Decoding micro-electrocorticographic signals by using explainable 3D convolutional neural network to predict finger movements.

Journal: Journal of neuroscience methods
PMID:

Abstract

BACKGROUND: Electroencephalography (EEG) and electrocorticography (ECoG) recordings have been used to decode finger movements by analyzing brain activity. Traditional methods focused on single bandpass power changes for movement decoding, utilizing machine learning models requiring manual feature extraction.

Authors

  • Chao-Hung Kuo
    Department of Internal Medicine, Kaohsiung Municipal Siaogang Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan; Division of Gastroenterology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan; Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan. Electronic address: kjh88kmu@gmail.com.
  • Guan-Tze Liu
    Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
  • Chi-En Lee
    Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan.
  • Jing Wu
    School of Pharmaceutical Science, Jiangnan University, Wuxi, 214122, Jiangsu, China.
  • Kaitlyn Casimo
    Center for Neurotechnology, University of Washington, Seattle, WA, USA.
  • Kurt E Weaver
    Center for Neurotechnology, University of Washington, Seattle, WA, USA; Department of Radiology, and Integrated Brain Imaging Center, University of Washington, Seattle, WA, USA.
  • Yu-Chun Lo
  • You-Yin Chen
  • Wen-Cheng Huang
    Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
  • Jeffrey G Ojemann
    Department of Neurological Surgery, University of Washington, Seattle, WA, USA.