Neural Network Approach to the Enhancement of EEG Signals in the Presence of EOG Artefacts

Sadasivan, PK and Dutt, Narayana D (1991) Neural Network Approach to the Enhancement of EEG Signals in the Presence of EOG Artefacts. In: 1991 IEEE Region 10 International Conference on EC3-Energy, Computer, Communication and Control Systems. TENCON '91, 28-30 August, New Delhi,India, Vol.2, 136-140.

Preview

PDF Neural_Network_Approach.pdf Download (845kB)

Abstract

Human electroencephalogram (EEG) contains useful diagnostic information on a variety of neurological disorders. However, like all biomedical signals, EEG is also contaminated with many , unwanted signals or artefacts which seriously affects its clinical usefulness. One of the main disturbances is due to the eye movements, which generate an electrical activity called electrooculogram (EOG). In this paper, we propose a Neural Network (NN) approach to the enhancement of EEG signals in the presence of EOG artefacts. We recast the EEG enhancement problem into the optimization framework by developing an appropriate cost function. The cost function is nothing but the energy in the enhanced EEG signal which is obtained through a non-linear prediction formulation, unlike the conventionally used linear prediction formulation. The minimization property of the Hopfield Neural Network is exploited to solve this problem. The optimum non- linear predictor coefficients obtained from this minimization algorithm are used to estimate the EOG artefact which is then subtracted from the corrupted EEG signal, sample by sample, to get the artefact minimized signal. Thus the power and efficacy of the NN approach have been exploited for the purpose of enhancement of CXG signals is the presence of EOG artefacts.

Item Type:	Conference Paper
Publisher:	IEEE
Additional Information:	Copyright 1990 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	25 May 2006
Last Modified:	19 Sep 2010 04:27
URI:	http://eprints.iisc.ac.in/id/eprint/6892

Actions (login required)

View Item