Neuro-computational modelling of closed-loop prostheses control.

Despite advancements in myoelectric control of upper-limb prostheses, their use remains challenging and the rejection rates are still high. One step towards improving the human-prosthesis interfacing is to implement artificial sensory feedback as this can improve performance and user experience. While some late-generation prostheses incorporate supplementary feedback, there is still a lack of a principled method to investigate the impact of feedback and evaluate its effectiveness. In this study, we focused on how the subjects learn to operate an upper-limb prosthesis to control the grip force in a grasping task under visual control. We manipulated target grip force, and sensory and motor noise and investigated how these factors affected control performance. To interpret the empirical findings, we developed a computational model in which learning is described as the gradual acquisition of an internal representation of the prosthesis transfer function. The experimental data were collected in 20 non-disabled subjects and the results showed that the precision of control decreased with the increase in the target force and the level of motor noise, whereas the sensory noise did not have a significant impact. The model qualitatively reproduced the main experimental findings, in particular the dependence of performance on motor noise magnitude. The developed model is the first promising step towards capturing the behavior of prosthesis user and developing a more comprehensive understanding of the interaction between the different factors governing closed-loop prosthesis control.