Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI.

Journal: Journal of neural engineering
Published Date:

Abstract

OBJECTIVE: Despite the effective application of deep learning (DL) in brain-computer interface (BCI) systems, the successful execution of this technique, especially for inter-subject classification, in cognitive BCI has not been accomplished yet. In this paper, we propose a framework based on the deep convolutional neural network (CNN) to detect the attentive mental state from single-channel raw electroencephalography (EEG) data.

Authors

  • Fatemeh Fahimi
    School of Computer Science and Engineering, Nanyang Technological University (NTU), Singapore. Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore.
  • Zhuo Zhang
  • Wooi Boon Goh
  • Tih-Shi Lee
  • Kai Keng Ang
  • Cuntai Guan