Using nonlinear dynamic metric tools for characterizing brain structures.

Abstract

The collective dynamic behavior of the neural mass of different brain structures can be assessed from electroencephalographic recordings with depth electrodes measurements at regular time intervals (EEG time series). In recent years, the cheery of nonlinear dynamics has developed methods for quantitative analysis of experimental time series. The aim of this article is to report a new attempt to characterize global brain dynamics through electrical activity using these nonlinear dynamical metric tools. In addition, the authors study the dependence of the metric magnitudes on brain structure. The methods employed in this work are independent of any modeling of brain activity. They rely solely on the analysis of data obtained from a single variable time series. The authors analyze the EEG signals from depth electrodes that intersect different brain anatomical structures in a patient with refractory epilepsy prone to surgical treatment. The electrical signal provided by this type of electrode guarantees a low noise signal.