Papers Published

1. Sanchez, JC; Erdogmus, D; Nicolelis, MAL; Wessberg, J; Principe, JC, Interpreting spatial and temporal neural activity through a recurrent neural network brain-machine interface., Ieee Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the Ieee Engineering in Medicine and Biology Society, vol. 13 no. 2 (June, 2005), pp. 213-219 [TNSRE.2005.847382], [doi] .
   (last updated on 2023/06/01)

   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;