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In the practical application of brain-machine interface technology, theproblem often faced is the low information content and high noise of the neuralsignals collected by the electrode and the difficulty of decoding by thedecoder, which makes it difficult for the robotic to obtain stable instructionsto complete the task. The idea based on the principle of cooperative sharedcontrol can be achieved by extracting general motor commands from brainactivity, while the fine details of the movement can be hosted to the robot forcompletion, or the brain can have complete control. This study proposes abrain-machine interface shared control system based on spiking neural networksfor robotic arm movement control and wheeled robots wheel speed control andsteering, respectively. The former can reliably control the robotic arm to moveto the destination position, while the latter controls the wheeled robots forobject tracking and map generation. The results show that the shared controlbased on brain-inspired intelligence can perform some typical tasks in complexenvironments and positively improve the fluency and ease of use ofbrain-machine interaction, and also demonstrate the potential of this controlmethod in clinical applications of brain-machine interfaces.

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