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| A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wavelet and ICA
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Source |
International Journal of Biometrics and Bioinformatics (IJBB) |
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Table of Contents |
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Complete Issue PDF(5.66MB) |
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Volume: 5 Issue: 2 |
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Pages: 28-148 |
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Publication
Date: May / June 2011 |
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ISSN
(Online): 1985-2347 |
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Pages |
130 - 148 |
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Author(s) |
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Published
Date |
31-05-2011 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
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KEYWORDS: Independent Component Analysis, Wavelet Transform, Electroencephalogram (EEG), Unscented Kalman Filter, Cycle Spinning |
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| This Manuscript is indexed in the following databases/websites:- |
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| 1. Directory of Open Access Journals (DOAJ) |
| 2. iSEEK |
| 3. Docstoc |
| 4. refSeek |
| 5. Scribd |
| 6. Libsearch |
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| In this paper we present a new algorithm using a merger of Independent Component Analysis and Translation Invariant Wavelet Transform. The efficacy of this algorithm is evaluated by applying contaminated EEG signals. Its performance was compared to three fixed-point ICA algorithms (FastICA, EFICA and Pearson-ICA) using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Amari Performance Index. Experiments reveal that our new technique is the most accurate separation method. |
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| 1 |
M. Akin, “Comparison of Wavelet Transform and FFT Methods in the Analysis of EEG Signals”, Journal of Medical Systems 26(3), 241-247, 2002.
|
|
|
| 2 |
M. Alfaouri and K. Daqrouq, “ECGSignal Denoising By Wavelet Transform Thresholding”, American Journal of Applied Sciences 5 (3), 276-281, 2008.
|
|
|
| 3 |
M.I. Bhatti, A. Pervaiz and M.H. Baig, “EEG Signal Decomposition and Improved Spectral Analysis Using Wavelet Transform”, In Proceedings of the 23rd Engineering in Medicine and Biology Society 2, 2001, 1862-1864.
|
|
|
| 4 |
Z. Chen, “Bayesian Filtering: From Kalman Filters to Particle Filters, and Beyond”, Adaptive Systems Lab., McMaster University., Hamilton, Ontario, Canada, 2003, Retrieved June 20, 2009, from: http://users.isr.ist.utl.pt/~jpg/tfc0607/chen_bayesian.pdf.
|
|
|
| 5 |
J. Chien; H. Hsin-Lung and S. Furui, “A new mutual information measure for independent component analysis”, In the Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2008 (ICASSP 2008), 2008, 1817 – 1820
|
|
|
| 6 |
S. Choi, A. Cichocki, L. Zhang, and S. Amari, “Approximate Maximum Likelihood Source Separation Using the Natural Gradient”,In the Proceedings of theIEICE Transaction Fundamental E84A(12), 2002.
|
|
|
| 7 |
R.R. Coifman, and D.L. Donoho, “Translation Invariant Denoising”,Lecture Notes in Statistics: Wavelets and Statistics, 125-150, 1995.
|
|
|
| 8 |
P. Comon, “Independent Component Analysis, a new concept?”, Signal Processing, Elsevier, 36(3), 287-314, 1994.
|
|
|
| 9 |
D.L. Donoho and I. Johnstone, “Adapting to unknown smoothness via wavelet shrinkage”, Journal of American Statistical Association, 90,1200—1224, 1995.
|
|
|
| 10 |
U.E. Emuir, A. Akin, A. Ertuzun, B. Sankur and K. Harmanci, “Wavelet Denoising vs ICA Denoising for Functional Optical Imaging”, In the Proceedings of the 1st International IEEE EMBS Conference on Neural Engineering, 2003, 384-387.
|
|
|
| 11 |
B. Ferguson, D. Abbott, “Denoising Techniques for Terahertz Response of Biological Samples”, Microelectronics Journal 32, 943-953, 2001.
|
|
|
| 12 |
N. Gadhok, and W. Kinsner, “Robust ICA for Cognitive Informatics”. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 2(4),86-92, 2008.
|
|
|
| 13 |
S. P. Ghael, A. M. Sayeed and R. G. Baraniuk, “Improved Wavelet Denoising via Empirical Wiener Filtering”, In the Proceedings of theSPIEvol. 3169, 389-399, 1997.
|
|
|
| 14 |
A. Graps, An Introduction to Wavelets. IEEE Journal of Computational Science and Engineering 2(2),1-17, 1995.
|
|
|
| 15 |
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.
|
|
|
| 16 |
C.S. Herrmann, M. Grigutsch and N.A. Busch, (2005). EEG oscillations and wavelet analysis. Event-related potentials: A methods handbook,MIT Press, 229-259, 2005.
|
|
|
| 17 |
G.G. Herrero, and K. Egiazarian, “Independent Component Analysis by a Resampling Strategy”, Technical Report 2005, Retrieved September 18, 2009, fromhttp://www.cs.tut.fi/~gomezher/projects/bss/rica/rica.pdf
|
|
|
| 18 |
S. Hoffman, and M. Falkenstien , “The Correction of Eye Blink Artefacts in the EEG: A Comparison of a Two Prominent Methods”, PLoS One 3(8):e3004, 2008
|
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|
| 19 |
A. Hyvärinen and E. Oja, “A Fast Fixed-Point Algorithm for Independent Component Analysis” , Neural Computation,9(7), 1483-1492, 1997
|
|
|
| 20 |
A. Hyvarinen, J. Karhunen and E. Oja , Independent Component Analysis, eds. Wiley & Sons, 2001
|
|
|
| 21 |
G. Inuso, F. La Foresta, N. Mammone, and F.C. Morabito, “Wavelet-ICA methodology for efficient artifact removal from Electroencephalographic recordings”, In the Proceedings of the International Conference on Neural Networks,1524-1529, 2007.
|
|
|
| 22 |
N. Jacob, and A. Martin, “Image Denoising in the Wavelet Domain Using Wiener Filtering”, Unpublished course project, University of Wisconsin, Madison, Wisconsin, USA, 2004.
|
|
|
| 23 |
S. Julier, and J.K. Uhlmann, “Unscented Filtering and Nonlinear Estimation”,Proceedings of the. IEEE 92(3), 401-421, 2004.
|
|
|
| 24 |
S. Julier, and J.K. Uhlmann, “A New Extension of the Kalman Filter to Nonlinear Systems” In the Proceedingd of AeroSense: 11th Int. Symp. Aerospace/Defense Sensing, Simulation and Controls,182-193, 1997.
|
|
|
| 25 |
A. Kallapur, S. Anavatti, and M. Garratt, “Extended and Unscented Kalman Filters for Attitude Estimation of an Unmanned Aerial Vehicle,In the Proceedings of the 27th IASTED Int. Conf. Modelling, Identification, and Control (MIC 2008) , 2008
|
|
|
| 26 |
J. Karvanen, J.,Eriksson, and K.V. Pearson, “System Based Method for Blind Separation", In the Proceedings of Second International Workshop on Independent Component Analysis and Blind Signal Separation, Helsinki 2000, 585—590, 2000.
|
|
|
| 27 |
L. Kaur, S. Gupta, and R.C. Chauhan, “Image Denoising using Wavelet Thresholding”, In the Proceedings of the 3rd Indian Conf. Computer Vision, Graphics & Image Processing (ICVGIP 2002), 22(14), 2002.
|
|
|
| 28 |
Z. Koldovský and P. Tichavský, P., "Time-Domain Blind Audio Source Separation Using Advanced ICA Methods", In the Proceedings of the 8th Annual Conference of the International Speech Communication Association (Interspeech 2007), pp. 846-849, 2007.
|
|
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| 29 |
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), 322-333, 2006.
|
|
|
| 30 |
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), 45-57, 2006.
|
|
|
| 31 |
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, 1996.
|
|
|
| 32 |
M. Mastriani, and A.E. Giraldez, “Kalman’s Shrinkage for Wavelet-Based Despeckling of SAR Images”, International Journal of Intelligent Systems and Technologies 1(3),190-196, 2006.
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| Janett Walters-Williams : Colleagues
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| Yan Li : Colleagues
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