Closed-loop hybrid Gaze Brain-machine interface based robotic arm control with augmented reality feedback

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Abstract

Copyright © 2017 Zeng, Wang, Wu, Song, Liu, Ji, Xu, Zhu, Li and Wen. Brain-machine interface (BMI) can be used to control the robotic arm to assist paralysis people for performing activities of daily living. However, it is still a complex task for the BMI users to control the process of objects grasping and lifting with the robotic arm. It is hard to achieve high efficiency and accuracy even after extensive trainings. One important reason is lacking of sufficient feedback information for the user to perform the closed-loop control. In this study, we proposed a method of augmented reality (AR) guiding assistance to provide the enhanced visual feedback to the user for a closed-loop control with a hybrid Gaze-BMI, which combines the electroencephalography (EEG) signals based BMI and the eye tracking for an intuitive and effective control of the robotic arm. Experiments for the objects manipulation tasks while avoiding the obstacle in the workspace are designed to evaluate the performance of our method for controlling the robotic arm. According to the experimental results obtained from eight subjects, the advantages of the proposed closedloop system (with AR feedback) over the open-loop system (with visual inspection only) have been verified. The number of trigger commands used for controlling the robotic arm to grasp and lift the objects with AR feedback has reduced significantly and the height gaps of the gripper in the lifting process have decreased more than 50% compared to those trials with normal visual inspection only. The results reveal that the hybrid Gaze-BMI user can benefit from the information provided by the AR interface, improving the efficiency and reducing the cognitive load during the grasping and lifting processes.

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APA

Zeng, H., Wang, Y., Wu, C., Song, A., Liu, J., Ji, P., … Wen, P. (2017). Closed-loop hybrid Gaze Brain-machine interface based robotic arm control with augmented reality feedback. Frontiers in Neurorobotics, 11(OCT). https://doi.org/10.3389/fnbot.2017.00060

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