Decomposition Level Comparison of Stationary Wavelet Transform Filter for Visual Task Electroencephalogram

Authors

  • Syarifah Noor Syakiylla Sayed Daud Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Rubita Sudirman Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia

DOI:

https://doi.org/10.11113/jt.v74.4661

Keywords:

EEG, stationary wavelet transform filter, mean square error, decomposition level

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.

References

J. Henderson. 1999. Memory and Forgetting. Abingdon, Oxon: Routledge.

X. Zhang, L. Chuchu, Z. Jing, and M. Xiyu. 2009. A Study of Different Background Language Songs on Memory Task Performance, International Symposium on Intelligent Ubiquitous Computing and Education. 291–294.

K. S. Park, H. Choi, K. J. Lee, J. Y. Lee, K. O. An, and E. J. Kim. 2011. Patterns of Electroencephalography (EEG) Change Against Stress Through Noise and Memorization Test. International Journal of Medicine and Medical Sciences. 3: 381–389.

W. Klimesch. 1999. EEG Alpha and Theta Oscillations Reflect Cognitive and Memory Performance: A Review and Analysis. Brain Research Reviews. 29: 169–195.

S. Z. Mohd-Tumari, R. Sudirman, and A. Ahmad. 2012. Identification of Working Memory Impairments in Normal Children Using Wavelet Approach. IEEE Symposium on Industrial Electronics and Applications (ISIEA). 326–330.

B. Schack, N. Vath, H. Petsche, H.-G. Geissler, and E. Moller. 2002. Phase-coupling of Theta–gamma EEG Rhythms during Short-Term Memory Processing. International Journal of Psychophysiology. 44: 143–163.

J. Onton, A. Delorme, and S. Makeig. 2005. Frontal Midline EEG Dynamics during Working Memory. Neuroimage. 27: 341–356.

J. J. Carr and J. M. Brown. 2001. Introduction to Biomedical Equipment Technology. New Jersey: Prentice Hall.

M. Teplan. 2002. Fundamentals of EEG Measurement. Measurement Science Review. 2: 1–11.

S. Sanei and J. A. Chambers. 2013. EEG Signal Processing. Retrieved from http://onlinelibrary.wiley.com/.

J. R. Walker. 2007. Brain Task Map. Retrieved from http://www.soft-dynamics.com/.

H. T. Gorji, K. Abbas and J. Haddadnia. 2013. Ocular Artifact Detection and Removing from EEG by Wavelet Families: A comparative Study. Journal of Information Engineering and Applications. 13: 39–46.

M. E. Palendeng. 2011. Removing Noise from Electro-encephalography Signals for BIS based Depth of Anaesthesia Monitors. M. E. Thesis. University of Southern Queensland Toowoomba, Australia.

E. Data, 2007. Quantification of Epileptiform Electro-encephalographic Activity during Sevoflurane Mask Induction. Anesthesiology. 107: 38–92.

W. G. Morsi and M. El-Hawary, 2008. A New Perspective for The IEEE Standard 1459-2000 via Stationary Wavelet Transform in The Presence of Nonstationary Power Quality Disturbance. IEEE Transactions on Power Delivery. 23: 2356–2365.

Z. Wang and A. C. Bovik, 2009. Mean Squared Error: Love It or Leave It? A New Look at Signal Fidelity Measures. IEEE Signal Processing Magazine. 26: 98–117.

A. Al Jumah, 2013. Denoising of an Image using Discrete Stationary Wavelet Transform and Various Thresholding Techniques. Scientific Research. 4(1): 33–41.

K. Yasoda and A. Shanmugam, 2014. Certain Analysis on EEG for the Detection of EOG Artifact using Symlet Wavelet, Journal of Theoretical and Applied Information Technology. 67: 54–58.

X. H. Wang, R. S. Istepanian, and Y. H. Song, 2003. Microarray Image Enhancement by Denoising using Stationary Wavelet Transform. IEEE Transactions on NanoBioscience. 2(4): 184–189.

Downloads

Published

2015-05-28

How to Cite

Decomposition Level Comparison of Stationary Wavelet Transform Filter for Visual Task Electroencephalogram. (2015). Jurnal Teknologi, 74(6). https://doi.org/10.11113/jt.v74.4661