Efficiently searching through large tACS parameter spaces using closed-loop Bayesian optimization.

Journal: Brain stimulation
Published Date: Jul 4, 2019

Abstract

BACKGROUND: Selecting optimal stimulation parameters from numerous possibilities is a major obstacle for assessing the efficacy of non-invasive brain stimulation.

Authors

  • Romy Lorenz
    MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF, UK; Max-Planck Institute for Human Cognitive and Brain Sciences, Leipzig, 04303, Germany. Electronic address: romy.lorenz@mrc-cbu.cam.ac.uk.
  • Laura E Simmons
    Computational, Cognitive and Clinical Neuroscience Laboratory, Department of Medicine, Imperial College London, London, W12 0NN, UK.
  • Ricardo P Monti
    Gatsby Computational Neuroscience Unit, University College London, London, W1T 4JG, UK.
  • Joy L Arthur
    Computational, Cognitive and Clinical Neuroscience Laboratory, Department of Medicine, Imperial College London, London, W12 0NN, UK.
  • Severin Limal
    Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, OX1 3PT, UK.
  • Ilkka Laakso
    Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland.
  • Robert Leech
  • Ines R Violante
    School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK. Electronic address: ines.violante@surrey.ac.uk.

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