Estimation of Evoked Potentials Using High Order Statistic-Based Adaptive Filter

Abstract

This paper is to present a high order statistics-based adaptive interference cancel filter (AIC-HOS) to process evoked potential (EP). In conventional ensemble averaging method, experiments have to conduct repetitively to record the required data. In normalized LMS adaptive filter, inappropriate step size always causes deficiency. This AIC-HOS system has none of the above disadvantages. This system was experimented in somatosensory evoked potential corrupted with EEG. Gradient type algorithm is used in this AIC-HOS structure to regulate the SNR of EEG and EP. This method is also simulated with visual evoked potential and audio evoked potential. The results obtained are satisfactory and acceptable in clinical usage. The AIC-HOS is superior to normalized LMS using adaptive filter in that it converges easily. Moreover, it is not sensitive to selection of step size in stabilities in convergency.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA410300

Entities

People

  • Bor-shing Lin
  • Bor-shyh Lin
  • Fok-ching Chong
  • Jen-chien Chien
  • Shu-mei Wu

Organizations

  • National Taiwan University

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Abstracts
  • Adaptive Filters
  • Algorithms
  • Computations
  • Data Science
  • Electrical Engineering
  • Electrophysiological Phenomena
  • Engineering
  • Equations
  • Filters
  • Gaussian Processes
  • Maximum Likelihood Estimation
  • Noise
  • Order Statistics
  • Power Spectra
  • Spectra
  • Statistics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Systems Analysis and Design
  • Vision Science/Vision Psychology/Cognitive Neuroscience.