Deep Learning in the Identification of Electroencephalogram Sources Associated with Sexual Orientation.

Journal: Neuropsychobiology
Published Date: Jan 1, 2023

Abstract

INTRODUCTION: It is unclear if sexual orientation is a biological trait that has neurofunctional footprints. With deep learning, the power to classify biological datasets without an a priori selection of features has increased by magnitudes. The aim of this study was to correctly classify resting-state electroencephalogram (EEG) data from males with different sexual orientation using deep learning and to explore techniques to identify the learned distinguishing features.

Authors

  • Anastasios Ziogas
    Department of Forensic Psychiatry, University Hospital of Psychiatry Zurich, Zurich, Switzerland.
  • Andreas Mokros
    FernUniversität in Hagen, Hagen, Germany.
  • Wolfram Kawohl
    Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland.
  • Mateo de Bardeci
    Department for Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich (PUK), Switzerland; University Hospital Zurich, Switzerland; University Zurich, Switzerland.
  • Ilyas Olbrich
    RG Gymnasium, Zurich, Switzerland.
  • Benedikt Habermeyer
    Clienia Schlössli AG, Oetwil am See, Switzerland.
  • Elmar Habermeyer
    Department of Forensic Psychiatry, University Hospital of Psychiatry Zurich, Zurich, Switzerland.
  • Sebastian Olbrich
    Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland.

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