Decoding intracranial EEG data with multiple kernel learning method.

Journal: Journal of neuroscience methods
PMID: 26692030

Abstract

BACKGROUND: Machine learning models have been successfully applied to neuroimaging data to make predictions about behavioral and cognitive states of interest. While these multivariate methods have greatly advanced the field of neuroimaging, their application to electrophysiological data has been less common especially in the analysis of human intracranial electroencephalography (iEEG, also known as electrocorticography or ECoG) data, which contains a rich spectrum of signals recorded from a relatively high number of recording sites.

Authors

  • Jessica Schrouff
    Laboratory of Behavioral & Cognitive Neuroscience, Stanford Human Intracranial Cognitive Electrophysiology Program (SHICEP), Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA, USA; Department of Computer Science, University College London, United Kingdom.
  • Janaina Mourão-Miranda
    Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom.
  • Christophe Phillips
    Cyclotron Research Centre, University of Liège, Belgium.
  • Josef Parvizi
    Department of Neurology, Stanford University School of Medicine, Palo Alto, CA, USA.

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