Modeling and decoding motor cortical activity using a switching Kalman filter
2004
Article
ps
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.
Author(s): | Wu, W. and Black, M. J. and Mumford, D. and Gao, Y. and Bienenstock, E. and Donoghue, J. P. |
Journal: | IEEE Trans. Biomedical Engineering |
Volume: | 51 |
Number (issue): | 6 |
Pages: | 933--942 |
Year: | 2004 |
Month: | June |
Department(s): | Perceiving Systems |
Bibtex Type: | Article (article) |
Paper Type: | Journal |
Links: |
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BibTex @article{Wu:TransBME:04, title = {Modeling and decoding motor cortical activity using a switching {Kalman} filter}, author = {Wu, W. and Black, M. J. and Mumford, D. and Gao, Y. and Bienenstock, E. and Donoghue, J. P.}, journal = {IEEE Trans. Biomedical Engineering}, volume = {51}, number = {6}, pages = {933--942}, month = jun, year = {2004}, doi = {}, month_numeric = {6} } |