Toward automated electrode selection in the electronic depth control strategy for multi-unit recordings

  • Authors:

  • Gert Van Dijck;Ahmad Jezzini;Stanislav Herwik;Sebastian Kisban;Karsten Seidl;Oliver Paul;Patrick Ruther;Francesca Ugolotti Serventi;Leonardo Fogassi;Marc M. Van Hulle;Maria Alessandra Umiltà

  • Affiliations:

  • Laboratorium voor Neuro- en Psychofysiologie, Katholieke Universiteit Leuven, Leuven, Belgium;Department of Neuroscience, University of Parma, Parma, Italy;Microsystem Materials Laboratory, Department of Microsystems Engineering, University of Freiburg, Freiburg, Germany;Microsystem Materials Laboratory, Department of Microsystems Engineering, University of Freiburg, Freiburg, Germany;Microsystem Materials Laboratory, Department of Microsystems Engineering, University of Freiburg, Freiburg, Germany;Microsystem Materials Laboratory, Department of Microsystems Engineering, University of Freiburg, Freiburg, Germany;Microsystem Materials Laboratory, Department of Microsystems Engineering, University of Freiburg, Freiburg, Germany;Department of Neuroscience, University of Parma, Parma, Italy;Department of Neuroscience, University of Parma, Parma, Italy and Italian Institute of Technology, Section of Parma, Parma, Italy;Laboratorium voor Neuro- en Psychofysiologie, Katholieke Universiteit Leuven, Leuven, Belgium;Department of Neuroscience, University of Parma, Parma, Italy and Italian Institute of Technology, Section of Parma, Parma, Italy

  • Venue:
  • ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
  • Year:
  • 2010

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Abstract

Multi-electrode arrays contain an increasing number of electrodes. The manual selection of good quality signals among hundreds of electrodes becomes impracticable for experimental neuroscientists. This increases the need for an automated selection of electrodes containing good quality signals. To motivate the automated selection, three experimenters were asked to assign quality scores, taking one of four possible values, to recordings containing action potentials obtained from the monkey primary somatosensory cortex and the superior parietal lobule. Krippendorff's alpha-reliability was then used to verify whether the scores, given by different experimenters, were in agreement. A Gaussian process classifier was used to automate the prediction of the signal quality using the scores of the different experimenters. Prediction accuracies of the Gaussian process classifier are about 80% when the quality scores of different experimenters are combined, through a median vote, to train the Gaussian process classifier. It was found that predictions based also on firing rate features are in closer agreement with the experimenters' assignments than those based on the signal-to-noise ratio alone.