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

Time to peak sales: how long is the climb?

Nature reviews. Drug discovery·2026
Same author

An explainable deep learning approach for sleep staging in sleep apnea patients across all age subgroups from pulse oximetry signals.

Engineering applications of artificial intelligence·2026
Same author

Impact of Repeated Antigen Exposure on Humoral Tolerance: Antidrug Antibodies After Single-Dose Versus Multi-dose Adalimumab.

BioDrugs : clinical immunotherapeutics, biopharmaceuticals and gene therapy·2026
Same author

Mechanism of lipid transfer by bridge-like protein VPS13A and the scramblase XK.

Cell·2026
Same author

Correction: Antiphospholipid antibodies induce endothelial procoagulant activity and release of extracellular vesicles independently of a second hit.

Frontiers in immunology·2026
Same author

Early prediction of longitudinal treatment adherence in obstructive sleep apnea using machine learning approaches.

BioData mining·2026

Related Experiment Video

Updated: May 5, 2026

Coherence between Brain Cortical Function and Neurocognitive Performance during Changed Gravity Conditions
12:29

Coherence between Brain Cortical Function and Neurocognitive Performance during Changed Gravity Conditions

Published on: May 23, 2011

19.5K

EEG Dataset Collection for Mental Workload Predictions in Flight-Deck Environment.

Aura Hernández-Sabaté1,2, José Yauri1, Pau Folch2

  • 1Computer Vision Center (CVC), C/ Sitges, Edifici O, 08193 Bellaterra, Spain.

Sensors (Basel, Switzerland)
|February 24, 2024
PubMed
Summary

This study introduces a new dataset of electroencephalogram (EEG) recordings to improve the detection of mental workload in pilots. This data will aid in developing AI models for enhanced flight safety.

Keywords:
EEG physiological datadeep learningflight simulationmental workloadserious gamestransfer learning

More Related Videos

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
07:48

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Published on: April 4, 2025

1.5K
Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task
07:08

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

Published on: December 5, 2025

1.2K

Related Experiment Videos

Last Updated: May 5, 2026

Coherence between Brain Cortical Function and Neurocognitive Performance during Changed Gravity Conditions
12:29

Coherence between Brain Cortical Function and Neurocognitive Performance during Changed Gravity Conditions

Published on: May 23, 2011

19.5K
Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
07:48

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Published on: April 4, 2025

1.5K
Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task
07:08

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

Published on: December 5, 2025

1.2K

Area of Science:

  • Cognitive Neuroscience
  • Human-Computer Interaction
  • Aerospace Psychology

Background:

  • High mental workload impairs performance, particularly in high-risk professions like aviation.
  • Existing deep learning models require extensive, annotated datasets for accurate cognitive state assessment.
  • There's a need for specific data on mental workload and brain function in flight-deck scenarios.

Purpose of the Study:

  • To present a novel dataset of electroencephalogram (EEG) recordings for mental workload recognition.
  • To facilitate the development of AI models for detecting and assessing cognitive states.
  • To bridge the gap in understanding brain functionality under varying mental workload conditions.

Main Methods:

  • Collected EEG data from participants undergoing induced mental workload across three experiments.
  • Utilized N-back test, Heat-the-Chair game, and an Airbus320 flight simulator for workload induction.
  • Validated the dataset by correlating task difficulty with self-perception, performance, and EEG patterns.

Main Results:

  • Demonstrated significant differences in EEG temporal patterns corresponding to varying theoretical workload levels.
  • Confirmed the dataset's utility for training and evaluating artificial intelligence models.
  • Established a correlation between task difficulty, self-reported workload, and objective performance metrics.

Conclusions:

  • The presented EEG dataset is valuable for advancing research in mental workload assessment.
  • This resource supports the development of AI-driven solutions for monitoring pilot cognitive states.
  • Improved understanding and detection of mental workload can enhance aviation safety and performance.