By Topic

Using nonlinear dynamic metric tools for characterizing brain structures

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Blanco, S. ; Buenos Aires Univ., Argentina ; Figliola, A. ; Kochen, S. ; Rosso, O.A.

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.

Published in:

Engineering in Medicine and Biology Magazine, IEEE  (Volume:16 ,  Issue: 4 )