Blind source separation for spike sorting of high density microelectrode array recordings

High-density microelectrode arrays (HD-MEAs) with large numbers of densely packed electrodes potentially allow for recording from every cell on the array and generate large, redundant datasets. Blind-source-separation algorithms (BSS), used to separate mixtures of independent sources into the original signals, are an ideal means to be applied to the spike sorting of HD-MEA recordings. We show that recorded neuronal signals represent convoluted mixtures, and we present a BSS algorithm. The algorithm uses the nonlinear energy operator as preprocessor and an extended method of independent-component analysis to separate convoluted mixtures. The algorithm is applied to recordings from retinal ganglion cells, and its performance is evaluated.