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Journal of Healthcare Engineering
Volume 2017, Article ID 3789386, 7 pages
Research Article

Development of Single-Channel Hybrid BCI System Using Motor Imagery and SSVEP

1Brain Research Center, National Chiao Tung University, Hsinchu City, Taiwan
2Institute of Bioinformatics and System Biology, National Chiao Tung University, Hsinchu City, Taiwan
3Department of Biological Science and Technology, National Chiao Tung University, Hsinchu City, Taiwan
4Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, Hsinchu City, Taiwan
5Office of Physical Education, National Chiao Tung University, Hsinchu City, Taiwan

Correspondence should be addressed to Li-Wei Ko; wt.ude.utcn.liam@okwl

Received 16 March 2017; Revised 17 May 2017; Accepted 22 June 2017; Published 7 August 2017

Academic Editor: Masaki Nakanishi

Copyright © 2017 Li-Wei Ko et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Numerous EEG-based brain-computer interface (BCI) systems that are being developed focus on novel feature extraction algorithms, classification methods and combining existing approaches to create hybrid BCIs. Several recent studies demonstrated various advantages of hybrid BCI systems in terms of an improved accuracy or number of commands available for the user. But still, BCI systems are far from realization for daily use. Having high performance with less number of channels is one of the challenging issues that persists, especially with hybrid BCI systems, where multiple channels are necessary to record information from two or more EEG signal components. Therefore, this work proposes a single-channel (C3 or C4) hybrid BCI system that combines motor imagery (MI) and steady-state visually evoked potential (SSVEP) approaches. This study demonstrates that besides MI features, SSVEP features can also be captured from C3 or C4 channel. The results show that due to rich feature information (MI and SSVEP) at these channels, the proposed hybrid BCI system outperforms both MI- and SSVEP-based systems having an average classification accuracy of 85.6 ± 7.7% in a two-class task.