Spike Sorting Using non Parametric Clustering VIA Cauchy Schwartz PDF Divergence
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