State-Space Decoding of Goal-Directed Movements
IEEE Signal Processing 25, 2008, 78-86 (Special issue on
brain-computer interfaces.)
Bayesian inference methods hold great promise for the prediction of
hand-movement trajectories in neural prosthetic devices. The accuracy
of such probabilistic methods can be improved by incorporating
meaningful priors, thereby appropriately constraining the space of
possible states that the system can attain. In this work we review and
extend methods for constructing reach trajectories that incorporate
prior information of the intended movement target. For computational
tractability, we model arm motion as a linear dynamical system driven
by Gaussian noise, conditioned on this end-point information. These
assumptions, while biomechanically unrealistic, give rise to a priori
model arm-paths that share many of the characteristics of natural arm
trajectories. Moreover, in this model formulation we may compute the
predicted arm position, given simultaneously observed neural data,
using standard forward-backward computations familiar from the theory
of the Kalman filter. Here we review an earlier recursive approach for
computing such reach trajectories and present a new nonrecursive
approach, with computations that may be performed analytically for the
most part, leading to a significant gain in the accuracy of the
inferred trajectory while imposing a very small computational
burden. Finally, we discuss extensions of our approach, including the
incorporation of multiple target observations at different times, and
multiple possible target locations.
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on motor cortical decoding | Liam Paninski's research page