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

A Structured Computational Roadmap for Lipidomics in R: Reproducible Workflows from Raw Data to Functional Insight.

Metabolites·2026
Same author

Human-in-the-Loop Artificial Intelligence: A Systematic Review of Concepts, Methods, and Applications.

Entropy (Basel, Switzerland)·2026
Same author

CpGene: a web application for epigenetic signature identification from DNA methylation arrays.

Bioinformatics (Oxford, England)·2026
Same author

AI agents in Alzheimer's disease management: challenges and future directions.

Frontiers in aging neuroscience·2026
Same author

Web-Based Application for Hashimoto's Disease Prediction Based on Thyroid Hormone Levels and Machine Learning Analysis.

Advances in experimental medicine and biology·2026
Same author

Comprehensive Differential Gene Expression Analysis in Glioblastoma Using PyDESeq2: A Comparison with Normal Brain Tissue.

Advances in experimental medicine and biology·2026
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jan 13, 2026

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

3.1K

Wearable EEG Sensor Analysis for Cognitive Profiling in Educational Contexts.

Eleni Lekati1, Georgios N Dimitrakopoulos1, Konstantinos Lazaros1

  • 1Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.

Sensors (Basel, Switzerland)
|October 29, 2025
PubMed
Summary
This summary is machine-generated.

Wearable electroencephalography (EEG) reveals distinct brain activity patterns in students learning fractions. This neurocognitive data can identify learning profiles and guide personalized educational strategies for improved math performance.

Keywords:
cognitive profilingneurophysiological monitoringpersonalized educationsignal analysiswearable EEG

More Related Videos

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
13:57

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

13.1K
A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings
10:45

A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings

Published on: January 22, 2018

8.0K

Related Experiment Videos

Last Updated: Jan 13, 2026

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

3.1K
Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
13:57

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

13.1K
A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings
10:45

A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings

Published on: January 22, 2018

8.0K

Area of Science:

  • Neuroscience
  • Educational Psychology
  • Cognitive Science

Background:

  • Electroencephalography (EEG) offers real-time neural activity insights for studying learning.
  • Wearable EEG and advanced signal analysis are increasingly used in educational research.
  • Understanding cognitive profiles during complex learning is crucial for effective instruction.

Purpose of the Study:

  • To examine the cognitive profiles of sixth-grade students during fraction learning using wearable EEG.
  • To identify neurocognitive markers associated with varying levels of mathematical understanding.
  • To explore the potential of EEG data for developing precision-oriented educational strategies.

Main Methods:

  • Utilized wearable EEG to record neural activity in 30 sixth-grade students during fraction learning tasks.
  • Employed interactive digital tools (Fraction Lab, Diamond Paper task) and validated estimations.
  • Processed EEG data to analyze spectral dynamics across delta, theta, alpha, and beta frequency bands.

Main Results:

  • Lower-performing students showed elevated delta and theta power under cognitive load.
  • Higher-performing students demonstrated stable beta activity, indicating cognitive control.
  • EEG features, particularly gamma and beta oscillations, reliably distinguished three learner profiles: Core Support Needed, Developing, and Advanced.

Conclusions:

  • EEG-based signal analysis is valuable for identifying neurocognitive markers linked to mathematical conceptual and procedural knowledge (PK).
  • Objective neural data can inform the development of adaptive and personalized educational interventions.
  • Neurocognitive markers identified through EEG hold significant potential for guiding adaptive instruction and supporting diverse learners.