Adaptive asynchronous control system of robotic arm based on augmented reality-assisted brain–computer interface. (12th November 2021)

• Record Type: Journal Article
• Authors: Chen, Lingling; Chen, Pengfei; Zhao, Shaokai; Luo, Zhiguo; Chen, Wei; Pei, Yu; Zhao, Hongyu; Jiang, Jing; Xu, Minpeng; Yan, Ye; Yin, Erwei
• Is Part Of: Journal of neural engineering. Volume 18:Number 6(2021)
• Publication Date: 2021-11-12
• Subjects: brain–computer interface -- robotic arm -- augmented reality -- dynamic window -- asynchronous control; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/ac3044
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective . Brain-controlled robotic arms have shown broad application prospects with the development of robotics, science and information decoding. However, disadvantages, such as poor flexibility restrict its wide application. Approach . In order to alleviate these drawbacks, this study proposed a robotic arm asynchronous control system based on steady-state visual evoked potential (SSVEP) in an augmented reality (AR) environment. In the AR environment, the participants were able to concurrently see the robot arm and visual stimulation interface through the AR device. Therefore, there was no need to switch attention frequently between the visual stimulation interface and the robotic arm. This study proposed a multi-template algorithm based on canonical correlation analysis and task-related component analysis to identify 12 targets. An optimization strategy based on dynamic window was adopted to adjust the duration of visual stimulation adaptively. Main results . Experimental results of this study found that the high-frequency SSVEP-based brain–computer interface (BCI) realized the switch of the system state, which controlled the robotic arm asynchronously. The average accuracy of the offline experiment was 94.97 %, whereas the average information translate rate was 67.37 ± 14.27 bits·min −1 . The online results from ten healthy subjects showed that the average selection time of a single online command was 2.04 s, which effectively reduced the visual fatigue of the subjects. Each subject could quickly complete the puzzle task. Significance . The experimental results demonstrated the feasibility and potential of this human-computer interaction strategy and provided new ideas for BCI-controlled robots.