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Simultaneus prediction of four kinematic variables for a brain-machine interface using a single recurrent neural network

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5 Author(s)
Sanchez, J.C. ; Dept. of Biomedical Eng., Florida Univ., Gainesville, FL, USA ; Principe, J.C. ; Carmena, J.M. ; Lebedev, M.A.
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Implementation of brain-machine interface neural-to-motor mapping algorithms in low-power, portable digital signal processors (DSPs) requires efficient use of model resources especially when predicting signals that show interdependencies. We show here that a single recurrent neural network can simultaneously predict hand position and velocity from the same ensemble of cells using a minimalist topology. Analysis of the trained topology showed that the model learns to concurrently represent multiple kinematic parameters in a single state variable. We further assess the expressive power of the state variables for both large and small topologies.

Published in:

Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE  (Volume:2 )

Date of Conference:

1-5 Sept. 2004

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