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A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wavelet and ICA
Janett Walters-Williams, Yan Li
Pages - 130 - 148     |    Revised - 01-05-2011     |    Published - 31-05-2011
Volume - 5   Issue - 2    |    Publication Date - May / June 2011  Table of Contents
Independent Component Analysis, Wavelet Transform, Electroencephalogram (EEG), Unscented Kalman Filter, Cycle Spinning
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.
CITED BY (9)  
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3 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.
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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.
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Mr. Janett Walters-Williams
- Jamaica
Dr. Yan Li
Department of Mathematics & Computing, Faculty of Sciences, University of Southern Queensland, Toowoomba, Australia - Australia