• IEEE Trans Biomed Eng · Jun 2004

    Comparative Study

    Modeling and decoding motor cortical activity using a switching Kalman filter.

    • Wei Wu, Michael J Black, David Mumford, Yun Gao, Elie Bienenstock, and John P Donoghue.
    • Division of Applied Mathematics, Brown University, Providence, RI 02912, USA. weiwu@dam.brown.edu
    • IEEE Trans Biomed Eng. 2004 Jun 1; 51 (6): 933-42.

    AbstractWe 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.

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