Blind Source Separation for Spike Sorting of High Density Microelectrode Array Recordings

  • Jäckel D
  • Frey U
  • Fiscella M
 et al. 
  • 4

    Readers

    Mendeley users who have this article in their library.
  • N/A

    Citations

    Citations of this article.

Abstract

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.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

There are no full text links

Authors

  • David Jäckel

  • Urs Frey

  • Michele Fiscella

  • Andreas Hierlemann

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free