Classification of EEG Signals on VEP-Based BCI Systems with Broad Learning

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

22 Scopus Citations
View graph of relations

Author(s)

  • Zhongke Gao
  • Weidong Dang
  • Mingxu Liu
  • Wei Guo
  • Kai Ma

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)7143-7151
Journal / PublicationIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume51
Issue number11
Online published4 Feb 2020
Publication statusPublished - Nov 2021

Abstract

Brain-computer interface (BCI) systems based on electroencephalography (EEG) signals have been extensively used in medical practice. To enhance the BCI performance, improving the classification accuracy of EEG signals is the key, which has always been the focus of research and development. In this article, a novel method integrating complex network and broad learning system (BLS) is proposed for visual evoked potential (VEP)-based BCI research. First, systematic VEP-based brain experiments are conducted for obtaining EEG signals, including steady-state VEP (SSVEP) and steady-state motion VEP (SSMVEP). Then, limited penetrable visibility graph (LPVG) and its degree sequence are employed to implement the preliminary feature extraction. All these features are finally fed into a BLS to study and classify the SSVEP and SSMVEP signals, respectively. The classification results show that our LPVG-based BLS can effectively classify VEP-based EEG signals, with average classification accuracy 96.22% for SSVEP and 74.54% for SSMVEP. These results are significantly better than other comparison methods as well as traditional CCA-based methods. All these open up new venues for studying EEG-based BCI systems via the fusion of network science and BLS.

Research Area(s)

  • Broad learning system (BLS), electroencephalography (EEG) signals, limited penetrable visibility graph (LPVG), steady-state motion VEP (SSMVEP), steady-state VEP (SSVEP)

Citation Format(s)

Classification of EEG Signals on VEP-Based BCI Systems with Broad Learning. / Gao, Zhongke; Dang, Weidong; Liu, Mingxu; Guo, Wei; Ma, Kai; Chen, Guanrong.

In: IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 51, No. 11, 11.2021, p. 7143-7151.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review