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

Learning Where to Look: Differentiable Slice Selection and Efficient Channel Attention for FCD-II MRI Classification.

IEEE journal of biomedical and health informatics·2026
Same author

Paraphilias and paraphilic disorders in Attention-Deficit/Hyperactivity Disorder: an observational study.

Journal of psychiatric research·2026
Same author

The neurobehavioral correlates of error processing in adult attention-deficit/hyperactivity disorder and their relationship with impulsivity.

Clinical neurophysiology practice·2025
Same author

Microstate-based Neurofeedback in Attention Deficit Hyperactivity Disorder Population: A Randomized Controlled Crossover Trial.

Brain topography·2025
Same author

$n$-Cylindrical Symbolic Response, a Standalone and Synergistic Biomarker for Epilepsy Diagnosis on EEG Modality.

IEEE journal of biomedical and health informatics·2025
Same author

A biomarker of brain arousal mediates the intergenerational link between maternal and child post-traumatic stress disorder.

Journal of psychiatric research·2024

Related Experiment Video

Updated: Mar 20, 2026

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

10.8K

Classification of EEG Single Trial Microstates Using Local Global Graphs and Discrete Hidden Markov Models.

Kostas Michalopoulos1, Michalis Zervakis2, Marie-Pierre Deiber3,4

  • 11 Center of Assistive Research Technologies, Wright State University, Dayton OH 45435, USA.

International Journal of Neural Systems
|June 4, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new method combining Local Global Graphs and Hidden Markov Models for analyzing Electroencephalogram (EEG) data. The approach effectively distinguishes between healthy individuals and those with Progressive Mild Cognitive Impairment (PMCI).

Keywords:
EEGHidden Markov modelsLG graphsclassification

More Related Videos

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
10:22

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Published on: December 6, 2016

21.3K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

15.3K

Related Experiment Videos

Last Updated: Mar 20, 2026

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

10.8K
Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
10:22

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Published on: December 6, 2016

21.3K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

15.3K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Analyzing single Electroencephalogram (EEG) trials presents challenges due to noise and complex spatio-temporal dynamics.
  • Identifying subtle differences in brain activity, particularly in early stages of cognitive decline, requires advanced analytical techniques.

Purpose of the Study:

  • To develop and validate a novel synergistic methodology for spatio-temporal analysis of single EEG trials.
  • To effectively discriminate between control subjects and patients with Progressive Mild Cognitive Impairment (PMCI) using EEG data.

Main Methods:

  • Integration of Local Global (LG) graphs to describe EEG topography structural features and enable robust comparisons.
  • Application of Hidden Markov Models (HMM) to model the sequence of topographies (microstates) and analyze transitions.
  • Training HMMs to learn microstate transitions and syntactic patterns for efficient single-trial analysis.

Main Results:

  • The proposed methodology successfully discriminates between control and Progressive MCI single EEG trials.
  • LG graphs provide effective similarity and distance measures for comparing EEG topographies, even in noisy conditions.
  • HMMs yield physiologically meaningful results for syntactic analysis of Event-Related Potentials (ERPs).

Conclusions:

  • The synergistic LG graph and HMM approach offers a powerful tool for analyzing complex EEG data.
  • This methodology holds promise for early detection and differentiation of neurological conditions like PMCI.
  • The findings support the use of HMMs in the syntactic analysis of ERPs for clinical applications.