Energy based evolving mean shift algorithm for neural spike classification

Zhi Yang, Qi Zhao, Wentai Liu

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

4 Scopus citations

Abstract

This paper presents a novel nonparametric clustering algorithm, called energy based evolving mean shift (EMS) clustering. It defines an energy function to characterize the compactness of the underlying data set and proves the clustering procedure converges. Through iterations, the data points collapse into well formed clusters and the associated energy approaches zero. Although as a general algorithm, the EMS is designed for resolving neural spikes to individual sources which is usually called "spike sorting".

Original languageEnglish (US)
Title of host publicationProceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009
Subtitle of host publicationEngineering the Future of Biomedicine, EMBC 2009
PublisherIEEE Computer Society
Pages966-969
Number of pages4
ISBN (Print)9781424432967
DOIs
StatePublished - 2009
Event31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 - Minneapolis, MN, United States
Duration: Sep 2 2009Sep 6 2009

Publication series

NameProceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009

Other

Other31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009
CountryUnited States
CityMinneapolis, MN
Period9/2/099/6/09

PubMed: MeSH publication types

  • Evaluation Study
  • Journal Article

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