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Title:	Modeling and Decoding Motor Cortical Activity Using a Switching Kalman Filter
Author:	Wu, Wei; Black, Michael J.; Mumford, David Bryant; Gao, Yun; Bienenstock, Elie; Donoghue, John P. Note: Order does not necessarily reflect citation order of authors.
Citation:	Wu, Wei, Michael J. Black, David Bryant Mumford, Yun Gao, Elie Bienenstock, and John P. Donoghue. 2004. Modeling and decoding motor cortical activity using a switching Kalman filter. IEEE Transactions on Biomedical Engineering 51(6): 933-942.
Full Text & Related Files:	Mumford_ModelCorticalKalman.pdf (473.6Kb; PDF)
Abstract:	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.
Published Version:	doi:10.1109/TBME.2004.826666
Other Sources:	http://www.dam.brown.edu/people/mumford/Papers/DigitizedVisionPapers--forNonCommercialUse/x04--CorticalKalman-WuBlack.pdf
Terms of Use:	This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page:	http://nrs.harvard.edu/urn-3:HUL.InstRepos:3637110

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