Adaptive filtering of evoked potentials using higher-order adaptive signal enhancer with genetic-type variable step-size prefilter
Journal
Medical and Biological Engineering and Computing
Journal Volume
43
Journal Issue
5
Pages
638-647
Date Issued
2005
Author(s)
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. © IFMBE: 2005.
Subjects
Adaptive signal enhancer; Evoked potentials; Genetic-type; Higher-order statistics; Variable step-size algorithm
Other Subjects
Adaptive filtering; Electroencephalography; Genetic algorithms; Least squares approximations; Signal to noise ratio; Statistical methods; Adaptive signal enhancer; Genetic-type prefilter; Higher-order statistics; Variable step-size algorithm; Variable step-size prefilter; Bioelectric potentials; algorithm; article; electroencephalogram; evoked response; low frequency noise; noise measurement; normal distribution; regression analysis; signal detection; signal noise ratio; Algorithms; Electroencephalography; Evoked Potentials; Humans; Signal Processing, Computer-Assisted
Type
journal article