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Unsupervised wavelet-based spike sorting with dynamic codebook searching and replenishment
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DOI：10.1016/j.neucom.2009.11.006.png)
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Spike information is beneficial for correlating neuronal activity to various stimuli, finding target neural areas for deep brain stimulation, and decoding intended motor command for brain-machine interface. Unsupervised classification based on spike features provides a way to separate spikes generated from different neurons. Here, we propose an unsupervised spike sorting method based on specific wavelet coefficients (SWC) and using both a new spike alignment technique based on multi-peak energy comparison (MPEC) and a dynamic codebook-based template-matching algorithm with a class-merging feature. The MPEC alignment reduced inconsistent alignment caused by spike deformation. Using SWC not only reduced the number of features but also performed better in terms of matching a neuronal spike to its own class than relying on spike waveform or whole wavelet coefficients. Moreover, the employed codebook searching and replenishment can be operated in an online, real-time mode. (C) 2009 Elsevier B.V. All rights reserved.
Keyword:
Unsupervised spike sorting
Spike alignment
Wavelet transform
Template matching
Class mergence
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