Walters-Williams, Janett and Li, Yan (2011) Performance comparison of known ICA algorithms to a wavelet-ICA merger. Signal Processing: An International Journal , 5 (3). pp. 80-92. ISSN 1985-2339
Official URL: http://www.cscjournals.org/csc/manuscript/Journals...
Abstract
These signals are however contaminated with artifacts which must be removed to have pure EEG signals. These artifacts can be removed by 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. Comparing these ICA algorithms, it is observed that our new techniques perform as well as these algorithms at denoising EEG signals.
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| Item Type: | Article (Commonwealth Reporting Category C) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | Published Version not available in ePrints due to copyright policy of publisher, but available in open access from the publisher website. |
| Faculty/School / Institute/Centre: | Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013) |
| Faculty/School / Institute/Centre: | Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013) |
| Date Deposited: | 02 Feb 2012 04:58 |
| Last Modified: | 05 Jan 2015 05:10 |
| Uncontrolled Keywords: | independent component analysis, wavelet transform, unscented Kalman filter, electroencephalogram |
| Fields of Research (2008): | 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining 09 Engineering > 0906 Electrical and Electronic Engineering > 090609 Signal Processing |
| Socio-Economic Objectives (2008): | E Expanding Knowledge > 97 Expanding Knowledge > 970110 Expanding Knowledge in Technology |
| URI: | http://eprints.usq.edu.au/id/eprint/19991 |
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