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Bayesian Population Decoding of Motor Cortical Activity Using a Kalman Filter (2005)  (Make Corrections)  
Yun Gao, Elie Bienenstock, John P. Donoghue, Michael J. Black



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Abstract: Effective neural motor prostheses require a method for decoding neural activity representing desired movement. In particular, the accurate reconstruction of a continuous motion signal is necessary for the control of devices such as computer cursors, robots, or a patient's own paralyzed limbs. For such applications we developed a real-time system that uses Bayesian inference techniques to estimate hand motion from the firing rates of multiple neurons. In this study, we used recordings that were... (Update)

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BibTeX entry:   (Update)

@misc{ gao-bayesian,
  author = "Yun Gao and Elie Bienenstock and John P. Donoghue and Michael J. Black",
  title = "Bayesian Population Decoding of Motor Cortical Activity Using a Kalman
    Filter",
  url = "citeseer.ist.psu.edu/gao05bayesian.html" }
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