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

ARKG: Adversarially Residual Knowledge Generalization to Open-Set Domain Adaptation.

IEEE transactions on neural networks and learning systems·2026
Same author

A solid waste classification using reinforcement learning boosted by data augmentation and hyperparameter optimization.

Scientific reports·2026
Same author

Developing appropriateness criteria for arterial catheters in adult intensive care unit patients.

Australian critical care : official journal of the Confederation of Australian Critical Care Nurses·2026
Same author

Complex emotion recognition system using basic emotions via facial expression, electroencephalogram, and electrocardiogram signals: a review.

Frontiers in psychology·2026
Same author

Attention-based graph neural network framework for non-invasive CAP score prediction in fatty liver disease via body modeling.

Physical and engineering sciences in medicine·2025
Same author

Efficacy of a Ceftazidime-loaded Nanofiber Insert in Treating <i>Pseudomonas aeruginosa</i>-induced Corneal Ulcers: An Animal Model.

Journal of ophthalmic & vision research·2025
Same journal

ECG arrhythmia classification via wavelet-driven feature extraction and swarm-optimised gradient boosting.

Computers in biology and medicine·2026
Same journal

Electro-osmotic metachronal cilia transport of viscoelastic blood infused with penta-hybrid nanoparticles in an oviduct: Analytical and neural network modeling.

Computers in biology and medicine·2026
Same journal

sEEGnal: an automated EEG preprocessing pipeline evaluated against expert-driven preprocessing.

Computers in biology and medicine·2026
Same journal

Corrigendum to "Integrating experimental biology, computational methods, and artificial Intelligence in anticancer drug discovery: Bridging the translational Gap" [Comput. Biol. Med. 213 (2026) 111832].

Computers in biology and medicine·2026
Same journal

Organ dose optimization for a point-of-care forearm X-ray photon-counting CT.

Computers in biology and medicine·2026
Same journal

Physics-guided transformation of breathomic feature spaces into disease-specific representations for respiratory disease classification.

Computers in biology and medicine·2026
See all related articles

Related Experiment Video

Updated: Jun 13, 2026

Neuroimaging-Guided TMS&#8211;EEG for Real-Time Cortical Network Mapping
09:55

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

Reconfiguring brain networks via lightweight dynamic connectivity framework: An EEG-based stress validation.

Sayantan Acharya1, Abbas Khosravi1, Douglas Creighton1

  • 1Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC, Australia.

Computers in Biology and Medicine
|June 11, 2026
PubMed
Summary
This summary is machine-generated.

A new dynamic brain connectivity framework, Time-Varying Directed Transfer Function (TV-DTF), effectively analyzes Electroencephalographic (EEG) signals for stress detection. Dynamic EEG features, particularly in the alpha band, show superior accuracy in machine learning models compared to static measures.

Keywords:
ClassificationConnectivityEEGFrameworkLightweightNetworkStressValidation

More Related Videos

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
08:23

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Related Experiment Videos

Last Updated: Jun 13, 2026

Neuroimaging-Guided TMS&#8211;EEG for Real-Time Cortical Network Mapping
09:55

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
08:23

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Signal Processing

Background:

  • Electroencephalographic (EEG) analysis combined with Artificial Intelligence (AI) and Machine Learning (ML) is increasingly used in stress research.
  • Static functional connectivity measures often overlook temporal and directional brain influences crucial for understanding dynamic neural processes.

Purpose of the Study:

  • To propose a lightweight dynamic brain connectivity framework for estimating Time-Varying Directed Transfer Function (TV-DTF) in EEG signals.
  • To evaluate the discriminative capability of TV-DTF features for stress detection using various ML models.
  • To compare the performance of dynamic TV-DTF features against static connectivity measures.

Main Methods:

  • Utilized EEG recordings from the 32-channel SAM 40 dataset during mental arithmetic tasks.
  • Estimated dynamic effective connectivity using a novel TV-DTF framework across different frequency bands.
  • Validated TV-DTF features using Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (GB), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost).

Main Results:

  • Alpha-band TV-DTF features demonstrated the strongest discriminative power, achieving 89.73% accuracy (3-class) with SVM and 93.69% accuracy (2-class) with XGBoost.
  • Dynamic alpha-TV-DTF and beta-TV-DTF features outperformed static measures (absolute power, phase locking) across all tested ML models.
  • Feature importance analysis revealed significant frontal-parietal and frontal-occipital information flow, indicating frontal lobe regulatory roles under stress.

Conclusions:

  • The lightweight TV-DTF framework robustly captures spatiotemporal brain dynamics and directional influences in EEG signals.
  • Dynamic connectivity measures, specifically TV-DTF, offer significant advantages over static methods for stress level classification.
  • Findings highlight the utility of TV-DTF in revealing neural mechanisms underlying stress responses and its potential for developing advanced diagnostic tools.