Home   >   CSC-OpenAccess Library   >    Manuscript Information
Performance Comparison of Known ICA Algorithms to a Wavelet-ICA Merger
Janett Walters-Williams, Yan Li
Pages - 80 - 92     |    Revised - 01-07-2011     |    Published - 05-08-2011
Volume - 5   Issue - 3    |    Publication Date - July / August 2011  Table of Contents
Independent Component Analysis, Wavelet Transform, Unscented Kalman Filter, Electroencephalogram
Electroencephalogram (EEG) is useful for biological research and clinical diagnosis. These signals are however contaminated with artifacts which must be removed to have pure EEG signals. These artifacts can be removed by using Independent Component Analysis (ICA). In this paper we studied the performance of three ICA algorithms (FastICA, JADE, and Radical) as well as our newly developed ICA technique which utilizes wavelet transform. Comparing these ICA algorithms, it is observed that our new technique performs as well as these algorithms at denoising EEG signals.
CITED BY (12)  
1 Patil, M. V., & Srinivas, M. C. Fully Automated Artifact Removal for BCI.
2 Mannapperuma, K., Holton, B. D., Lesniewski, P. J., & Thomas, J. C. (2015). Performance limits of ICA-based heart rate identification techniques in imaging photoplethysmography. Physiological measurement, 36(1), 67.
3 Al-Qaisi, A. (2015). Blind Source Separation of Mixed Noisy Audio Signals Using an Improved FastICA. Journal of Applied Sciences, 15(9), 1158.
4 Al-Qazzaz, N. K., Hamid Bin Mohd Ali, S., Ahmad, S. A., Islam, M. S., & Escudero, J. (2015). Selection of mother wavelet functions for multi-channel EEG signal analysis during a working memory task. Sensors, 15(11), 29015-29035.
5 Singh, N., Godiyal, A. K., Panigrahi, B. K., Anand, S., & Santhosh, J. (2015, September). Source localization in alcoholic and control subjects to estimate cognitive load using EEG signal. In Computer, Communication and Control (IC4), 2015 International Conference on (pp. 1-5). IEEE.
6 Al-Qazzaz, N. K., Ali, S. H. B., Ahmad, S. A., Chellappan, K., Islam, M. S., & Escudero, J. (2014). Role of EEG as Biomarker in the Early Detection and Classification of Dementia. The Scientific World Journal, 2014.
7 Al-Kadi, M. I., Reaz, M. B. I., Ali, M. A. M., & Liu, C. Y. (2014). Reduction of the Dimensionality of the EEG Channels during Scoliosis Correction Surgeries Using a Wavelet Decomposition Technique. Sensors, 14(7), 13046-13069.
8 Rutledge, D. N., & Bouveresse, D. J. R. (2013). Independent components analysis with the JADE algorithm. TrAC Trends in Analytical Chemistry, 50, 22-32.
9 Holton, B. D., Mannapperuma, K., Lesniewski, P. J., & Thomas, J. C. (2013). Signal recovery in imaging photoplethysmography. Physiological measurement, 34(11), 1499.
10 Al-Kadi, M. I., Reaz, M. B. I., & Ali, M. A. M. (2013). Evolution of electroencephalogram signal analysis techniques during anesthesia. Sensors, 13(5), 6605-6635.
11 Sindhumol, S., Kumar, A., & Balakrishnan, K. (2013). Automated Brain Tissue Classification by Multisignal Wavelet Decomposition and Independent Component Analysis. ISRN Biomedical Imaging, 2013.
12 Sindhumol, S., Kumar, A., & Balakrishnan, K. (2013, April). Wavelet based independent component analysis for multispectral brain tissue classification. In Communications and Signal Processing (ICCSP), 2013 International Conference on (pp. 415-418). IEEE.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
A. Graps. “An Introduction to Wavelets”. IEEE Journal of Computational Science and Engineering 2(2), pp. 1-17, 1995.
A. Kallapur, S. Anavatti, and M Garratt. “Extended and Unscented Kalman Filters for Attitude Estimation of an Unmanned Aerial Vehicle”. In Proc 27th IASTED International Conference on Modelling, Identification, and Control (MIC 2008), 2008.
B. Ferguson and D. Abbott. “Denoising Techniques for Terahertz Response of Biological Samples”. Microelectronics Journal 32, pp. 943-953, 1991.
G. Inuso, F. La Foresta, N. Mammone, and F.C. Morabito. “Wavelet-ICA methodology for efficient artifact removal from Electroencephalographic recordings”. In Proc International Joint Conference on Neural Networks, 2007, pp. 1524-1529.
G. Welch and G. Bishop. “An Introduction to the Kalman Filter”, Technical Report TR 95-041, University of North Carolina, Chapel Hill, North Carolina, USA. Internet: http://www.cs.unc.edu/~welch/media/pdf/kalman_intro.pdf 2006, [Aug 29, 2009]
G. Zouridakis and D. Iyer. “Comparison between ICA and Wavelet-based Denoising of single-trial evoked potentials”. In Proc 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2004, pp. 87-90.
G.G. Herrero, and K. Egiazarian. “Independent Component Analysis by a Resampling Strategy”. Internet: http://www.cs.tut.fi/~gomezher/projects/bss/rica/rica.pdf, 2005 [Sep 18, 2009]
J. Zhao, Q. Xi, X. Wang, and Y. Yang. “Image Denoising via Wavelet Domain Wiener filter based on Coorelation Model”. In 16th World Conference on Nondestructive Testing (WCNDT 04), 2004.
Kaur, L., Gupta, S., & Chauhan, R.C., “Image Denoising using Wavelet Thresholding”. In Proc 3rd Indian Conference on Computer Vision, Graphics & Image Processing (ICVGIP 2002), 22(14), 2002.
M. Alfaouri and K. Daqrouq. “ECG Signal Denoising By Wavelet Transform Thresholding”. American Journal of Applied Sciences 5 (3), pp. 276-281, 2008.
N. Gadhok and W. Kinsner. “Robust ICA for Cognitive Informatics”. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 2(4), pp. 86-92, 2008.
N. Jacob, and A. Martin. “Image Denoising in the Wavelet Domain Using Wiener Filtering”. Unpublished course project, University of Wisconsin, Madison, Wisconsin, USA, 2004.
N. Nikolaev and A. Gotchev. “ECG Signal Denoising using Wavelet Domain Wiener Filtering”. In Proc 10th European Signal Processing Conference (EUSIPCO 2000), 2000.
P. Comon. “Independent Component Analysis, a new concept?” Signal Processing, Elsevier, 36(3), pp. 287-314, 1994.
P. Senthil Kumar, R. Arumuganathan, K. Sivakumar, and C. Vimal. “A Wavelet based Statistical Method for De-noising of Ocular Artifacts in EEG Signals”. International Journal of Computer Science and Network Security (IJCSNS). 8(9), pp. 87-92, 2008.
P. Senthil Kumar, R. Arumuganathan, K. Sivakumar, and C. Vimal. “Removal of Ocular Artifacts in the EEG through Wavelet Transform without using an EOG Reference Channel”. International Journal of Open Problems in Computer Science & Mathematics (IJOPCM) 1(3), pp. 189-198, 2008.
P. Shui and Y. Zhao. “Image Denoising Algorithm using Doubling Local Wiener Filtering with Block Adaptive Windows in Wavelet Domain”. Signal Processing 87(7), pp. 1721-1734, 2007.
R. Sameni and M.B. Jutten. “Filtering Electrocardiogram Signals using the Extended Kalman Filter”. In Proc 27th IEEE Engineering in Medicine and Biology (EMBS) Annual Conference 2005, pp. 5639-5642.
R.R. Coifman, and D.L. Donoho. “Translation Invariant Denoising”. Lecture Notes in Statistics: Wavelets and Statistics, pp. 125-150. 1995.
S. Choi, A. Cichocki, L. Zhang, and S. Amari. “Approximate Maximum Likelihood Source Separation Using the Natural Gradient”. In Proc. IEEE Workshop on Signal Processing Advances in Vireless Communications, (Taoyuan, Taiwan), 2001, pp. 235-238.
S. Julier and J.K. Uhlmann. “A New Extension of the Kalman Filter to Nonlinear Systems”. In Proceedings of AeroSense: In 11th International Symposium on Aerospace/Defense Sensing, Simulation and Controls, 1997, pp. 182-193.
S. Julier, and J.K. Uhlmann. “Unscented Filtering and Nonlinear Estimation”. In Proc IEEE 92(3), 2004, pp. 401-421.
S. Makeig, J. Anthony, A.J. Bell, T. Jung, and T.J. Sejnowski. “Independent Component Analysis of Electroencephalographic data”. Advances in Neural Information Processing Systems 8, MIT Press Cambridge MA 8, pp. 145-151, 1996.
V. Krishnaveni, S. Jayaraman, A. Gunasekaran and K. Ramadoss. “Automatic Removal of Ocular Artifacts using JADE Algorithm and Neural Network”. International Journal of Intelligent Systems and Technologies 1(4), pp. 322-333, 2006.
V. Krishnaveni, S. Jayaraman, S. Aravind, V. Hariharasudhan, and K. Ramadoss. “Automatic Identification and Removal of Ocular Artifacts from EEG using Wavelet Transform”. Measurement Science Review 6(2, 4), pp. 45-57, 2006
W. Zhou and J. Gotman. “Removal of EMG and ECG Artifacts from EEG Based on Wavelet Transform and ICA”. In Proc 26th Annual International Conference on the IEEE EMBS, 2004, pp. 392-395.
W. Zhou and J. Gotman. “Removing Eye-movement Artifacts from the EEG during the Intracarotid Amobarbital Procedure”. Epilepsia 46(3), pp. 409-411, 2005.
Y.M. Hawwar, A.M. Reza, and R.D. Turney. “Filtering(Denoising) in the Wavelet Transform Domain”. Unpublished, Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, 2002.
Z. Chen, “Bayesian Filtering: From Kalman Filters to Particle Filters, and Beyond”. Adaptive Systems Lab., McMaster University., Hamilton, Ontario, Canada. Internet: http://users.isr.ist.utl.pt/~jpg/tfc0607/chen_bayesian.pdf. 2003, [Jun 20, 2009]
Z. Weidong and L. Yingyuan.. 2001. “EEG Multi-resolution Analysis using Wavelet Transform”, In Proc 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE/EMBS) 2001.
Mr. Janett Walters-Williams
University of Technology, Jamaica - Jamaica
Mr. Yan Li
- Australia

View all special issues >>