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.
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