ARTIFACT REMOVAL AND BRAIN RHYTHM DECOMPOSITION FOR EEG SIGNAL USING WAVELET APPROACH
DOI:
https://doi.org/10.11113/jt.v78.9460Keywords:
Artifact, brain signal, electroencephalography, feature extraction, filtering, wavelet approachAbstract
This recent study introduces and discusses briefly the use of wavelet approach in removing the artifacts and extraction of features for electroencephalography (EEG) signal. Many of new approaches have been discovered by the researcher for processing the EEG signal. Generally, the EEG signal processing can be divided into pre-processing and post-processing. The aim of processing is to remove the unwanted signal and to extract important features from the signal. However, the selections of non-suitable approach affect the actual result and wasting the time and energy. Wavelet is among the effective approach that can be used for processing the biomedical signal. The wavelet approach can be performed in MATLAB toolbox or by coding, that require a simple and basic command. In this paper, the application of wavelet approach for EEG signal processing is introduced. Moreover, this paper also discusses the effect of using db3 mother wavelet with 5th decomposition level of stationary wavelet transform and db4 mother wavelet with 7th decomposition level of discrete wavelet transform in removing the noise and decomposing of the brain rhythm. Besides, the simulation result are also provided for better configuration.
References
Lakshmi, M. R., Prasad, T. V. and Prakash, C. V. 2014. Survey on EEG Signal Processing Methods. International Journal of Advanced Research in Computer Science and Software Engineering. 4(1): 84-91.
Chambers, S. S. and Jonathan, A. 2007. EEG Signal Processing. 1st edition. West Sussex, England: Wiley. 1-20.
Brown, J. M. and Carr, J. J. 1998. Introduction to Biomedical Equipment Technology. 4th Edition. New Jersey, United State: Prentice Hall. 1-30.
Jonathan, R. W., Niels, B., William, J., Dennis, J., Hunter, P., Gerwin, S., Emanuel, D., Louis, A. Q., Charles, J. R., and Theresa, M. V. 2000. Brain–Computer Interface Technology: A Review of the First International Meeting, IEEE Transactions on Rehabilitation Engineering. 164-173.
Chengbin, S., Yimin, B., Jiatuo, X., Deqi, K., Hong, Z., Qi, W, Huiling, S., Wenxin, W., Minmin, J., Xu, W., and Ye, D. 2013. IEEE International Conference on Bioinformatics and Biomedicine. 31-37.
Barry, R. J., Clarke, A. R., and Johnstone, S. J. 2003. A Review of Electrophysiology in Attention-Deficit/Hyperactivity Disorder: I. Qualitative and Quantitative Electroencephalography. Clinical Neurophysiology. 114(2): 171-183.
Petten, C. V., Hackley, S., Heinze, H. and Clarke, V. 2005. An Introduction to Event-Related Potentials and their Neural Origins. The MIT Press. 1-37
Mohd Tumari, S. Z., Sudirman, R., and Ahmad, A. H. 2012. Identification of Working Memory Impairments in Normal Children using Wavelet Approach. IEEE Symposium on Industrial Electronics and Application (ISIEA). 326-330.
Teplan, M. 2002. Fundamentals of EEG Measurement. Measurement Science Review. 2(2): 1-11.
Siuly, Li, Y., and Wen, P. 2011. EEG Signal Classification Based on Simple Random Sampling Technique with Least Square Support Vector Machine. International Journal of Biomedical Engineering and Technology. 7(4): 390-409.
Adikarapatti, V. K. 2007. Optimal EEG Channels and Rhythm Selection for Task Classification. Doctoral dissertation. Wright State University, USA. 1-15.
Herbert, R., Johannes, M. and Gert, P. 2000. Optimal Spatial Filtering of Single Trial EEG during Imagined Hand Movement. IEEE Transactions on Rehabilitation Engineering. 8(4): 441-446.
He, P., Kahle, M., Wilson, G. and Russel, C. 2005. Removal of Ocular Srtifacts from EEG: A Comparison of Adaptive Filtering and Regression Method using Simulated Data. IEEE Conference on Engineering in Medicine and Biology. 5041-5044.
Palendeng, M. E. 2011. Removing Noise from Electroencephalogram Signals for BIS Based Depth of Anaesthesia Monitors. Master thesis. University of Southern Queensland Toowoomba, Australia. 13-90.
Ali, B., Mehrdad, Fatourechi, Rabab K. W. and Gary E. B. 2007. A Survey of Signal Processing Algorithms in Brain–Computer Interfaces Based on Electrical Brain Signals. Journal of Neural Engineering. 4: R32-R57.
Kaushik, G., Sinha, H. P., and Dewan, L. 2014. Biomedical Signals Analysis by DWT Signal Denoising with Neural Networks. Journal of Theoretical and Applied Information Technology. 3(1): 1-18.
Misiti, M., Misiti, Y., Oppenheim, G. and Poggi J. 2009. Wavelet Toolbox. The MathWorks, Inc: United State. 1-736.
Mallat, S. 2009. A Wavelet Tour of Signal Processing: The Sparse Way. 3rd edition. Elsevier: Burlington. 2-11.
Daud, S. S. and Sudirman, R. 2015. Effect of White Noise Stimulation and Visual Working Memory Task on Brain Signal. ARPN Journal of Engineering and Applied Sciences. 10(18): 8491-8499.
Suyi, L. and Jun, L. 2009. The Optimal De-Noising Algorithm for ECG using Stationary Wavelet Transform. IEEE Conference The Computer Science And Information Engineering. 469-473.
Morsi, W. and El-Hawary M. 2008. A New Perspective for The IEEE Standard 1459-2000 via Stationary Wavelet Transform in The Presence of Non-stationary Power Quality Disturbance. IEEE Transactions on Power Delivery. 2356-2365.
Daud, S. S., and Sudirman, R. 2015. Decomposition Level Comparison of Stationary Wavelet Transform Filter for Visual Task Electroencephalogram. Jurnal Teknologi. 74(6).
Alomari, M. H., Awada, E. A., Samaha, A., and Alkamha, K. 2014. Wavelet-Based Feature Extraction for the Analysis of EEG Signals Associated with Imagined Fists and Feet Movements. Computer and Information Science. 7(2): 17-27.
Subasi, A. 2007. EEG Signal Classification using Wavelet Feature Extraction and a Mixture of Expert Model. Expert Systems with Applications. 32(4): 1084-1093.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
