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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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.
|Item Type:||Article (Commonwealth Reporting Category C)|
|Additional Information:||Published Version not available in ePrints due to copyright policy of publisher, but available in open access from the publisher website.|
|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|
|Deposited On:||01 Feb 2012 20:24|
|Last Modified:||25 Jun 2012 14:43|
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