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 language||English (US)|
|Number of pages||10|
|Journal||Journal of Neuroscience Methods|
|State||Published - Sep 30 2012|
- Neural waveform
- Template Matching
ASJC Scopus subject areas