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

Partial directed coherence analysis of resting-state EEG signals for alcohol use disorder detection using machine learning.

Frontiers in neuroscience·2025
Same author

Explainable AI in Diagnostic Radiology for Neurological Disorders: A Systematic Review, and What Doctors Think About It.

Diagnostics (Basel, Switzerland)·2025
Same author

Edge Computing for AI-Based Brain MRI Applications: A Critical Evaluation of Real-Time Classification and Segmentation.

Sensors (Basel, Switzerland)·2024
Same author

Functional excitation-inhibition ratio for social anxiety analysis and severity assessment.

Frontiers in psychiatry·2024
Same author

Corrigendum: Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures.

Frontiers in psychiatry·2023
Same author

Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures.

Frontiers in psychiatry·2023
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: Sep 5, 2025

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram
06:12

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram

Published on: March 13, 2018

10.7K

Drowsiness Detection Using Ocular Indices from EEG Signal.

Sreeza Tarafder1, Nasreen Badruddin1, Norashikin Yahya1

  • 1Department of Electrical and Electronic Engineering, Institute of Health and Analytics, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia.

Sensors (Basel, Switzerland)
|July 9, 2022
PubMed
Summary
This summary is machine-generated.

Driver drowsiness detection can be improved by analyzing electroencephalography (EEG) ocular artifacts. This study shows that features from these artifacts can accurately classify alert and drowsy states, achieving 91.10% accuracy.

Keywords:
drowsiness detectionelectroencephalographyensemble learningmachine learningocular artifacts

More Related Videos

Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content
10:41

Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content

Published on: May 26, 2018

7.0K
Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
07:11

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Published on: May 25, 2020

6.5K

Related Experiment Videos

Last Updated: Sep 5, 2025

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram
06:12

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram

Published on: March 13, 2018

10.7K
Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content
10:41

Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content

Published on: May 26, 2018

7.0K
Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
07:11

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Published on: May 25, 2020

6.5K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Driver drowsiness is a significant cause of road accidents.
  • Electroencephalography (EEG) signals are increasingly used for drowsiness detection.
  • Ocular artifacts in EEG are typically removed as noise.

Purpose of the Study:

  • To investigate the utility of EEG ocular artifacts for classifying driver states.
  • To determine if features extracted from ocular artifacts can differentiate between alert and drowsy states.

Main Methods:

  • Utilized the BLINKER algorithm to extract 25 blink-related features from a public EEG dataset.
  • Trained and optimized machine learning models (Decision Tree, SVM, KNN, Bagged Trees, Boosted Trees) using seven selected features.
  • Evaluated classification performance for alert vs. drowsy states.

Main Results:

  • Features extracted from EEG ocular artifacts demonstrated classification capability.
  • Optimized ensemble-boosted trees achieved the highest accuracy of 91.10%.
  • This approach successfully classified drowsy and alert states.

Conclusions:

  • EEG ocular artifacts contain valuable information for driver drowsiness detection.
  • Analyzing these artifacts offers a promising alternative to traditional EEG signal processing.
  • Machine learning models, particularly ensemble methods, can effectively leverage artifact features for improved safety.