Modeling and decoding motor cortical activity using a switching Kalman filter

@article{Wu2004ModelingAD,
  title={Modeling and decoding motor cortical activity using a switching Kalman filter},
  author={Wei Wu and Michael J. Black and David Mumford and Yun Gao and Elie Bienenstock and John P. Donoghue},
  journal={IEEE Transactions on Biomedical Engineering},
  year={2004},
  volume={51},
  pages={933-942}
}
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… CONTINUE READING
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References

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Switching Kalman Filters

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Motor cortical representation of speed and direction during reaching.

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