Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 580795, 5 pages
Research Article

Multiscale Autoregressive Identification of Neuroelectrophysiological Systems

1Electrical Engineering Department, Pennsylvania State University, University Park, PA 16802, USA
2Neurology Department, Penn State Hershey Medical Center, 500 University Drive, Hershey, PA 17033, USA

Received 8 September 2011; Revised 1 December 2011; Accepted 2 December 2011

Academic Editor: Henggui Zhang

Copyright © 2012 Timothy P. Gilmour et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Electrical signals between connected neural nuclei are difficult to model because of the complexity and high number of paths within the brain. Simple parametric models are therefore often used. A multiscale version of the autoregressive with exogenous input (MS-ARX) model has recently been developed which allows selection of the optimal amount of filtering and decimation depending on the signal-to-noise ratio and degree of predictability. In this paper, we apply the MS-ARX model to cortical electroencephalograms and subthalamic local field potentials simultaneously recorded from anesthetized rodent brains. We demonstrate that the MS-ARX model produces better predictions than traditional ARX modeling. We also adapt the MS-ARX results to show differences in internuclei predictability between normal rats and rats with 6OHDA-induced parkinsonism, indicating that this method may have broad applicability to other neuroelectrophysiological studies.