TY - GEN
T1 - Design and Optimization of a Real-Time Asynchronous BCI Control Strategy for Robotic Manipulator Assistance
AU - Saduanov, Batyrkhan
AU - Tokmurzina, Dana
AU - Kunanbayev, Kassymzhomart
AU - Abibullaev, Berdakh
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/2
Y1 - 2020/2
N2 - This paper presents the design and evaluation of a robotic system controlled by motor imagery based Brain-Computer Interface (BCI), which allows users to perform reach-grasp-release activities in 3D space. The sequential axis control methodology is used in the research to address a low information-transfer rate problem, where a participant has to provide several low-level commands to the robot, such as position on x, y, and z-axes, and close/release gripper actions. With the advantage of the motor imagery paradigm, the system is asynchronous, and the operator performs self-paced control. The BCI system is based on sensory-motor rhythms elicited by electroencephalographic (EEG) signals in the frequency domain to classify mental intent in real-time by a soft voting ensemble classifier model. The experimental results have shown the feasibility of controlling a six-degree-of-freedom robot manipulator with the classification performance in the range of 64-86% tested on seven healthy individuals.
AB - This paper presents the design and evaluation of a robotic system controlled by motor imagery based Brain-Computer Interface (BCI), which allows users to perform reach-grasp-release activities in 3D space. The sequential axis control methodology is used in the research to address a low information-transfer rate problem, where a participant has to provide several low-level commands to the robot, such as position on x, y, and z-axes, and close/release gripper actions. With the advantage of the motor imagery paradigm, the system is asynchronous, and the operator performs self-paced control. The BCI system is based on sensory-motor rhythms elicited by electroencephalographic (EEG) signals in the frequency domain to classify mental intent in real-time by a soft voting ensemble classifier model. The experimental results have shown the feasibility of controlling a six-degree-of-freedom robot manipulator with the classification performance in the range of 64-86% tested on seven healthy individuals.
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U2 - 10.1109/BCI48061.2020.9061608
DO - 10.1109/BCI48061.2020.9061608
M3 - Conference contribution
AN - SCOPUS:85084039385
T3 - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
BT - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
Y2 - 26 February 2020 through 28 February 2020
ER -