Modeling and decoding motor cortical activity using a switching Kalman filter

IEEE Trans Biomed Eng. 2004 Jun;51(6):933-42. doi: 10.1109/TBME.2004.826666.

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.

Publication types

  • Comparative Study
  • Evaluation Study
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Action Potentials / physiology
  • Algorithms
  • Animals
  • Computer Simulation
  • Electroencephalography / methods*
  • Hand / physiology*
  • Likelihood Functions
  • Macaca
  • Models, Neurological*
  • Models, Statistical
  • Motor Neurons / physiology*
  • Movement / physiology*
  • Nerve Net / physiology*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Signal Processing, Computer-Assisted
  • Stochastic Processes