Modeling and decoding motor cortical activity using a switching Kalman filter

Author

Wu, Wei ; Black, Michael J. ; Mumford, David ; Gao, Yun ; Bienenstock, Elie ; Donoghue, John P.

Author_Institution

Dept. of Comput. Sci., Brown Univ., Providence, RI, USA

Volume

51

Issue

6

fYear

2004

fDate

6/1/2004 12:00:00 AM

Firstpage

933

Lastpage

942

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.

Keywords

Kalman filters; Markov processes; bioelectric potentials; biomechanics; decoding; neurophysiology; prosthetics; switched filters; Gaussian mixture; Markov chain; crudely sorted neural data; decoding methods; encoding methods; firing rates; hand kinematics; motor cortical activity; motor cortical neurons; on-line prosthetic applications; real-time inference; switching Kalman filter model; Brain modeling; Decoding; Electrodes; Encoding; Kinematics; Mathematics; Neural prosthesis; Neuroscience; Position measurement; Prosthetics; Action Potentials; Algorithms; Animals; Computer Simulation; Electroencephalography; Hand; Likelihood Functions; Macaca; Models, Neurological; Models, Statistical; Motor Neurons; Movement; Nerve Net; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Stochastic Processes;

fLanguage

English

Journal_Title

Biomedical Engineering, IEEE Transactions on

Publisher

ieee

ISSN

0018-9294

Type

jour

DOI

10.1109/TBME.2004.826666

Filename

1300785