Papers Published

1. Sanchez, J.C. and Erdogmus, D. and Nicolelis, M.A.L. and Wessberg, J. and Principe, J.C., Interpreting spatial and temporal neural activity through a recurrent neural network brain-machine interface, IEEE Trans. Neural Syst. Rehabil. Eng. (USA), vol. 13 no. 2 (2005), pp. 213 - 19 [TNSRE.2005.847382] .
   (last updated on 2007/04/15)

   Abstract:
   We propose the use of optimized brain-machine interface (BMI) models for interpreting the spatial and temporal neural activity generated in motor tasks. In this study, a nonlinear dynamical neural network is trained to predict the hand position of primates from neural recordings in a reaching task paradigm. We first develop a method to reveal the role attributed by the model to the sampled motor, premotor, and parietal cortices in generating hand movements. Next, using the trained model weights, we derive a temporal sensitivity measure to asses how the model utilized the sampled cortices and neurons in real-time during BMI testing

   Keywords:
   bioelectric phenomena;biomechanics;brain;handicapped aids;medical signal processing;neurophysiology;nonlinear dynamical systems;physiological models;recurrent neural nets;spatiotemporal phenomena;