Spike sorting using non parametric clustering via Cauchy Schwartz PDF divergence

Sudhir Rao, Justin C. Sanchez, Seungju Han, Jose C. Principe

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

4 Scopus citations

Abstract

We propose a new method of clustering neural spike waveforms for spike sorting. After detecting the spikes using a threshold detector, we use principal component analysis (PCA) to get the first few PCA components of the data. Clustering on these PCA components is achieved by maximizing the Cauchy Schwartz PDF divergence measure which uses the Parzen window method to non parametrically estimate the pdf of the clusters. Comparison with other clustering techniques in spike sorting like k-means and Gaussian mixture elucidates the superiority of our method in terms of classification results and computational complexity.

Original languageEnglish (US)
Title of host publication2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
PagesV881-V884
StatePublished - Dec 1 2006
Event2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 - Toulouse, France
Duration: May 14 2006May 19 2006

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume5
ISSN (Print)1520-6149

Other

Other2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006
CountryFrance
CityToulouse
Period5/14/065/19/06

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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    Rao, S., Sanchez, J. C., Han, S., & Principe, J. C. (2006). Spike sorting using non parametric clustering via Cauchy Schwartz PDF divergence. In 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings (pp. V881-V884). [1661417] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 5).