Metrics

Views
585

In This Chapter

Advanced Topics for Modeling Electroencephalograms

Authored by: Hernando Ombao , Anna Louise Schröder , Carolina Euán , Chee-Ming Ting , Balqis Samdin

Handbook of Neuroimaging Data Analysis

Print publication date:  November  2016
Online publication date:  November  2016

Print ISBN: 9781482220971
eBook ISBN: 9781315373652
Adobe ISBN:

10.1201/9781315373652-22

 Download Chapter

 

Abstract

In this chapter, we consider a number of approaches for modeling and analyzing multichannel electroencephalograms when they exhibit “non-stationary” behavior. An example are the traces in Figure 21.1 which were recorded around an epileptic seizure episode. One observes an increase in the variance or wave amplitudes at seizure onset. This epileptic seizure recording captures brain activity of a subject who suffered a spontaneous epileptic seizure while being connected to the EEG. The recording is digitized at 100 Hz and about 500 seconds long, providing us with a time series of length T = 50000. Data is collected at 21 channels, 19 bipolar scalp electrodes placed according to the 10-20 system (see Figure 21.2), and two sphenoidal electrodes placed intracranially at the base of the temporal lobe. The data has been previously analyzed, for example in (57), where the primary focus was on obtaining the optimal representation from a family of models defined by the smooth complex exponential (SLEX) library (see Section 21.5). Seizure EEGs are realizations of a rapidly changing electrophysiological process resulting from abnormal firing behavior of a network of neuronal subpopulations. These aberrations in neuronal electrical activity are expressed by fluctuating amplitudes of waveforms, changing spectral decompositions and evolving cross-dependence between channels. A complete analysis of this type of a brain signal requires a variety of advanced tools which the authors have developed. We discuss recently developed methods that uncover many characteristics of this signal that could help us better understand not only the evolution of the seizure process but also potential changes in neurophysiology that precede seizure onset.

 Cite
Search for more...
Back to top

Use of cookies on this website

We are using cookies to provide statistics that help us give you the best experience of our site. You can find out more in our Privacy Policy. By continuing to use the site you are agreeing to our use of cookies.