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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
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KEYWORDS
Independent Component Analysis, Wavelet Transform, Unscented Kalman Filter, Electroencephalogram
ABSTRACT
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)  
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9 Holton, B. D., Mannapperuma, K., Lesniewski, P. J., & Thomas, J. C. (2013). Signal recovery in imaging photoplethysmography. Physiological measurement, 34(11), 1499.
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Mr. Janett Walters-Williams
University of Technology, Jamaica - Jamaica
jwalters@utech.edu.jm
Mr. Yan Li
- Australia


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