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Decomposition level comparison of stationary wavelet transform filter for visual task electroencephalogram

Sayed Daud, Syarifah Noor Syakiylla and Sudirman, Rubita (2015) Decomposition level comparison of stationary wavelet transform filter for visual task electroencephalogram. Jurnal Teknologi, 74 (6). pp. 7-13. ISSN 0127-9696

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Official URL: http://dx.doi.org/10.11113/jt.v74.4661

Abstract

There has been a lot of research on the study of the human brain. Many modalities such as medical resonance imaging (MRI), computerized tomography (CT), positron emission tomography (PET), electroencephalography (EEG) and etc. has been invented. However, between this modality the electroencephalography widely chosen by researchers due to it is low cost, non-invasive techniques, and safely use. One of the major problems, the signal is corrupted by artifacts, whether to come from the muscle movement (electromyography artifact), eye blink and movement (electrooculography artifact) and power line interference. Filtering technique is applied to the signal in order to remove these artifacts. Wavelet approach is one of the technique that can filter out the artifact. This paper aim to determine which decomposition level is suitable for filtering EEG signal at channel Fp1, Fz, F8, Pz, O1 and O2 use stationary wavelet transform filter at db3 mother wavelet. Eight different decomposition levels have been selected and analyze based on mean square error (MSE) parameter. The Neurofax 9200 was used to record the brain signal at selected channel. Result shows that the decomposition at level 5 is suitable for filtering process using this stationary wavelet transform approach without losing important information.

Item Type:Article
Uncontrolled Keywords:decomposition level, EEG, mean square error, stationary wavelet transform filter
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:58213
Deposited By: Haliza Zainal
Deposited On:04 Dec 2016 04:07
Last Modified:29 Jul 2021 03:28

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