Optimal estimation of feed-forward-controlled linear systems

The neuroprosthetic interface must infer an intended movement from the neural activity that would accompany it in healthy individuals. We show that an optimal estimator for a controlled system such as that responsible for human movements jointly estimates the goal and the trajectory of point-to-point movements. We demonstrate that this paradigm can achieve orders of magnitude of increased accuracy in regimes in which the interface has low SNR. With high SNR, our technique proves reliably more accurate than a typical approach which ignores the controlled nature of the system under observation. Furthermore, we show that even when the system violates the model assumptions of feedforward linear control with additive noise, system performance remains appreciably better than the alternative.