Adaptive filtering of evoked potentials using higher-order adaptive signal enhancer with genetic-type variable step-size prefilter

Bor-Shyh Lin*, F. C. Chong, F. Lai

*Corresponding author for this work

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

An adaptive signal enhancer based on third-order statistics with a genetic-type, variable step-size prefilter is introduced to recover evoked potentials (EPs). EPs are usually embedded in the ongoing electroencephalogram with a very low signal-to-noise ratio (SNR). As a higher-order statistics technique has a natural tolerance to Gaussian noise, it is applicable for filtering EPs. An adaptive signal enhancer based on third-order statistics was used as the major filter in this study. However, the efficiency of the adaptive signal enhancer was reduced when the total power of uncorrelated noises was large. To improve the performance for EPs under poor SNR, a low-noise signal is required. Therefore a prefilter with a genetic-type, variable step-size algorithm was employed to enhance the SNR of the signal in this study. The fundamental idea of a genetic-type, variable step-size algorithm is that its step-sizes are regularly readjusted to optimum. Therefore this algorithm can be used as a prefilter with different noise levels. Experimental results showed that, for filtering EPs, the proposed scheme is superior to the adaptive signal enhancer with a normalised least mean square algorithm.

Original languageEnglish
Pages (from-to)638-647
Number of pages10
JournalMedical and Biological Engineering and Computing
Volume43
Issue number5
DOIs
StatePublished - 1 Sep 2005

Keywords

  • Adaptive signal enhancer
  • Evoked potentials
  • Genetic-type
  • Higher-order statistics
  • Variable step-size algorithm

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