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A SSVEP Based EEG Signal Analysis to Discriminate the Effects of Music Levels on Executional Attention

Received: 19 March 2015     Accepted: 17 April 2015     Published: 1 June 2015
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Abstract

In this work the electrical activity in brain or known as electroencephalogram (EEG) signal is being analyzed to study the various effects of sound on the human brain activity. The effect is in the form of variation in either frequency or in the power of different EEG bands. A biological EEG signal stimulated by Music listening reflects the state of mind, impacts the analytical brain and the subjective-artistic brain. A two channel EEG acquisition unit is being used to extract brain signal with high transfer rate as well as good SNR. This paper focused on three types of brain waves which are theta (4-7 Hz), alpha (8-12 Hz) and beta wave (13-30 Hz). The analysis is carried out using Power Spectral density (PSD), Correlation co-efficient analysis. The outcome of this research depicted that high amplitude Alpha and low amplitude Beta wave and low amplitude Alpha and high amplitude Beta wave is associated with melody and rock music respectively meanwhile theta has no effect. High power of alpha waves and low power of beta waves that obtained during low levels of sound (Melody) indicate that subjects were in relaxed state. When subjects exposed to high level of sound (Rock), beta waves power increased indicating subjects in disturbed state. Meanwhile, the decrease of alpha wave magnitude showed that subjects in tense. Thus the subject’s executional attention level is determined by analyzing the different components of EEG signal.

Published in American Journal of Bioscience and Bioengineering (Volume 3, Issue 3-1)

This article belongs to the Special Issue Bio-Electronics: Biosensors, Biomedical Signal Processing, and Organic Engineering

DOI 10.11648/j.bio.s.2015030301.15
Page(s) 27-33
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Electroencephalogram (EEG), Steady-State Visual Evoked Potential (SSVEP), Non-Invasive Signal Recording, Power Spectral Density (PSD), Correlation Coefficient, Brain Wave, Eeg Bands

References
[1] M. G. H Coles., M. D. Rugg, “Event-related brain potentials: An introduction.”, New York: Oxford University PressJ. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73, 1995.
[2] C. D. Frith ,K. J. Friston, “ Studying brain function with neuroimaging. In: Cognitive Neuroscience (Rugg MD, ed), pp169-192. Hove, England: Psychology Press, 2013.
[3] M. K. Hasan, R. Z. Rusho, and M. Ahmad “A Direct Noninvasive Brain Interface with Computer Based On Steady-State Visual-Evoked Potential (SSVEP) With High Transfer Rates” International Conference on Advances in Electrical Engineering (ICAEE), 2013, Dkaka, Bangladesh.
[4] R. Bhoria, S. Gupta , “A Study of the effect of sound on EEG”, International Journal of Electronics and Computer Science Engineering (IJECSE), Volume 2, Number 1, ISSN- 2277-1956.
[5] M. K. Hasan, R. Z. Rusho, T. M. Hossain, T. K. Ghosh, and M. Ahmad, “Design and Simulation of Cost Effective Wireless EEG Acquisition System for Patient Monitoring”, International Conference on Informatics, Electronics and Vission (ICIEV), 2014, Dhaka, Bangladesh.
[6] H. Hassan , Z. H. Murat, V. Ross and N. Buniyamin, “A Preliminary Study on the Effects of Music on Human Brainwaves ”, International Conference on Control, Automation and Information Sciences (ICCAIS), 2012.
[7] F. R. Dillman-Carpentier, R.F. Potter, “Effects of music on physiological arousal: Explorations into tempo and genre”, Media Psychol 10:339-63.
[8] N. Hurless, A. Mekic, S. Peña, E. Humphries, H. Gentry, D. F. Nichols, “Music genre preference and tempo alter alpha and beta waves in human non-musicians”, The Premier Undergraduate Neuroscience Journal, 2013.
[9] R. S. S. A. Kadir, M. H. Ghazali, Z. H. Murat, M. N. Taib, H. A. Rahman, S. A. M. Aris, “The priliminary Study on the ERffect of Nasyid Music and Rock Music on Brainwave Signal Using EEG”, 2nd Internatonal Congress on Engineering Education, december 8-9, 2010, Kuala Lumpur, Malaysia.
[10] N. G. Karthick, V. I. T. Ahamed, P. K. Joseph, “Music and the EEG: A Study using Nonlinear Methods”, International Conference on Biomedical and Pharmaceutical Engineering (ICBPE), 2006, December 11-14, Singapore.
[11] R. Bhoria, P. Singal, D. Verma, “Analysis of Effect of Sound Levels on EEG”, International Journal of Advanced Technology & Engineering Research (IJATER), March 2012, Volume 2, ISSUE 2, ISSN NO: 2250-3536.
[12] P. D. Welch, “The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodogram”, IEEE Trans. Audio & Electroacoust.15, 70–73.
[13] E. Malar, M. Gauthaam, D. Chakravarthy, “A Novel Approach for the Detection of Drunken Driving using the Power Spectral Density Analysis of EEG”, International Journal of Computer Applications (0975 – 8887), Volume 21, No.7, May 2011.
[14] [14] J. F. D. Saa M. S. Gutierrez, “EEG Signal Classification Using Power Spectral Features and linearDiscriminant Analysis: A Brain Computer Interface Application”, Eighth LACCEI Latin American and Caribbean Conference for Engineering and Technology (LACCEI-2010), “Innovation and Development for the Americas”, June 1-4, 2010, Arequipa, Perú.
[15] [15] B. H. Jansen, J. R. Bourne, J. W. Ward, “Autoregressive Estimation of Short Segment Spectra for Computerized EEG Analysis”, Department of Electrical and Biomedical Engineering, School of Engineering, School of Medicine, Vanderbilt University
Cite This Article
  • APA Style

    Md. Kamrul Hasan, Md. Shazzad Hossain, Tarun Kanti Ghosh, Mohiuddin Ahmad. (2015). A SSVEP Based EEG Signal Analysis to Discriminate the Effects of Music Levels on Executional Attention. American Journal of Bioscience and Bioengineering, 3(3-1), 27-33. https://doi.org/10.11648/j.bio.s.2015030301.15

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    ACS Style

    Md. Kamrul Hasan; Md. Shazzad Hossain; Tarun Kanti Ghosh; Mohiuddin Ahmad. A SSVEP Based EEG Signal Analysis to Discriminate the Effects of Music Levels on Executional Attention. Am. J. BioSci. Bioeng. 2015, 3(3-1), 27-33. doi: 10.11648/j.bio.s.2015030301.15

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    AMA Style

    Md. Kamrul Hasan, Md. Shazzad Hossain, Tarun Kanti Ghosh, Mohiuddin Ahmad. A SSVEP Based EEG Signal Analysis to Discriminate the Effects of Music Levels on Executional Attention. Am J BioSci Bioeng. 2015;3(3-1):27-33. doi: 10.11648/j.bio.s.2015030301.15

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  • @article{10.11648/j.bio.s.2015030301.15,
      author = {Md. Kamrul Hasan and Md. Shazzad Hossain and Tarun Kanti Ghosh and Mohiuddin Ahmad},
      title = {A SSVEP Based EEG Signal Analysis to Discriminate the Effects of Music Levels on Executional Attention},
      journal = {American Journal of Bioscience and Bioengineering},
      volume = {3},
      number = {3-1},
      pages = {27-33},
      doi = {10.11648/j.bio.s.2015030301.15},
      url = {https://doi.org/10.11648/j.bio.s.2015030301.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bio.s.2015030301.15},
      abstract = {In this work the electrical activity in brain or known as electroencephalogram (EEG) signal is being analyzed to study the various effects of sound on the human brain activity. The effect is in the form of variation in either frequency or in the power of different EEG bands. A biological EEG signal stimulated by Music listening reflects the state of mind, impacts the analytical brain and the subjective-artistic brain. A two channel EEG acquisition unit is being used to extract brain signal with high transfer rate as well as good SNR. This paper focused on three types of brain waves which are theta (4-7 Hz), alpha (8-12 Hz) and beta wave (13-30 Hz). The analysis is carried out using Power Spectral density (PSD), Correlation co-efficient analysis. The outcome of this research depicted that high amplitude Alpha and low amplitude Beta wave and low amplitude Alpha and high amplitude Beta wave is associated with melody and rock music respectively meanwhile theta has no effect. High power of alpha waves and low power of beta waves that obtained during low levels of sound (Melody) indicate that subjects were in relaxed state. When subjects exposed to high level of sound (Rock), beta waves power increased indicating subjects in disturbed state. Meanwhile, the decrease of alpha wave magnitude showed that subjects in tense. Thus the subject’s executional attention level is determined by analyzing the different components of EEG signal.},
     year = {2015}
    }
    

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  • TY  - JOUR
    T1  - A SSVEP Based EEG Signal Analysis to Discriminate the Effects of Music Levels on Executional Attention
    AU  - Md. Kamrul Hasan
    AU  - Md. Shazzad Hossain
    AU  - Tarun Kanti Ghosh
    AU  - Mohiuddin Ahmad
    Y1  - 2015/06/01
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    N1  - https://doi.org/10.11648/j.bio.s.2015030301.15
    DO  - 10.11648/j.bio.s.2015030301.15
    T2  - American Journal of Bioscience and Bioengineering
    JF  - American Journal of Bioscience and Bioengineering
    JO  - American Journal of Bioscience and Bioengineering
    SP  - 27
    EP  - 33
    PB  - Science Publishing Group
    SN  - 2328-5893
    UR  - https://doi.org/10.11648/j.bio.s.2015030301.15
    AB  - In this work the electrical activity in brain or known as electroencephalogram (EEG) signal is being analyzed to study the various effects of sound on the human brain activity. The effect is in the form of variation in either frequency or in the power of different EEG bands. A biological EEG signal stimulated by Music listening reflects the state of mind, impacts the analytical brain and the subjective-artistic brain. A two channel EEG acquisition unit is being used to extract brain signal with high transfer rate as well as good SNR. This paper focused on three types of brain waves which are theta (4-7 Hz), alpha (8-12 Hz) and beta wave (13-30 Hz). The analysis is carried out using Power Spectral density (PSD), Correlation co-efficient analysis. The outcome of this research depicted that high amplitude Alpha and low amplitude Beta wave and low amplitude Alpha and high amplitude Beta wave is associated with melody and rock music respectively meanwhile theta has no effect. High power of alpha waves and low power of beta waves that obtained during low levels of sound (Melody) indicate that subjects were in relaxed state. When subjects exposed to high level of sound (Rock), beta waves power increased indicating subjects in disturbed state. Meanwhile, the decrease of alpha wave magnitude showed that subjects in tense. Thus the subject’s executional attention level is determined by analyzing the different components of EEG signal.
    VL  - 3
    IS  - 3-1
    ER  - 

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Author Information
  • Dept. of Electrical and Electronic Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh

  • Dept. of Electrical and Electronic Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh

  • Dept. of Biomedical Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh

  • Dept. of Electrical and Electronic Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh

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