Simultaneus prediction of four kinematic variables for a brain-machine interface using a single recurrent neural network
Sanchez, J.C.; Principe, J.C.; Carmena, J.M.; Lebedev, M.A.; Nicolelis, M.A.L.
Engineering in Medicine and Biology Society, 2004. IEMBS apos;04. 26th Annual International Conference of the IEEE
Volume 2, Issue , 1-5 Sept. 2004 Page(s):5321 - 5324
Digital Object Identifier   10.1109/IEMBS.2004.1404486
Summary: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.

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