EEG-Based Feature Classification Combining 3D-Convolutional Neural Networks with Generative Adversarial Networks for Motor Imagery.

Journal: Journal of integrative neuroscience
PMID: 39207066

Abstract

BACKGROUND: The adoption of convolutional neural networks (CNNs) for decoding electroencephalogram (EEG)-based motor imagery (MI) in brain-computer interfaces has significantly increased recently. The effective extraction of motor imagery features is vital due to the variability among individuals and temporal states.

Authors

  • Chengcheng Fan
    Shanghai University of Medicine & Health Science, School of Medical Instrument, 257 Tianxiong Road, Pudong New District, Shanghai 201318, China.
  • Banghua Yang
    School of Mechatronic Engineering and Automation, Research Center of Brain Computer Engineering, Shanghai University, Shanghai, 200444, China. yangbanghua@126.com.
  • Xiaoou Li
    Shanghai University of Medicine & Health Science, School of Medical Instrument, 257 Tianxiong Road, Pudong New District, Shanghai 201318, China.
  • Shouwei Gao
    School of Mechanical and Electrical Engineering and Automation, Shanghai University, Shanghai 200444, China.
  • Peng Zan
    Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China.

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