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Volume 4 Issue 4
Oct.  2017

IEEE/CAA Journal of Automatica Sinica

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Saugat Bhattacharyya, Amit Konar and D.N. Tibarewala, "Motor Imagery and Error Related Potential Induced Position Control of a Robotic Arm," IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 639-650, Oct. 2017. doi: 10.1109/JAS.2017.7510616
Citation: Saugat Bhattacharyya, Amit Konar and D.N. Tibarewala, "Motor Imagery and Error Related Potential Induced Position Control of a Robotic Arm," IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 639-650, Oct. 2017. doi: 10.1109/JAS.2017.7510616

Motor Imagery and Error Related Potential Induced Position Control of a Robotic Arm

doi: 10.1109/JAS.2017.7510616
Funds:  This work was supported by UGC Sponsored UPE-Ⅱ Project in Cognitive Science of Jadavpur University, Kolkata
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  • The paper introduces an electroencephalography (EEG) driven online position control scheme for a robot arm by utilizing motor imagery to activate and error related potential (ErrP) to stop the movement of the individual links, following a fixed (pre-defined) order of link selection. The right (left) hand motor imagery is used to turn a link clockwise (counterclockwise) and foot imagery is used to move a link forward. The occurrence of ErrP here indicates that the link under motion crosses the visually fixed target position, which usually is a plane/line/point depending on the desired transition of the link across 3D planes/around 2D lines/along 2D lines respectively. The imagined task about individual link's movement is decoded by a classifier into three possible class labels:clockwise, counterclockwise and no movement in case of rotational movements and forward, backward and no movement in case of translational movements. One additional classifier is required to detect the occurrence of the ErrP signal, elicited due to visually inspired positional link error with reference to a geometrically selected target position. Wavelet coefficients and adaptive autoregressive parameters are extracted as features for motor imagery and ErrP signals respectively. Support vector machine classifiers are used to decode motor imagination and ErrP with high classification accuracy above 80%. The average time taken by the proposed scheme to decode and execute control intentions for the complete movement of three links of a robot is approximately 33 seconds. The steady-state error and peak overshoot of the proposed controller are experimentally obtained as 1.1% and 4.6% respectively.


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