI, K., SRIDHAR, S. and KHANWANI, P. 2010. Estimation of effects of Alpha music on EEG components by time and frequency domain analysis. In: International Conference on Computer and Communication Engineering, ICCCE, Kuala Lumpur: IEEE, pp. 11–13. Available from: https://ieeexplore.ieee.org/document/5556761?part=1

WANG, D., et al., 2016. Exploiting ongoing EEG with multilinear partial least squares during free-listening to music. In: IEEE 26th International Workshop on Machine Learning for Signal Processing, Salerno: IEEE, pp. 1–6. Available from: https://ieeexplore.ieee.org/document/7738849




How to Cite

COSTA, A.C.P.R. da ., DAVI RAMOS, C., FERREIRA-JORGE, A.R.., CAMPOS, M., DESTRO-FILHO, J.-B.. and FROSI ROSA, P., 2021. Electroencephalogram patterns with the presence of an unknown musical stimulus. Bioscience Journal [online], vol. 37, pp. e37066. [Accessed22 July 2024]. DOI 10.14393/BJ-v37n0a2021-49455. Available from: https://seer.ufu.br/index.php/biosciencejournal/article/view/49455.



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