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Volume 7 Issue 5
Sep.  2020

IEEE/CAA Journal of Automatica Sinica

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Arnab Rakshit, Amit Konar and Atulya K. Nagar, "A Hybrid Brain-Computer Interface for Closed-Loop Position Control of a Robot Arm," IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1344-1360, Sept. 2020. doi: 10.1109/JAS.2020.1003336
Citation: Arnab Rakshit, Amit Konar and Atulya K. Nagar, "A Hybrid Brain-Computer Interface for Closed-Loop Position Control of a Robot Arm," IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1344-1360, Sept. 2020. doi: 10.1109/JAS.2020.1003336

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

doi: 10.1109/JAS.2020.1003336
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  • 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.

     

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    Highlights

    • A novel scheme of Brain-comanded position-control of a robot arm in a 3-dimensional environment is proposed.
    • The proposed scheme uses SSVEP for random link selection, MI for movement initiation, and P300 to turn back the link on crossing the target positions.
    • Positional overshoot and steady-state error are reduced by an exponential decrease in speed and reversal of turning of the motors as the target position is crossed.
    • The fundamental contribution includes a non-traditional approach to computing transfer function of the position controllerand its parameter setting to attain stability by Root Locus analysis.

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