Solving alignment problems in neural spike sorting using frequency domain PCA

The principal component analysis (PCA) is a popular projection method in neural spike sorting. When the waveforms extracted from a spike train are aligned incorrectly, however, the projection performance of the PCA deteriorates drastically, and the clustering errors multiply. This drawback is taken care of by the frequency domain PCA in this paper. By experiments, it is shown that the proposed approach maintains good projection performance under considerable alignment errors of the waveforms.