Visual evoked potential estimation by artificial neural network filter: comparison with the ensemble averaging method

Author

Fung, K.S.M. ; Chan, F.H.Y. ; Lam, F.K. ; Poon, P.W.F. ; Liu, J.G.

Author_Institution

Dept. of Electr. & Electron. Eng., Hong Kong Univ., Hong Kong

Volume

1

fYear

1995

fDate

20-25 Sep 1995

Firstpage

805

Abstract

The application of an artificial neural network filter (ANNF) to give a non-linear estimation of the visual evoked potential (VEP) is presented. A feed forward ANNF is designed and trained by a training set consisting of a training signal and a target signal. The training signal is the raw VEP from a single trial while the target signal has much higher SNR which is achieved by ensemble averaging of 100 trials. The result shows that 10 ensembles is needed by ANNF to generate a satisfactory result against 60 ensembles required by traditional ensemble averaging. VEP from individual trial could be obtained; thus the study of the variation of signal across trials is possible

Keywords

feedforward neural nets; filters; medical signal processing; multilayer perceptrons; visual evoked potentials; artificial neural network filter; clinical application; electrodiagnostics; ensemble averaging method; gross electrical response; nonlinear estimation; signal variation across trials; training set; visual evoked potential estimation; Artificial neural networks; Electrodes; Electroencephalography; Feeds; Filters; Multilayer perceptrons; Nervous system; Neurons; Physiology; Signal design;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 1995., IEEE 17th Annual Conference

Conference_Location

Montreal, Que.

Print_ISBN

0-7803-2475-7

Type

conf

DOI

10.1109/IEMBS.1995.575372

Filename

575372