Artifact rejection for improving the performance of evoked potential neural network classifiers

Lalit Gupta, Dennis L. Molfese, Ravi Tammana, Mark McAvoy

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherSociety of Photo-Optical Instrumentation Engineers
Number of pages11
ISBN (Print)0819419869, 9780819419866
StatePublished - 1995
Externally publishedYes
EventOptical Engineering Midwest'95. Part 2 (of 2) - Chicago, IL, USA
Duration: May 18 1995May 19 1995


OtherOptical Engineering Midwest'95. Part 2 (of 2)
CityChicago, IL, USA

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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