Optimal recognition of neural waveforms

Isaac N. Bankman, Kenneth O. Johnson, Wolfger Schneider

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

Abstract

An optimal approach is presented for the detection and classification of neural spikes as well as resolution of their superpositions. It is shown that the optimal methods which are based on Bayesian classification, perform at the theoretically expected level in simulations with physiological neural spikes and neural noise. The optimal performance is obtained by using a whitening filter that eliminates the autocorrelation in neural noise. The performance remained above 90% correct when the SNR was as low as 2.

Original languageEnglish (US)
Title of host publicationProceedings of the Annual Conference on Engineering in Medicine and Biology
PublisherPubl by IEEE
Pages409-410
Number of pages2
Editionpt 1
ISBN (Print)0780302168
StatePublished - Dec 1 1991
EventProceedings of the 13th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Orlando, FL, USA
Duration: Oct 31 1991Nov 3 1991

Publication series

NameProceedings of the Annual Conference on Engineering in Medicine and Biology
Numberpt 1
Volume13
ISSN (Print)0589-1019

Other

OtherProceedings of the 13th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
CityOrlando, FL, USA
Period10/31/9111/3/91

ASJC Scopus subject areas

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

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