A new approach to denoising EEG signals - merger of translation invariant wavelet and ICA

Walters-Williams, Janett and Li, Yan (2011) A new approach to denoising EEG signals - merger of translation invariant wavelet and ICA. International Journal of Biometrics and Bioinformatics, 5 (2). pp. 130-148. ISSN 1985-2347

Abstract

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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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.
Depositing User: Dr Yan Li
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 01 Feb 2012 10:24
Last Modified: 03 Jul 2013 00:52
Uncontrolled Keywords: independent component analysis, wavelet transform, unscented Kalman filter, electroencephalogram (EEG), cycle spinning
Fields of Research (FOR2008): 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 Objective (SEO2008): E Expanding Knowledge > 97 Expanding Knowledge > 970110 Expanding Knowledge in Technology
URI: http://eprints.usq.edu.au/id/eprint/19995

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