Wu, WeiBlack, Michael J.Mumford, DavidGao, YunBienenstock, ElieDonoghue, John P.2010-02-122004Wu, 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): 933-942.0018-9294http://nrs.harvard.edu/urn-3:HUL.InstRepos:3637110We 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.en-USmixture modelmotor cortexneural decodingneural prosthesisswitching Kalman filterJournal Article2010-02-1210.1109/TBME.2004.826666