A novel Morse code-inspired method for multiclass motor imagery brain-computer interface (BCI) design.
Motor imagery (MI)-based brain-computer interfaces (BCIs) allow disabled individuals to control external devices voluntarily, helping us to restore lost motor functions. However, the number of control commands available in MI-based BCIs remains limited, limiting the usability of BCI systems in control applications involving multiple degrees of freedom (DOF), such as control of a robot arm. To address this problem, we developed a novel Morse code-inspired method for MI-based BCI design to increase the number of output commands. Using this method, brain activities are modulated by sequences of MI (sMI) tasks, which are constructed by alternately imagining movements of the left or right hand or no motion. The codes of the sMI task was detected from EEG signals and mapped to special commands. According to permutation theory, an sMI task with N-length allows 2 × (2(N)-1) possible commands with the left and right MI tasks under self-paced conditions. To verify its feasibility, the new method was used to construct a six-class BCI system to control the arm of a humanoid robot. Four subjects participated in our experiment and the averaged accuracy of the six-class sMI tasks was 89.4%. The Cohen's kappa coefficient and the throughput of our BCI paradigm are 0.88 ± 0.060 and 23.5bits per minute (bpm), respectively. Furthermore, all of the subjects could operate an actual three-joint robot arm to grasp an object in around 49.1s using our approach. These promising results suggest that the Morse code-inspired method could be used in the design of BCIs for multi-DOF control.
National University of Defense Technology, 410073 Changsha, Hunan, People's Republic of China. Electronic address: jiangjun@nudt.edu.cn.
Jun
J
Jiang
National University of Defense Technology, 410073 Changsha, Hunan, People's Republic of China.
Zongtan
Z
Zhou
China National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, 100094 Beijing, People's Republic of China.
Erwei
E
Yin
National University of Defense Technology, 410073 Changsha, Hunan, People's Republic of China.
Yang
Y
Yu
National University of Defense Technology, 410073 Changsha, Hunan, People's Republic of China.
Yadong
Y
Liu
National University of Defense Technology, 410073 Changsha, Hunan, People's Republic of China.
Dewen
D
Hu
D000328:Adult; D000465:Algorithms; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D005246:Feedback; D005260:Female; D064368:Healthy Volunteers; D006801:Humans; D019018:Imagery, Psychotherapy; D008297:Male; D009048:Motor Skills; D012372:ROC Curve; D015203:Reproducibility of Results; D012371:Robotics; D012815:Signal Processing, Computer-Assisted; D012984:Software; D055815:Young Adult
Brain–computer interface (BCI); Electroencephalogram (EEG); Morse code; Multi-DOF robot arm control; Sequential motor imagery