A hybrid brain computer interface system based on the neurophysiological protocol and brain-actuated switch for wheelchair control.
BACKGROUND
Brain Computer Interfaces (BCIs) are developed to translate brain waves into machine instructions for external devices control. Recently, hybrid BCI systems are proposed for the multi-degree control of a real wheelchair to improve the systematical efficiency of traditional BCIs. However, it is difficult for existing hybrid BCIs to implement the multi-dimensional control in one command cycle.


METHODS
This paper proposes a novel hybrid BCI system that combines motor imagery (MI)-based bio-signals and steady-state visual evoked potentials (SSVEPs) to control the speed and direction of a real wheelchair synchronously. Furthermore, a hybrid modalities-based switch is firstly designed to turn on/off the control system of the wheelchair.


RESULTS
Two experiments were performed to assess the proposed BCI system. One was implemented for training and the other one conducted a wheelchair control task in the real environment. All subjects completed these tasks successfully and no collisions occurred in the real wheelchair control experiment.


CONCLUSIONS
The protocol of our BCI gave much more control commands than those of previous MI and SSVEP-based BCIs. Comparing with other BCI wheelchair systems, the superiority reflected by the index of path length optimality ratio validated the high efficiency of our control strategy.


CONCLUSIONS
The results validated the efficiency of our hybrid BCI system to control the direction and speed of a real wheelchair as well as the reliability of hybrid signals-based switch control.
Department of Computer Science and Technology, Tongji University, 201804 Shanghai, China; Institute of Medical Psychology and Behavioral Neurobiology, University of Tuebingen, D-72074 Tuebingen, Germany. Electronic address: caolei1987@gmail.com.
Lei
L
Cao
Department of Computer Science and Technology, Tongji University, 201804 Shanghai, China. Electronic address: nijance@gmail.com.
Jie
J
Li
Department of Computer Science and Technology, Tongji University, 201804 Shanghai, China. Electronic address: jhf@tongji.edu.cn.
Hongfei
H
Ji
Department of Computer Science and Technology, Tongji University, 201804 Shanghai, China. Electronic address: cjjiang@tongji.edu.cn.
Changjun
C
Jiang
D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000295:instrumentation / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology; D006801:Humans; D007092:Imagination / Q000502:physiology; D008297:Male; D009043:Motor Activity; D015203:Reproducibility of Results; D014910:Wheelchairs; D059015:Wireless Technology; D055815:Young Adult
Hybrid BCI; Motor imagery; SSVEP; Switch control; Wheelchair control