Paper ID: 2210.09531 • Published Oct 18, 2022
The Brain-Inspired Cooperative Shared Control Framework for Brain-Machine Interface
Junjie Yang, Ling Liu, Shengjie Zheng, Lang Qian, Gang Gao, Xin Chen, Xiaojian Li
TL;DR
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In brain-machine interface (BMI) applications, a key challenge is the low
information content and high noise level in neural signals, severely affecting
stable robotic control. To address this challenge, we proposes a cooperative
shared control framework based on brain-inspired intelligence, where control
signals are decoded from neural activity, and the robot handles the fine
control. This allows for a combination of flexible and adaptive interaction
control between the robot and the brain, making intricate human-robot
collaboration feasible. The proposed framework utilizes spiking neural networks
(SNNs) for controlling robotic arm and wheel, including speed and steering.
While full integration of the system remains a future goal, individual modules
for robotic arm control, object tracking, and map generation have been
successfully implemented. The framework is expected to significantly enhance
the performance of BMI. In practical settings, the BMI with cooperative shared
control, utilizing a brain-inspired algorithm, will greatly enhance the
potential for clinical applications.