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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

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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.