An Intersubject Brain-Computer Interface Based on Domain-Adversarial Training of Convolutional Neural Network.

Journal: IEEE transactions on bio-medical engineering
Published Date:

Abstract

OBJECTIVE: Attention decoding plays a vital role in daily life, where electroencephalography (EEG) has been widely involved. However, training a universally effective model for everyone is impractical due to substantial interindividual variability in EEG signals. To tackle the above challenge, we propose an end-to-end brain-computer interface (BCI) framework, including temporal and spatial one-dimensional (1D) convolutional neural network and domain-adversarial training strategy, namely DA-TSnet.

Authors

  • Di Chen
    Department of Gastroenterology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China. Electronic address: 2389446889@qq.com.
  • Haiyun Huang
  • Zijing Guan
  • Jiahui Pan
    School of Software, South China Normal University, Guangzhou 510641, China.
  • Yuanqing Li
    Center for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, China.