A SSVEP Based EEG Signal Analysis to Discriminate the Effects of Music Levels on Executional Attention
American Journal of Bioscience and Bioengineering
Volume 3, Issue 3-1, June 2015, Pages: 27-33
Received: Mar. 19, 2015;
Accepted: Apr. 17, 2015;
Published: Jun. 1, 2015
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Md. Kamrul Hasan, Dept. of Electrical and Electronic Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh
Md. Shazzad Hossain, Dept. of Electrical and Electronic Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh
Tarun Kanti Ghosh, Dept. of Biomedical Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh
Mohiuddin Ahmad, Dept. of Electrical and Electronic Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh
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.
Md. Kamrul Hasan,
Md. Shazzad Hossain,
Tarun Kanti Ghosh,
A SSVEP Based EEG Signal Analysis to Discriminate the Effects of Music Levels on Executional Attention, American Journal of Bioscience and Bioengineering. Special Issue: Bio-Electronics: Biosensors, Biomedical Signal Processing, and Organic Engineering.
Vol. 3, No. 3-1,
2015, pp. 27-33.
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.
C. D. Frith ,K. J. Friston, “ Studying brain function with neuroimaging. In: Cognitive Neuroscience (Rugg MD, ed), pp169-192. Hove, England: Psychology Press, 2013.
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.
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.
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.
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.
F. R. Dillman-Carpentier, R.F. Potter, “Effects of music on physiological arousal: Explorations into tempo and genre”, Media Psychol 10:339-63.
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.
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.
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.
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.
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.
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.
 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.
 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