Enhancing Domain Diversity of Transfer Learning-Based SSVEP-BCIs by the Reconstruction of Channel Correlation.

Journal: IEEE transactions on bio-medical engineering
PMID: 39255081

Abstract

OBJECTIVE: The application of transfer learning, specifically pre-training and fine-tuning, in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has been demonstrated to effectively improve the classification performance of deep learning methods with limited calibration data. However, effectively learning task-related knowledge from source domains during the pre-training phase remains challenging. To address this issue, this study proposes an effective data augmentation method called Reconstruction of Channel Correlation (RCC) to optimize the utilization of the source domain data.

Authors

  • Wenlong Ding
    Haihe Hospital, Tianjin University, Tianjin Institute of Respiratory Diseases, Tianjin, China.
  • Aiping Liu
    School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China.
  • Chengjuan Xie
  • Kai Wang
    Department of Rheumatology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China.
  • Xun Chen
    Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China. xunchen@ece.ubc.ca.

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