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Abstract:
Statistical learning and probabilistic inference techniques
are used to infer the hand position of a subject from
multi-electrode recordings of neural activity in motor cortex.
First, an array of electrodes provides training data of neural
firing conditioned on hand kimenatics. We learn a nonparametric
representation of this firing activity using a Bayesian model and
rigourously compare it with previous models using
cross-validation. Second, we infer a posterior probability
distribution over hand motion conditioned on a sequence of neural
test data data using Bayesian inference. The learned firing
models of multiple cells are used to define a non-Gaussian
likelihood term which is combined with a prior probability for
the kinematics. A particle filtering method is used to represent,
update, and propogate the posterior distribution over time. The
approach is compared with traditional linear filtering methods;
the results suggest that it may be appropriate for neural
prosthetic applications.
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