Modeling and decoding motor cortical activity using a switching Kalman filter.
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.
Wu
Wei
W
Division of Applied Mathematics, Brown University, Providence, RI 02912, USA. weiwu@dam.brown.edu
Black
Michael J
MJ
Mumford
David
D
Gao
Yun
Y
Bienenstock
Elie
E
Donoghue
John P
JP
D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D006225:Hand / Q000502:physiology*; D016013:Likelihood Functions; D008251:Macaca; D008959:Models, Neurological; D015233:Models, Statistical; D009046:Motor Neurons / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes