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

TL;DR

This paper introduces a brain-inspired cooperative shared control framework for brain-machine interfaces that improves robotic control by combining neural decoding with robotic fine control, enhancing BMI performance and clinical potential.

Contribution

It presents a novel cooperative control framework based on brain-inspired algorithms using spiking neural networks for improved BMI robotic control.

Findings

Successfully implemented modules for robotic arm control, object tracking, and map generation.

Demonstrated potential for enhanced BMI performance in practical applications.

Framework facilitates flexible human-robot collaboration.

Abstract

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.