Walters-Williams, Janett and Li, Yan (2011) Performance comparison of known ICA algorithms to a wavelet-ICA merger. Signal Processing, 5 (3). pp. 80-92. ISSN 2005-4254
|HTML Citation||EndNote||MODS||Dublin Core||Reference Manager|
Full text not available from this archive.
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.
|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|
|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:||02 Feb 2012 14:58|
|Last Modified:||21 Jun 2012 10:29|
Archive Staff Only: edit this record