The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Biomedical Engineering, Xi’an Jiaotong University, Xi’an, China
The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Biomedical Engineering, Xi’an Jiaotong University, Xi’an, China
The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Biomedical Engineering, Xi’an Jiaotong University, Xi’an, China
The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Biomedical Engineering, Xi’an Jiaotong University, Xi’an, China
The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Biomedical Engineering, Xi’an Jiaotong University, Xi’an, China
The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Biomedical Engineering, Xi’an Jiaotong University, Xi’an, China
The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Biomedical Engineering, Xi’an Jiaotong University, Xi’an, China
The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Biomedical Engineering, Xi’an Jiaotong University, Xi’an, China
Quadcopter is an important way for the human to explore the physical world. The brain-computer interface (BCI) technology is used to control the quadcopter flight in order to help disabled persons communicate with the external world freely. In this study, a quadcopter control system using a hybrid BCI based on off-line optimization and enhanced human-machine interaction was designed to control the quadcopter flight in 3D physical space. The proposed system implemented the control of quadcopter moving up/down, forward/backward, left/right by six different SSVEP, and turning left/right by left-hand and right-hand motor imagery. Meanwhile, the optimization of the control system and the human-machine interaction enhancement improved practicability in real-time use. Five subjects participated in an on-line experiment to control the quadcopter flight in real-time. The average classification accuracy of EEG-based commands in the on-line experiment was 87.09±2.82% and information transfer rate (ITR) was 0.857±0.085 bits/min. The results demonstrated the feasibility of multidirectional control of quadcopter flight in 3D space by using hybrid BCI technology and revealed the practicality and operability of the hybrid BCI control system based on off-line optimization and human-machine interaction enhancement.
