A Hybrid Brain-Computer Interface for Closed-Loop Position Control of a Robot Arm
文献类型：期刊论文
| 作者 | Arnab Rakshit; Amit Konar; Atulya K. Nagar |
| 刊名 | IEEE/CAA Journal of Automatica Sinica
 |
| 出版日期 | 2020 |
| 卷号 | 7期号:5页码:1344-1360 |
| 关键词 | Brain-computer interfacing (BCI) electroencephalography (EEG) Jaco robot arm motor imagery P300 steady-state visually evoked potential (SSVEP) |
| ISSN号 | 2329-9266 |
| DOI | 10.1109/JAS.2020.1003336 |
| 英文摘要 | Brain-Computer interfacing (BCI) has currently added a new dimension in assistive robotics. Existing brain-computer interfaces designed for position control applications suffer from two fundamental limitations. First, most of the existing schemes employ open-loop control, and thus are unable to track positional errors, resulting in failures in taking necessary online corrective actions. There are examples of a few works dealing with closed-loop electroencephalography (EEG)-based position control. These existing closed-loop brain-induced position control schemes employ a fixed order link selection rule, which often creates a bottleneck preventing time-efficient control. Second, the existing brain-induced position controllers are designed to generate a position response like a traditional first-order system, resulting in a large steady-state error. This paper overcomes the above two limitations by keeping provisions for steady-state visual evoked potential (SSVEP) induced link-selection in an arbitrary order as required for efficient control and generating a second-order response of the position-control system with gradually diminishing overshoots/undershoots to reduce steady-state errors. Other than the above, the third innovation is to utilize motor imagery and P300 signals to design the hybrid brain-computer interfacing system for the said application with gradually diminishing error-margin using speed reversal at the zero-crossings of positional errors. Experiments undertaken reveal that the steady-state error is reduced to 0.2%. The paper also provides a thorough analysis of the stability of the closed-loop system performance using the Root Locus technique. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/43038]  |
| 专题 | 自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica |
| 推荐引用方式 GB/T 7714 | Arnab Rakshit,Amit Konar,Atulya K. Nagar. A Hybrid Brain-Computer Interface for Closed-Loop Position Control of a Robot Arm[J]. IEEE/CAA Journal of Automatica Sinica,2020,7(5):1344-1360. |
| APA | Arnab Rakshit,Amit Konar,&Atulya K. Nagar.(2020).A Hybrid Brain-Computer Interface for Closed-Loop Position Control of a Robot Arm.IEEE/CAA Journal of Automatica Sinica,7(5),1344-1360. |
| MLA | Arnab Rakshit,et al."A Hybrid Brain-Computer Interface for Closed-Loop Position Control of a Robot Arm".IEEE/CAA Journal of Automatica Sinica 7.5(2020):1344-1360. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。