Parameter estimation and multichannel fusion for classifying averaged ERPs

Lalit Gupta, Jim Phegley, Dennis L. Molfese

Research output: Contribution to journalConference articlepeer-review

Abstract

A parameter estimation and classification fusion approach is developed to classify averaged event-related potentials (ERPs) recorded from multiple channels. It is shown that the parameters of the averaged ERP ensemble can be estimated directly from the parameters of the single-trial ensemble. The parameter estimation methods are applied to independently design a Gaussian likelihood ratio classifier for each channel. A fusion rule is formulated to classify an ERP using the classification results from all the channels. Very importantly, it is shown that parametric classifiers can be designed and evaluated without having to collect a prohibitively large number of single-trial ERPs. It is also shown that the performance of a majority rule fusion classifier is consistently superior to the rule that selects a single best channel.

Original languageEnglish (US)
Pages (from-to)163-164
Number of pages2
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume1
StatePublished - 2002
EventProceedings of the 2002 IEEE Engineering in Medicine and Biology 24th Annual Conference and the 2002 Fall Meeting of the Biomedical Engineering Society (BMES / EMBS) - Houston, TX, United States
Duration: Oct 23 2002Oct 26 2002

Keywords

  • Classification
  • Event-related potentials
  • Fusion
  • Parameter estimation
  • Signal averaging

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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