A Hybrid Brain-Computer Interface for Closed-Loop Position Control of a Robot Arm

作     者：Arnab Rakshit Amit Konar Atulya K.Nagar Arnab Rakshit;Amit Konar;Atulya K. Nagar

作者机构：Artificial Intelligence Lab.Department of Electronics&Tele-Communication EngineeringJadavpur UniversityKolkata 700032India Department of MathematicsComputer Scienceand EngineeringLiverpool Hope UniversityLiverpool L169JDUnited Kingdom IEEE 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2020年第7卷第5期

页      面：1344-1360页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0711[理学-系统科学] 07[理学] 08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0836[工学-生物工程] 

基　　金：Jadavpur University, JU Liverpool Hope University UK Research and Innovation, UKRI Council of Scientific and Industrial Research, India, CSIR 

主　　题：Brain-computer interfacing(BCI) electroencepha-lography(EEG) Jaco robot arm motor imagery P300 steady-state visually evoked potential(SSVEP) 

摘      要：Brain-Computer interfacing(BCI)has currently added a new dimension in assistive *** braincomputer interfaces designed for position control applications suffer from two fundamental ***,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 *** are examples of a few works dealing with closed-loop electroencephalography(EEG)-based position *** existing closed-loop brain-induced position control schemes employ a fixed order link selection rule,which often creates a bottleneck preventing time-efficient ***,the existing brain-induced position controllers are designed to generate a position response like a traditional firstorder system,resulting in a large steady-state *** paper overcomes the above two limitations by keeping provisions for steady-state visual evoked potential(SSVEP)induced linkselection 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 *** 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 *** 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.