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Related Experiment Video

Updated: Jun 27, 2026

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

Large-Scale Synchronization Dynamics During Epileptic Seizures: A Patient-Independent EEG Network Analysis.

Oleg Gorshkov1, Hernando Ombao1

  • 1Statistics Program, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia.

Entropy (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

Epileptic seizure network dynamics reveal reproducible patterns using electroencephalography (EEG). Patient-independent analysis shows distinct interhemispheric and intrahemispheric synchronization changes during seizures.

Keywords:
EEG functional connectivityepileptic seizuresleave-one-patient-out (LOPO) validationpatient-independent classificationphase synchronization

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Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy
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Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

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Last Updated: Jun 27, 2026

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Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy
10:23

Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

Published on: June 23, 2023

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Medical Imaging

Background:

  • Epileptic seizures involve complex brain network dynamics.
  • Understanding these dynamics is crucial for diagnosis and treatment.
  • Scalp electroencephalography (EEG) offers a non-invasive window into brain activity.

Purpose of the Study:

  • To characterize reproducible large-scale synchronization patterns during epileptic seizures.
  • To identify patient-independent network-level alterations.
  • To develop a method for quantifying seizure-related network changes.

Main Methods:

  • Utilized scalp EEG recordings from the CHB-MIT database.
  • Estimated functional connectivity using phase-lag measures (Imaginary Coherence, debiased weighted phase lag index).
  • Trained a neural network classifier with Leave-One-Patient-Out (LOPO) validation and introduced Relative Pathological Synchronization (RPS).

Main Results:

  • Synchronization patterns systematically deviate from baseline activity during seizures in a time-dependent manner.
  • Interhemispheric connectivity exhibited earlier and higher peak RPS values than intrahemispheric connectivity.
  • Theta-band synchronization was a consistent feature, with longer seizures correlating with higher peak RPS.

Conclusions:

  • Large-scale synchronization patterns contain stable, patient-independent information about seizure dynamics.
  • Interhemispheric connectivity plays a critical role in early seizure dynamics.
  • The developed RPS metric effectively quantifies seizure-related network alterations.