Papers Published

1. Sanchez, J.C. and Principe, J.C. and Carmena, J.M. and Lebedev, M.A. and Nicolelis, M.A.L., Simultaneus prediction of four kinematic variables for a brain-machine interface using a single recurrent neural network, Conference Proceedings. 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.04CH37558), vol. Vol.7 (2004), pp. 5321 - 4 .
   (last updated on 2007/04/15)

   Abstract:
   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

   Keywords:
   biomechanics;brain;cellular biophysics;kinematics;medical signal processing;neurophysiology;prosthetics;recurrent neural nets;user interfaces;