Deep Learning With Convolutional Neural Networks for Motor Brain-Computer Interfaces Based on Stereo-Electroencephalography (SEEG).

Journal: IEEE journal of biomedical and health informatics
Published Date: May 4, 2023

Abstract

OBJECTIVE: Deep learning based on convolutional neural networks (CNN) has achieved success in brain-computer interfaces (BCIs) using scalp electroencephalography (EEG). However, the interpretation of the so-called 'black box' method and its application in stereo-electroencephalography (SEEG)-based BCIs remain largely unknown. Therefore, in this paper, an evaluation is performed on the decoding performance of deep learning methods on SEEG signals.

Authors

  • Xiaolong Wu
    Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
  • Shize Jiang
    Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
  • Guangye Li
  • Shengjie Liu
    School of Computer Science, Sichuan University, Chengdu 610065, China.
  • Benjamin Metcalfe
    Centre for Autonomous Robotics (CENTAUR), University of Bath, Bath BA2 7AY, UK.
  • Liang Chen
    Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
  • Dingguo Zhang

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