Unsupervised wavelet-based spike sorting with dynamic codebook searching and replenishment

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.