Neural control of motor prostheses

Abstract

Neural interfaces (NIs) for motor control have recently become increasingly advanced. This has been possible owing to substantial progress in our understanding of the cortical motor system as well as the development of appropriate decoding methods in both non-human primates and paralyzed patients. So far, neural interfaces have controlled mainly computer screens and robotic arms. An important advancement has been the demonstration of neural interfaces that can directly control the subject's muscles. Furthermore, it has been shown that cortical plasticity alone can optimize neural interface performance in the absence of machine learning, which emphasizes the role of the brain for neural interface adaptation. Future motor prostheses may use also sensory feedback to enhance their control capabilities.