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PubMed · 15188861

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

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

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Action PotentialsAlgorithmsAnimalsComputer SimulationElectroencephalographyHandLikelihood FunctionsMacacaModels, NeurologicalModels, StatisticalMotor NeuronsMovementNerve NetReproducibility of ResultsSensitivity and SpecificitySignal Processing, Computer-AssistedStochastic ProcessesComparative StudyEvaluation StudyJournal ArticleResearch Support, U.S. Gov't, Non-P.H.S.Research Support, U.S. Gov't, P.H.S.

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Wei Wu, Michael J Black, David Mumford, Yun Gao, Elie Bienenstock, John P Donoghue. 2004. Modeling and decoding motor cortical activity using a switching Kalman filter.. https://doi.org/10.1109/tbme.2004.826666

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