Neural signal classification using a simplified feature set with nonparametric clustering

Zhi Yang, Qi Zhao, Wentai Liu

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

This paper presents a spike sorting method using a simplified feature set with a nonparametric clustering algorithm. The proposed feature extraction algorithm is efficient and has been implemented with a custom integrated circuit chip interfaced with the PC. The proposed clustering algorithm performs nonparametric clustering. It defines an energy function to characterize the compactness of the data and proves that the clustering procedure converges. Through iterations, the data points collapse into well formed clusters and the associated energy approaches zero. By claiming these isolated clusters, neural spikes are classified.

Original languageEnglish (US)
Pages (from-to)412-422
Number of pages11
JournalNeurocomputing
Volume73
Issue number1-3
DOIs
StatePublished - Jan 1 2009

Keywords

  • Action potential
  • Clustering
  • Spike feature extraction
  • Spike sorting

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