  In the practical application of brain-machine interface technology, the
problem often faced is the low information content and high noise of the neural
signals collected by the electrode and the difficulty of decoding by the
decoder, which makes it difficult for the robotic to obtain stable instructions
to complete the task. The idea based on the principle of cooperative shared
control can be achieved by extracting general motor commands from brain
activity, while the fine details of the movement can be hosted to the robot for
completion, or the brain can have complete control. This study proposes a
brain-machine interface shared control system based on spiking neural networks
for robotic arm movement control and wheeled robots wheel speed control and
steering, respectively. The former can reliably control the robotic arm to move
to the destination position, while the latter controls the wheeled robots for
object tracking and map generation. The results show that the shared control
based on brain-inspired intelligence can perform some typical tasks in complex
environments and positively improve the fluency and ease of use of
brain-machine interaction, and also demonstrate the potential of this control
method in clinical applications of brain-machine interfaces.
