Classifying Obsessive-Compulsive Disorder from Resting-State EEG using Convolutional Neural Networks: A Pilot Study.

Journal: medRxiv : the preprint server for health sciences
Published Date: May 7, 2025

Abstract

OBJECTIVE: Classifying obsessive-compulsive disorder (OCD) using brain data remains challenging. Resting-state electroencephalography (EEG) offers an affordable and noninvasive approach, but traditional machine learning methods have limited its predictive capability. We explored whether convolutional neural networks (CNNs) applied to minimally processed EEG time-frequency representations could offer a solution, effectively distinguishing individuals with OCD from healthy controls.

Authors

  • Brian A Zaboski
    Department of Psychiatry, Yale University School of Medicine, New Haven, CT.
  • Sarah Kathryn Fineberg
    Department of Psychiatry, Yale University School of Medicine, New Haven, CT.
  • Patrick D Skosnik
    Department of Psychiatry, Yale University School of Medicine, New Haven, CT.
  • Stephen Kichuk
    Department of Psychiatry, Yale University School of Medicine, New Haven, CT.
  • Madison Fitzpatrick
    Department of Psychiatry, Yale University School of Medicine, New Haven, CT.
  • Christopher Pittenger
    Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06510, USA.

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