Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(1.34MB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
Estimate the Activation of EEG Bands from Different Brain Lobes with Classified Music Stimulation
Monira Islam, Md. Salah Uddin Yusuf, Chowdhury Azimul Haque
Pages - 101 - 113     |    Revised - 31-05-2019     |    Published - 30-06-2019
Volume - 13   Issue - 3    |    Publication Date - June 2019  Table of Contents
MORE INFORMATION
KEYWORDS
EEG, Spectral Analysis, ANOVA, Topography, Graphical User Interface.
ABSTRACT
Physiological research with human brain is getting more popular because it is the center of human nervous system. Music is a popular source of entertainment in modern era which affects differently in different brain lobes for having different frequency and pitch. The brain lobes are divided into frontal, central and parietal lobe. In this paper, an approach has been proposed to identify the activated brain lobes by using spectral analysis from EEG signal due to music evoked stimulation. In later phase, the impact of music on the EEG bands (alpha, beta, delta, theta) originating from different brain lobes is analyzed. Music has both positive and negative impact on human brain activity. According to linguistic variation, subject age and preference, volume level of songs, the impact on different EEG bands varies. In this work, music is categorized as mild, pop, rock song at different volume level (low, comfortable and high) based on Power Spectral Density (PSD) analysis. The average PSD value is 0.21 W/Hz, 0.32W/Hz and 0.84W/Hz for mild, pop and rock song respectively. The volume levels are considered as 0%-15% volume level for low volume, 16%- 55% volume level for comfortable volume and 56%-100% volume level for high volume. At comfortable volume level the central lobe of the brain is more activated for mild song and parietal lobe is activated for both pop and rock songs based on logarithmic power and PSD analysis. A statistical test two- way ANOVA has been conducted to indicate the variation in EEG band. For two-way ANOVA analysis, the P-value was taken as 0.05. A topographical representation has been performed for effective brain mapping to show the effects of music on the EEG bands for mild, pop and rock songs at the mentioned volume level. The maximum percentage of alpha band activation is 60% in comfortable volume which decreases with high volume and it indicates that, when the music stimuli moved towards the high-volume level, human cognition state moves from relax to stress condition due to the activeness of beta band. A Graphical User Interface (GUI) has been designed in MATLAB platform for the entire work.
1 Google Scholar 
2 refSeek 
3 ResearchGate 
4 Doc Player 
5 Scribd 
6 SlideShare 
1 Suto, J., & Oniga, S. (2018). Music Stimuli Recognition in Electroencephalogram Signal. Elektronika ir Elektrotechnika, 24(4), 68-71.
2 Schellenberg, E. G. (2012). Cognitive performance after listening to music: A review of the Mozart effect. Music, health, and wellbeing, 324-338.
3 Staeren, N., Renvall, H., De Martino, F., Goebel, R., & Formisano, E. (2009). Sound categories are represented as distributed patterns in the human auditory cortex. Current Biology, 19(6), 498-502.
4 Rajmohan, V., & Mohandas, E. (2007). The limbic system. Indian journal of psychiatry, 49(2), 132.
5 Froneman, T. (2019). The Assessment of Concussion Recovery Using Electroencephalography.
6 iMotions Biometric Research Platform (2016). EEG Pocket Guide.
7 Kumar, J. S., & Bhuvaneswari, P. (2012). Analysis of Electroencephalography (EEG) signals and its categorization-a study. Procedia engineering, 38, 2525-2536.
8 Hasan, M. K., Al Mahmud, N., Hossain, M. S., & Ahmad, M. (2015, December). Alpha band dependency of EEG signal on different stimulation of brain for human computer interaction. In 2015 2nd International Conference on Electrical Information and Communication Technologies (EICT) (pp. 148-151). IEEE.
9 Nawrocka, A., & Holewa, K. (2014). The Analysis of the Different Frequencies Sound Waves Effect on the EEG Signal. In Solid State Phenomena (Vol. 208, pp. 177-182). Trans Tech Publications.
10 Soeta, Y., & Nakagawa, S. (2012). Auditory evoked responses in human auditory cortex to the variation of sound intensity in an ongoing tone. Hearing research, 287(1-2), 67-75.
11 Mercadié, L., Caballe, J., Aucouturier, J. J., & Bigand, E. (2014). Effect of synchronized or desynchronized music listening during osteopathic treatment: An EEG study. Psychophysiology, 51(1), 52-59.
12 Lin, W. C., Chiu, H. W., & Hsu, C. Y. (2006, January). Discovering EEG signals response to musical signal stimuli by time-frequency analysis and independent component analysis. In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference (pp. 2765-2768). IEEE.
13 Kumagai, Y., Arvaneh, M., & Tanaka, T. (2017). Familiarity affects entrainment of EEG in music listening. Frontiers in human neuroscience, 11, 384.
14 Dey, A., Palit, S. K., Bhattacharya, D. K., & Tibarewala, D. N. (2014, April). Study of the effect of different music stimuli on autonomic nervous system of a single subject. In 2014 International Conference on Communication and Signal Processing (pp. 1322-1326). IEEE.
15 Nawaz, R., Nisar, H., & Voon, Y. V. (2018). The Effect of Music on Human Brain; Frequency Domain and Time Series Analysis Using Electroencephalogram. IEEE Access, 6, 45191-45205.
16 Schaefer, R. S., Vlek, R. J., & Desain, P. (2011). Music perception and imagery in EEG: Alpha band effects of task and stimulus. International Journal of Psychophysiology, 82(3), 254-259.
17 Markovic, A., Kühnis, J., & Jäncke, L. (2017). Task Context Influences Brain Activation during Music Listening. Frontiers in Human Neuroscience, 11.
18 Straticiuc, V., Nicolae, I. E., Strungaru, R., Vasile, T. M., Bajenaru, O. A., & Ungureanu, G. M. (2016, June). A preliminary study on the effects of music on human brainwaves. In 2016 8th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) (pp. 1-4). IEEE.
19 Haque, C. A., Islam, M., Saad, A. M., & Yusuf, M. S. U. (2019, February). An Approach to Estimate the Activation of Different Bands of EEG Signal using Classified Songs. In 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) (pp. 1-6). IEEE.
20 Islam, M., Ahmed, T., Yusuf, M. S. U., & Ahmad, M. (2015). Cognitive state estimation by effective feature extraction and proper channel selection of EEG signal. Journal of Circuits, Systems and Computers, 24(02), 1540005.
21 Advanced Brain Monitoring, Inc. (2014). B-Alert User Manual.
22 WMA declaration of Helsinki: Ethical principles for medical research involving human subjects 2013. (2014). Guildford, Surrey: Canary Publications.
23 Xu, W., Li, A., Shi, B., & Zhao, J. (2018). A Novel Design of Sparse FIR Multiple Notch Filters with Tunable Notch Frequencies. Mathematical Problems in Engineering, 2018.
24 Al-Fahoum, A. S., & Al-Fraihat, A. A. (2014). Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains. ISRN neuroscience, 2014.
Miss Monira Islam
Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology (KUET) Khulna-9203 - Bangladesh
Professor Md. Salah Uddin Yusuf
Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology (KUET) Khulna-9203 - Bangladesh
ymdsalahu2@gmail.com
Mr. Chowdhury Azimul Haque
Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology (KUET) Khulna-9203 - Bangladesh