Deep learning-based EEG source imaging is robust under varying electrode configurations.

Journal: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Published Date: Apr 16, 2025

Abstract

OBJECTIVES: Previous research has underscored the necessity of high-density EEG for accurate and reliable EEG source imaging (ESI) results with conventional ESI methods, limiting their utility in clinical settings with only low-density EEG available. In recent years, deep learning-based ESI methods have exhibited robust performance by directly learning spatiotemporal brain activity patterns from data.

Authors

  • Jesse Rong
    Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States.
  • Rui Sun
    The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou 325027, China.
  • Boney Joseph
    Departments of Neurology and Biomedical Engineering, Mayo Foundation, Rochester, MN, USA.
  • Greg Worrell
    Department of Neurology, Mayo Clinic, Rochester, Minnesota.
  • Bin He
    Clinical Translational Medical Center, The Affiliated Dongguan Songshan Lake Central Hospital, Guangdong Medical University, Dongguan, Guangdong, China.

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