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

Decoding Attention through EEG: Paving the Way for BCI Applications in Attention-Related Disorders.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

From high- to low-density EEG for automatic classification of dream experiences during stage 2 of NREM.

Sleep advances : a journal of the Sleep Research Society·2025
Same author

Enhancing sleep stage classification with 2-class stratification and permutation-based channel selection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Unlocking Dreams and Dreamless Sleep: Machine Learning Classification With Optimal EEG Channels.

BioMed research international·2025
Same author

GRU-powered sleep stage classification with permutation-based EEG channel selection.

Scientific reports·2024
Same author

Newly identified Phonocardiography frequency bands for psychological stress detection with Deep Wavelet Scattering Network.

Computers in biology and medicine·2024

Related Experiment Video

Updated: May 28, 2025

Disruption of Frontal Lobe Neural Synchrony During Cognitive Control by Alcohol Intoxication
09:26

Disruption of Frontal Lobe Neural Synchrony During Cognitive Control by Alcohol Intoxication

Published on: February 6, 2019

18.7K

EEG-Based Alcohol Detection System for Driver Monitoring.

Molly Vassbotn1, Iselin J Nordstrøm-Hauge1, Andres Soler1

  • 1Department of Engineering Cybernetics, Norwegian University of Science and Technology, O.S. Bragstads plass 2D, Trondheim, 7034, Norway. Norges Teknisk-naturvitenskapelige Universitet Department of Engineering Cybernetics Norwegian University of Science and Technology Trondheim7034 Norway.

International Journal of Psychological Research
|February 10, 2025
PubMed
Summary

This study introduces an electroencephalography (EEG)-based system to detect alcohol impairment in drivers. Individualized models achieved 90.7% accuracy, showing promise for preventing drunk driving.

Keywords:
Alcohol DetectionConvolutional Neural Network (CNN)EEGNetElectroencephalography (EEG)Flanker Test

More Related Videos

The Use of Trace Eyeblink Classical Conditioning to Assess Hippocampal Dysfunction in a Rat Model of Fetal Alcohol Spectrum Disorders
19:57

The Use of Trace Eyeblink Classical Conditioning to Assess Hippocampal Dysfunction in a Rat Model of Fetal Alcohol Spectrum Disorders

Published on: August 5, 2017

8.3K
Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder
05:12

Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder

Published on: June 23, 2023

863

Related Experiment Videos

Last Updated: May 28, 2025

Disruption of Frontal Lobe Neural Synchrony During Cognitive Control by Alcohol Intoxication
09:26

Disruption of Frontal Lobe Neural Synchrony During Cognitive Control by Alcohol Intoxication

Published on: February 6, 2019

18.7K
The Use of Trace Eyeblink Classical Conditioning to Assess Hippocampal Dysfunction in a Rat Model of Fetal Alcohol Spectrum Disorders
19:57

The Use of Trace Eyeblink Classical Conditioning to Assess Hippocampal Dysfunction in a Rat Model of Fetal Alcohol Spectrum Disorders

Published on: August 5, 2017

8.3K
Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder
05:12

Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder

Published on: June 23, 2023

863

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Public Health

Background:

  • Alcohol consumption is a prevalent social activity globally, with significant rates reported in the US and Norway.
  • Driving under the influence of alcohol (DUI) remains a major cause of traffic fatalities and injuries worldwide.
  • Existing methods for detecting alcohol impairment may have limitations in real-time application for drivers.

Purpose of the Study:

  • To develop the foundational steps for an electroencephalography (EEG)-based alcohol detection system.
  • To assess the feasibility of using EEG signals to identify alcohol-impaired individuals, aiming to prevent drunk driving.
  • To create a dataset and evaluate machine learning models for alcohol detection using EEG.

Main Methods:

  • Designed an experimental protocol involving EEG data collection and blood alcohol concentration (BAC) measurement.
  • Participants performed the Flanker task under both alcohol-affected and non-affected conditions.
  • Utilized the EEGNet architecture for both intra-subject (individual-specific) and inter-subject (general) alcohol detection models.

Main Results:

  • Statistical analysis confirmed alcohol's effect on participants' performance and EEG signals during the Flanker task.
  • The intra-subject EEGNet model achieved a high mean classification accuracy of 90.7% for detecting alcohol impairment.
  • The inter-subject EEGNet model demonstrated a mean classification accuracy of 62.9%, exceeding chance levels.

Conclusions:

  • EEG signals can accurately detect alcohol impairment, particularly with individualized models.
  • The developed approach shows significant potential as a precursor to a real-time EEG-based alcohol detection system for drivers.
  • This research offers a novel, non-invasive method to enhance road safety by potentially preventing drunk driving incidents.