Simultaneus prediction of four kinematic variables for a brain-machine interface using a single recurrent neural network.
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.
Sanchez
J C
JC
Department of Biomedical Engineering, Florida University, Gainesville, FL, USA.
Principe
J C
JC
Carmena
J M
JM
Lebedev
Mikhail A
MA
Nicolelis
M A L
MA