In our previous work a shoulder-elbow rehabilitation robot was developed and applied for the rehabilitation of chronic stroke patients. The goal of this study is to integrate an EEG-based brain-computer-interface (BCI) and the rehabilitation robot to build a system by which patients can use imagery movement of arm to control the robot to assist forward reach movement of the arm. Two personal computers were employed, one for EEG processing and the other for controlling the robot. An optimal filter was realized to reduce noise in EEG and the algorithms to translate mu waves of C3 and C4 of human brain into robot command were proposed. Eight healthy and five chronic stroke subjects were recruited to test functions of the system. Two indices, namely accuracy and trigger time were utilized to evaluate performance of all subjects. The training lasted for eight weeks with two days per week. The results show the healthy subjects had no side difference on weekly accuracies. However, for the affected arm of stroke patients accuracy of the fourth week is significantly higher than that of the first week. Trigger time of the intact arm of the stroke group at eighth week is smaller than that of third week. The separation of EEG processing and robot command generation does improve quality of EEG and the EEG controlled rehabilitation robot might be used in future neuro-rehabilitation of patients.
|Translated title of the contribution||Development of EEG Brain-Computer Interface System for Control of Shoulder-Elbow Rehabilitation Robot|
|Number of pages||9|
|Journal||Journal of the Chinese Society of Mechanical Engineers, Transactions of the Chinese Institute of Engineers, Series C/Chung-Kuo Chi Hsueh Kung Ch'eng Hsuebo Pao|
|Publication status||Published - 2020 Feb 1|
All Science Journal Classification (ASJC) codes
- Mechanical Engineering