AUTHOR=Lainscsek Claudia , Hernandez Manuel E., Weyhenmeyer Jonathan , Sejnowski Terrence J., Poizner Howard TITLE=Non-Linear Dynamical Analysis of EEG Time Series Distinguishes Patients with Parkinson’s Disease from Healthy Individuals JOURNAL=Frontiers in Neurology VOLUME=4 YEAR=2013 URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2013.00200 DOI=10.3389/fneur.2013.00200 ISSN=1664-2295 ABSTRACT=
The pathophysiology of Parkinson’s disease (PD) is known to involve altered patterns of neuronal firing and synchronization in cortical-basal ganglia circuits. One window into the nature of the aberrant temporal dynamics in the cerebral cortex of PD patients can come from analysis of the patients electroencephalography (EEG). Rather than using spectral-based methods, we used data models based on delay differential equations (DDE) as non-linear time-domain classification tools to analyze EEG recordings from PD patients on and off dopaminergic therapy and healthy individuals. Two sets of 50 1-s segments of 64-channel EEG activity were recorded from nine PD patients on and off medication and nine age-matched controls. The 64 EEG channels were grouped into 10 clusters covering frontal, central, parietal, and occipital brain regions for analysis. DDE models were fitted to individual trials, and model coefficients and error were used as features for classification. The best models were selected using repeated random sub-sampling validation and classification performance was measured using the area under the ROC curve