Virtual EEG-electrodes: Convolutional neural networks as a method for upsampling or restoring channels.

Journal: Journal of neuroscience methods
Published Date: Mar 9, 2021

Abstract

BACKGROUND: In clinical practice, EEGs are assessed visually. For practical reasons, recordings often need to be performed with a reduced number of electrodes and artifacts make assessment difficult. To circumvent these obstacles, different interpolation techniques can be utilized. These techniques usually perform better for higher electrode densities and values interpolated at areas far from electrodes can be unreliable. Using a method that learns the statistical distribution of the cortical electrical fields and predicts values may yield better results.

Authors

  • Mats Svantesson
    Department of Clinical Neurophysiology, University Hospital of Linköping, Sweden; Center for Social and Affective Neuroscience, Linköping University, Sweden; Center for Medical Image Science and Visualization, Linköping University, Sweden; Department of Biomedical and Clinical Sciences, Linköping University, Sweden. Electronic address: mats.svantesson@liu.se.
  • Håkan Olausson
    Department of Clinical Neurophysiology, University Hospital of Linköping, Sweden; Center for Social and Affective Neuroscience, Linköping University, Sweden; Department of Biomedical and Clinical Sciences, Linköping University, Sweden.
  • Anders Eklund
    Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
  • Magnus Thordstein
    Department of Clinical Neurophysiology, University Hospital of Linköping, Sweden; Center for Medical Image Science and Visualization, Linköping University, Sweden; Department of Biomedical and Clinical Sciences, Linköping University, Sweden.

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