Papers Published

1. Sanchez, J.C. and Sung-Phil Kim and Erdogmus, D. and Rao, Y.N. and Principe, J.C. and Wessberg, J. and Nicolelis, M., Input-output mapping performance of linear and nonlinear models for estimating hand trajectories from cortical neuronal firing patterns, Neural Networks for Signal Processing XII. Proceedings of the 2002 IEEE Signal Processing Society Workshop. (Cat. No.02TH8641) (2002), pp. 139 - 48 [NNSP.2002.1030025] .
   (last updated on 2007/04/15)

   Abstract:
   Linear and nonlinear (TDNN) models have been shown to estimate hand position using populations of action potentials collected in the pre-motor and motor cortical areas of a primate's brain. One of the applications of this discovery is to restore movement in patients suffering from paralysis. For real-time implementation of this technology, reliable and accurate signal processing models that produce small error variance in the estimated positions are required. In this paper, we compare the mapping performance of the FIR filter, gamma filter and recurrent neural network (RNN) in the peaks of reaching movements. Each approach has strengths and weaknesses that are compared experimentally. The RNN approach shows very accurate peak position estimations with small error variance

   Keywords:
   brain models;FIR filters;handicapped aids;medical signal processing;patient treatment;position control;real-time systems;recurrent neural nets;