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

IEEE/CAA Journal of Automatica Sinica 2020年第5期7卷 1344-1360页

作者： Arnab Rakshit Amit Konar Atulya K.nagar Artificial Intelligence Lab. Department of Electronics&Tele-Communication EngineeringJadavpur UniversityKolkata 700032India Department of Mathematics Computer Scienceand EngineeringLiverpool Hope UniversityLiverpool L169JDUnited Kingdom IEEE

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.

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

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