Brain dynamics and modeling in epilepsy: Prediction and control studies

Leonidas Iasemidis, Shivkumar Sabesan, Niranjan Chakravarthy, Awadhesh Prasad, Konstantinos Tsakalis

Research output: Chapter in Book/Report/Conference proceedingChapter

7 Scopus citations


Epilepsy is a major neurological disorder characterized by intermittent paroxysmal neuronal electrical activity, that may remain localized or spread, and severely disrupt the brain's normal operation. Epileptic seizures are typical manifestations of such pathology. It is in the last 20 years that prediction and control of epileptic seizures has been the subject of intensive interdisciplinary research. In this communication, we investigate epilepsy from the point of view of pathology of the dynamics of the electrical activity of the brain. In this framework, we revisit two critical aspects of the dynamics of epileptic seizures - the seizure predictability and seizure resetting - that may prove to be the keys for improved seizure prediction and seizure control schemes. We use human EEG data and the concepts of spatial synchronization of chaos, phase and energy to first show that seizures could be predictable in the order of tens of minutes prior to their onset. We then present additional statistical evidence that the pathology of the brain dynamics prior to seizures is reset mostly upon seizures' occurrence, a phenomenon we have called seizure resetting. Finally, using a biologically-plausible neural population mathematical model that can exhibit seizure-like behavior, we provide evidence for the effectiveness of a recently devised seizure control scheme we have called 'feedback decoupling'. This scheme also provides an interesting dynamical model for ictogenesis (generation of seizures).

Original languageEnglish (US)
Title of host publicationComplex Dynamics in Physiological Systems
Subtitle of host publicationFrom Heart to Brain
EditorsSyamal Dana, Prodyot Roy, Jurgen Kurths
Number of pages30
StatePublished - 2009

Publication series

NameUnderstanding Complex Systems
ISSN (Print)1860-0832
ISSN (Electronic)1860-0840

ASJC Scopus subject areas

  • Software
  • Computational Mechanics
  • Artificial Intelligence


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