Future neuroprosthetics will be tightly coupled with the user in such a way that the resulting system can replace and restore impaired upper limb functions because controlled by the same neural signals than their natural counterparts. However, robust and natural interaction of subjects with sophisticated prostheses over long periods of time remains a major challenge. To tackle this challenge we can get inspiration from natural motor control, where goal-directed behavior is dynamically modulated by perceptual feedback resulting from executed actions.
Current brain-machine interfaces (BMI) partly emulate human motor control as they decode cortical correlates of movement parameters –from onset of a movement to directions to instantaneous velocity– in order to generate the sequence of movements for the neuroprosthesis.
Read more here: https://eecs.berkeley.edu/research/colloquium/160907