Higher order statistics-based radial basis function network for evoked potentials

Bor-Shyh Lin, Bor Shing Lin, Fok Ching Chong, Feipei Lai

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

In this study, higher order statistics-based radial basis function network (RBF) was proposed for evoked potentials (EPs). EPs provide useful information on diagnosis of the nervous system. They are time-varying signals typically buried in ongoing EEG, and have to be extracted by special methods. RBF with least mean square (LMS) algorithm is an effective method to extract EPs. However, using LMS algorithm usually encounters gradient noise amplification problem, i.e., its performance is sensitive to the selection of step sizes and additional noise. Higher order statistics technique, which can effectively suppress Gaussian and symmetrically distributed non-Gaussian noises, was used to reduce gradient noise amplification problem on adaptation in this study. Simulations and human experiments were also carried out in this study.

Original languageEnglish
Pages (from-to)93-100
Number of pages8
JournalIEEE Transactions on Biomedical Engineering
Volume56
Issue number1
DOIs
StatePublished - 1 Jan 2009

Keywords

  • Evoked potentials
  • Higher order statistics
  • Least mean square algorithm
  • Radial basis function network

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