The Brain-Inspired Cooperative Shared Control Framework for Brain-Machine Interface

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 c...

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Bibliographic Details
Published inarXiv.org
Main Authors Yang, Junjie, Liu, Ling, Zheng, Shengjie, Lang, Qian, Gao, Gang, Chen, Xin, Li, Xiaojian
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 12.10.2024
Subjects
Brain
Control methods
Cooperative control
Decoding
Interfaces
Man-machine interfaces
Neural networks
Robot arms
Robot control
Robotics
Speed control
Steering
Task complexity
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Summary: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.
ISSN:2331-8422