Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Co-Loaded PEGylated Nanoliposomes of Bendamustine and Rutin: Formulation, Release Kinetics, and a Hybrid Predictive Modeling Framework.

Pharmaceutics·2026
Same author

Deep learning-enhanced X-space reconstruction for magnetic particle imaging: a physics-consistent approach.

Biomedical physics & engineering express·2026
Same author

On the Use of a Depth Camera for the Assessment of Upper Extremity Movements in Healthy Individuals.

Sensors (Basel, Switzerland)·2026
Same author

Dynamic Neural Network States During Social and Non-Social Cueing in Virtual Reality Working Memory Tasks: A Leading Eigenvector Dynamics Analysis Approach.

Brain sciences·2025
Same author

Classification of Epileptic and Psychogenic Nonepileptic Seizures via Time-Frequency Features of EEG Data.

International journal of neural systems·2023
Same author

Detection of Alzheimer's Dementia by Using Signal Decomposition and Machine Learning Methods.

International journal of neural systems·2022

Related Experiment Video

Updated: Oct 18, 2025

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

12.6K

Synchronization Analysis In Epileptic EEG Signals Via State Transfer Networks Based On Visibility Graph Technique.

Ali Olamat1, Pinar Ozel2, Aydin Akan3

  • 1Biomedical Engineering Department, Istanbul University, Istanbul, Turkey.

International Journal of Neural Systems
|September 29, 2021
PubMed
Summary

This study introduces the state transfer network (STN) method for analyzing electroencephalography (EEG) data in epilepsy. STN reveals increased neural synchronization during seizures, aiding in epileptic seizure classification.

Keywords:
Epilepsyictalmotifnetworksynchronizationvisibility graph

More Related Videos

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.4K
Use of a Wireless Video-EEG System to Monitor Epileptiform Discharges Following Lateral Fluid-Percussion Induced Traumatic Brain Injury
09:16

Use of a Wireless Video-EEG System to Monitor Epileptiform Discharges Following Lateral Fluid-Percussion Induced Traumatic Brain Injury

Published on: June 21, 2019

25.9K

Related Experiment Videos

Last Updated: Oct 18, 2025

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

12.6K
Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.4K
Use of a Wireless Video-EEG System to Monitor Epileptiform Discharges Following Lateral Fluid-Percussion Induced Traumatic Brain Injury
09:16

Use of a Wireless Video-EEG System to Monitor Epileptiform Discharges Following Lateral Fluid-Percussion Induced Traumatic Brain Injury

Published on: June 21, 2019

25.9K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Signal Processing

Background:

  • Epilepsy is a neurological disorder characterized by recurrent seizures due to abnormal brain neuron electrical discharges.
  • Electroencephalography (EEG) is crucial for monitoring brain activity but requires advanced analysis for complex conditions like epilepsy.

Purpose of the Study:

  • To introduce and evaluate a novel graph analysis method, the state transfer network (STN), for interpreting multichannel EEG recordings in epilepsy.
  • To assess changes in neural synchronization between brain regions during epileptic seizures using the STN method.

Main Methods:

  • Analysis of preictal and ictal EEG data from 17 subjects (18 channels each) using the state transfer network (STN).
  • Quantification of network motifs and their persistence to reflect neural synchronization levels.
  • Comparison of STN findings with established methods like synchronization likelihood (SL), visibility graph similarity (VGS), and global field synchronization (GFS).

Main Results:

  • The STN method demonstrated increased overall motif persistence during the ictal phase compared to the preictal phase, indicating heightened neural synchronization during seizures.
  • Intermotif cross-correlation analysis confirmed the increased synchronization during the ictal state.
  • The STN method showed good agreement with existing techniques and offered improved efficiency.

Conclusions:

  • The state transfer network (STN) provides a novel nonlinear analysis technique for generalized synchronization in multichannel EEG data.
  • The STN method is effective for classifying epileptic seizures by detecting changes in neural synchronization patterns.