Modeling and decoding motor cortical activity using a switching Kalman filter
Wei Wu; Black, M.J.; Mumford, D.; Yun Gao; Bienenstock, E.; Donoghue, J.P.
Biomedical Engineering, IEEE Transactions on
Volume 51, Issue 6, June 2004 Page(s):933 - 942
Digital Object Identifier   10.1109/TBME.2004.826666
Summary:We present a switching Kalman filter model for the real-time inference of hand kinematics from a population of motor cortical neurons. Firing rates are modeled as a Gaussian mixture where the mean of each Gaussian component is a linear function of hand kinematics. A "hidden state" models the probability of each mixture component and evolves over time in a Markov chain. The model generalizes previous encoding and decoding methods, addresses the non-Gaussian nature of firing rates, and can cope with crudely sorted neural data common in on-line prosthetic applications.

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