Date of Award

12-1990

Document Type

Thesis

Degree Name

Master of Science in Electrical Engineering

Department

Department of Electrical and Computer Engineering

First Advisor

Robert Williams, PhD

Abstract

This thesis describes a time sequenced adaptive filter developed to estimate visually evoked fields (EF) using visually evoked potentials (EP). These non-stationary signals are buried in strong background noise. The two types of noise are magnetoencephalogram (MEG) and electronencephalogram (EEG). The filter implementation is based on the Ferrara Time Sequenced Adaptive (TSAF) using the Least-Mean-Square (LMS) algorithm and the Williams modified P-vector algorithm (mPa). This essentially results in two filters. A two stage filter structure is used in which the first stage removes the time-varying mean of the input signals. This allows the second stage to process zero-mean signals which increases the convergence speed of the filter. The theory for the two filters is overviewed with the input signals to the filters modelled as the sum of three uncorrelated components: average signal response, signal jitter, and noise. The signal model is verified based on a statistical analysis of simulated EP data files. The software implementation is then shown to be error free.

AFIT Designator

AFIT-EN-GE90D

DTIC Accession Number

ADA230474

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