Control of a 7-DOF Robotic Arm System With an SSVEP-Based BCI

International Journal of Neural Systems
Xiaogang ChenXiaorong Gao

Abstract

Although robot technology has been successfully used to empower people who suffer from motor disabilities to increase their interaction with their physical environment, it remains a challenge for individuals with severe motor impairment, who do not have the motor control ability to move robots or prosthetic devices by manual control. In this study, to mitigate this issue, a noninvasive brain-computer interface (BCI)-based robotic arm control system using gaze based steady-state visual evoked potential (SSVEP) was designed and implemented using a portable wireless electroencephalogram (EEG) system. A 15-target SSVEP-based BCI using a filter bank canonical correlation analysis (FBCCA) method allowed users to directly control the robotic arm without system calibration. The online results from 12 healthy subjects indicated that a command for the proposed brain-controlled robot system could be selected from 15 possible choices in 4[Formula: see text]s (i.e. 2[Formula: see text]s for visual stimulation and 2[Formula: see text]s for gaze shifting) with an average accuracy of 92.78%, resulting in a 15 commands/min transfer rate. Furthermore, all subjects (even naive users) were able to successfully complete the entire move-grasp-lift t...Continue Reading

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Citations

Oct 16, 2019·Journal of Neural Engineering·Yufeng KeDong Ming
Jan 17, 2019·International Journal of Neural Systems·Yurong LiYuan Yang
Dec 14, 2018·Journal of Neural Engineering·Xiaogang ChenXiaorong Gao
Dec 18, 2020·Frontiers in Neurorobotics·Yuanlu ZhuPengcheng Li
Dec 24, 2020·International Journal of Neural Systems·Pasquale ArpaiaMarco Parvis
Apr 7, 2020·Computers in Biology and Medicine·Xiaogang ChenXiaorong Gao
Nov 5, 2021·Journal of Neural Engineering·Muhammad Ahmed KhanSadasivan Puthusserypady
Oct 26, 2021·International Journal of Neural Systems·Naishi FengBin Zhou

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