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

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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: Copyright 2011 Computer Science Journals. Open access journal. This publication is copyright. It may be reproduced in whole or in part for the purposes of study, research, or review, but is subject to the inclusion of an acknowledgment of the source.
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: 22 Oct 2014 04:39
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
10 Technology > 1004 Medical Biotechnology > 100402 Medical Biotechnology Diagnostics (incl. Biosensors)
06 Biological Sciences > 0601 Biochemistry and Cell Biology > 060105 Cell Neurochemistry
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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