Research Article
Removal of Ocular Artifacts from EEG Signals by Fast RLS Algorithm using Wavelet Transform
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 21 - Issue 4 |
| Published: May 2011 |
| Authors: P.Ashok Babu, Dr.K.V.S.V.R. Prasad |
10.5120/2503-3384
|
P.Ashok Babu, Dr.K.V.S.V.R. Prasad . Removal of Ocular Artifacts from EEG Signals by Fast RLS Algorithm using Wavelet Transform. International Journal of Computer Applications. 21, 4 (May 2011), 1-5. DOI=10.5120/2503-3384
@article{ 10.5120/2503-3384,
author = { P.Ashok Babu,Dr.K.V.S.V.R. Prasad },
title = { Removal of Ocular Artifacts from EEG Signals by Fast RLS Algorithm using Wavelet Transform },
journal = { International Journal of Computer Applications },
year = { 2011 },
volume = { 21 },
number = { 4 },
pages = { 1-5 },
doi = { 10.5120/2503-3384 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2011
%A P.Ashok Babu
%A Dr.K.V.S.V.R. Prasad
%T Removal of Ocular Artifacts from EEG Signals by Fast RLS Algorithm using Wavelet Transform%T
%J International Journal of Computer Applications
%V 21
%N 4
%P 1-5
%R 10.5120/2503-3384
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This paper presents an adaptive filtering method to remove ocular artifacts in the electroencephalogram (EEG) records. The major concern in analyzing EEG signal is the presence of ocular artifacts in EEG records caused due to various factors. It is essential to design specific filters to remove the artifacts in EEG records. Here, we proposed an adaptive filtering method that uses RLS (Recursive Least Square) algorithm and FRLS (Fast Recursive Least Squares) to remove ocular artifacts from EEG recordings through wavelet transform. We compared RLS & FRLS algorithms with wavelet transforms. Elapsed time can be decreased by using the FRLS algorithm compared to other techniques and also we can compare the PSNR and MSE values.
References
- Akay, M. 1998.“Time Frequency and Wavelets in Biomedical Signal Processing”, IEEE Press series in biomedical Engineering
- Croft, R.J. and R.J. Barry, 2000. “Removal of ocular artifact from the EEG: a review”, Clinical Neurophysiology, 30(1): 5 – 19.
- Garces, A., Correa, E. Laciar, H.D. Patino and M.E. Valentinuzzi, 2007. “Artifact removal from EEG Signals using adaptive filters in cascade” ,16 Argentine Bioengineering the Congress and the 5th Conference on Clinical Engineering, 90:1-10.
- Goldberger, A.L., L.A.N. Amaral, L. Glass, J.M. Hausdorff, P.C.h. Ivanov, R.G. Mark,J.E. Mietus, G.B. Moody,C.K..Peng, H.E. Stanley, 2000. Physiobank, PhysioToolkit PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220 Circulation Electronic, Webpage http://circ.ahajournals.org/cgi/content/full/101/23/e215;
- Mallat, S.G. 1989. “ A Theory for Multiresolution signal decomposition : The Wavelet representation”.IEEE Trans. On Pattern Analysis and Machine Intell. 2(7)674 - 693
- Nason, G.P. and B.W. Silverman, 1995.“The Stationary Wavelet Transform and some Statistical Applications”, Tech. Rep. BS8 1Tw, University of Bristol.
- Samar, V. J., 1999. Ajit Bopardikar,Raghuveer Rao, and Kenneth Swartz, “Wavelet analysis of neuroelectric waveforms: A conceptual tutorial”, Brain and Language, 66: 7 – 60, 1999.
- Sidney, C. 1998. Burrus, Ramesh A Gopinath and Haitao Guo, “Introduction to wavelets and wavelet transforms”, Prentice- Hall international Inc.
- Tompkins, W.J. 1993. “Biomedical digital signal processing”, New Jersey, Prentice Hall.
- Widrow, B. and S.D. Stearns, 1985. “Adaptive Signal Processing”, New Jersey, Prentice Hall.
- P.senthil Kumar, R. Armuganathan, K.Sivakumar, C.Vimal,2009. “An Adaptive method to remove ocular Artifacts from EEG signals using Wavelet Transforms’’, Journal of Applied Science and Research.
- Jacob Benesty, Tomas G¨ ansler, “New Insights into the RLS Algorithm’’, EURASIP Journal on Applied Signal Processing 2004:3, 331–339
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