title: Modeling and decoding motor cortical activity using a switching Kalman filter.
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.
authors.affiliation: Division of Applied Mathematics, Brown University, Providence, RI 02912, USA. weiwu@dam.brown.edu
authors.forename: Wei
authors.initials: W
authors.lastname: Wu
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Black
authors.forename: David
authors.initials: D
authors.lastname: Mumford
authors.forename: Yun
authors.initials: Y
authors.lastname: Gao
authors.forename: Elie
authors.initials: E
authors.lastname: Bienenstock
authors.forename: John P
authors.initials: JP
authors.lastname: Donoghue
mesh_terms: 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