Modeling and Decoding Motor Cortical Activity Using a Switching Kalman Filter

DSpace/Manakin Repository

Modeling and Decoding Motor Cortical Activity Using a Switching Kalman Filter

Citable link to this page

 

 
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:
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
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)

 
 

Search DASH


Advanced Search
 
 

Submitters