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

Application of Filter Bank to Improve Fatigue Monitoring in Wearable EEG-Based Brain-Computer Interface.

NeuroSci·2026
Same author

Recruitment Challenges in Spinal Cord Stimulation Trial for Motor Recovery in Patients with Chronic Complete Spinal Cord Injury.

Journal of clinical medicine·2025
Same author

Comparing a BCI communication system in a patient with Multiple System Atrophy, with an animal model.

Brain research bulletin·2025
Same author

Resting-state EEG biomarkers of accelerated intermittent theta burst stimulation treatment for depression: a pilot study.

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

A Personalized Multimodal BCI-Soft Robotics System for Rehabilitating Upper Limb Function in Chronic Stroke Patients.

Biomimetics (Basel, Switzerland)·2025
Same author

Smart Sleep Monitoring: Sparse Sensor-Based Spatiotemporal CNN for Sleep Posture Detection.

Sensors (Basel, Switzerland)·2024

Related Experiment Video

Updated: Jun 8, 2025

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

7.6K

Wearable EEG-Based Brain-Computer Interface for Stress Monitoring.

Brian Premchand1, Liyuan Liang1, Kok Soon Phua1

  • 1Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #21-01 Connexis (South Tower), Singapore 138632, Singapore.

Neurosci
|November 1, 2024
PubMed
Summary
This summary is machine-generated.

Detecting stress using electroencephalogram (EEG) brain activity is more effective than heart rate variability (HRV) for high-pressure work. This brain-computer interface (BCI) system shows promise for evaluating mental stress.

Keywords:
Brain–Computer Interface (BCI)Cognitive Vigilance Task (CVT)Multi-Modal Integrated Task (MMIT)distressstress

More Related Videos

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

2.3K
Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students
12:51

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

Published on: June 16, 2018

7.4K

Related Experiment Videos

Last Updated: Jun 8, 2025

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

7.6K
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

2.3K
Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students
12:51

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

Published on: June 16, 2018

7.4K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Occupational Health

Background:

  • Stress detection is crucial for managing cognitive performance and health.
  • Chronic stress negatively impacts performance and well-being.
  • Brain-Computer Interface (BCI) systems offer potential for real-time stress monitoring.

Purpose of the Study:

  • To develop and evaluate a BCI system for detecting stress in high-pressure work environments.
  • To compare the efficacy of electroencephalogram (EEG) and electrocardiogram (ECG)-derived heart rate variability (HRV) for stress detection.

Main Methods:

  • A BCI system integrating EEG headband and ECG chest belt was used.
  • Data were collected from 40 participants during two designed cognitive tasks (CVT, MMIT).
  • EEG and HRV features were used to train separate stress classification models.

Main Results:

  • The designed Multi-Modal Integration Task (MMIT) significantly increased self-reported stress levels.
  • The EEG-based model achieved higher accuracy (81.0% for MMIT, 77.2% for CVT) compared to the HRV-based model (62.1% for CVT, 56.0% for MMIT).

Conclusions:

  • EEG signals are effective predictors of stress during cognitive tasks.
  • The proposed EEG-based BCI system demonstrates potential for evaluating mental stress in demanding work settings.