Robotic arm control system based on augmented reality brain-computer interface and computer vision.
10.7507/1001-5515.202011039
- Author:
Xiaogang CHEN
1
;
Kun LI
2
Author Information
1. Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, P.R.China.
2. Medical Equipment Management Division of 3201 Hospital, Hanzhong, Shaanxi 723000, P.R.China.
- Publication Type:Journal Article
- Keywords:
augmented reality;
brain-computer interface;
computer vision;
robotic arm;
steady-state visual evoked potential
- MeSH:
Augmented Reality;
Brain-Computer Interfaces;
Computers;
Electroencephalography;
Evoked Potentials, Visual;
Humans;
Photic Stimulation;
Robotic Surgical Procedures
- From:
Journal of Biomedical Engineering
2021;38(3):483-491
- CountryChina
- Language:Chinese
-
Abstract:
Brain-computer interface (BCI) has great potential to replace lost upper limb function. Thus, there has been great interest in the development of BCI-controlled robotic arm. However, few studies have attempted to use noninvasive electroencephalography (EEG)-based BCI to achieve high-level control of a robotic arm. In this paper, a high-level control architecture combining augmented reality (AR) BCI and computer vision was designed to control a robotic arm for performing a pick and place task. A steady-state visual evoked potential (SSVEP)-based BCI paradigm was adopted to realize the BCI system. Microsoft's HoloLens was used to build an AR environment and served as the visual stimulator for eliciting SSVEPs. The proposed AR-BCI was used to select the objects that need to be operated by the robotic arm. The computer vision was responsible for providing the location, color and shape information of the objects. According to the outputs of the AR-BCI and computer vision, the robotic arm could autonomously pick the object and place it to specific location. Online results of 11 healthy subjects showed that the average classification accuracy of the proposed system was 91.41%. These results verified the feasibility of combing AR, BCI and computer vision to control a robotic arm, and are expected to provide new ideas for innovative robotic arm control approaches.
