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Proceedings Paper

Artifact rejection for improving the performance of evoked potential neural network classifiers
Author(s): Lalit Gupta; Dennis L. Molfese; Ravi Tammana; Mark McAvoy
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Paper Abstract

This paper is aimed at improving, through artifact rejection, the performance of neural network evoked potential (EP) classifiers designed to detect match/mismatch conditions. A cluster analysis approach is formulated to identify artifacts that occur in the signals used for training the neural network classifiers. The clustering based artifact detection algorithm uses a distance measure resulting from a nonlinear alignment procedure designed to optimally align EP signals. Match and mismatch EPs collected for network training are clustered and the identified artifact signals are excluded from the training set. Artifacts that occur during testing are also identified and rejected by including an additional output in the neural net classifier for the artifact class. Preliminary experiments conducted show significant improvements in classification accuracy when the proposed artifact rejection methods are incorporated in the training and testing phases of a neural network EP classifier.

Paper Details

Date Published: 18 August 1995
PDF: 11 pages
Proc. SPIE 2622, Optical Engineering Midwest '95, (18 August 1995); doi: 10.1117/12.216788
Show Author Affiliations
Lalit Gupta, Southern Illinois Univ./Carbondale (United States)
Dennis L. Molfese, Southern Illinois Univ./Carbondale (United States)
Ravi Tammana, Audre Inc. (United States)
Mark McAvoy, Southern Illinois Univ./Carbondale (United States)

Published in SPIE Proceedings Vol. 2622:
Optical Engineering Midwest '95
Rudolph P. Guzik, Editor(s)

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