The M-Sorter: An automatic and robust spike detection and classification system

Yuan Yuan, Chenhui Yang, Jennie Si

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

Neural spike detection and classification, or spike sorting, is the first and a critical step prior to any single unit based neuroscientific studies and applications. A good spike sorter is usually characterized by high detection and classification accuracy, robust to changes in signal-to-noise ratio, objectivity in detection results or less user dependency, and real-time applicability. Here we present an automatic and robust spike detection and classification system, the M-Sorter, based on the multiple correlation of wavelet coefficients (MCWC) detection algorithm in conjunction with template matching for classification. Unlike many existing spike sorters that make use of a series of complex spike classifiers to deal with the challenges resulted from a low performance spike detector, the M-Sorter relies on a high performance yet computationally efficient detection algorithm and thus a simple classifier suffices to generate high quality spike sorting results. In this paper we provide step by step implementation procedures of the M-Sorter, and compare its performance with other popular sorters.

Original languageEnglish (US)
Pages (from-to)281-290
Number of pages10
JournalJournal of Neuroscience Methods
Volume210
Issue number2
DOIs
StatePublished - Sep 30 2012

Keywords

  • Classification
  • Detection
  • K-Means
  • MCWC
  • Neural waveform
  • Sorting
  • Spike
  • Template Matching

ASJC Scopus subject areas

  • Neuroscience(all)

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