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 Safe and Efficient Brain-Computer Interface Using Moving Object Trajectories and LED-Controlled Activation.

Micromachines·2025
Same author

Roza: a new and comprehensive metric for evaluating classification systems.

Computer methods in biomechanics and biomedical engineering·2021
Same author

Diagnosis of COVID-19 and non-COVID-19 patients by classifying only a single cough sound.

Neural computing & applications·2021
Same author

An automatic EEG-based sleep staging system with introducing NAoSP and NAoGP as new metrics for sleep staging systems.

Cognitive neurodynamics·2021

Related Experiment Video

Updated: Jan 10, 2026

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients
06:11

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients

Published on: April 18, 2025

1.5K

Intersession Robust Hybrid Brain-Computer Interface: Safe and User-Friendly Approach with LED Activation Mechanism.

Sefa Aydın1, Mesut Melek2, Levent Gökrem3

  • 1Department of Electronics and Automation, Gumushane University, Gumushane 29100, Turkey.

Micromachines
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

This novel Brain-Computer Interface (BCI) system uses EEG and EOG signals with a safe LED activation to improve accuracy and reduce eye strain. The hybrid system enhances stability and user comfort for practical BCI applications.

Keywords:
ElectroencephalographyElectrooculography artefactsEmotiv Flexbrain–computer interfaceclassificationcorrelation alignmentcross-sessionmachine learning

More Related Videos

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

13.7K
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

Related Experiment Videos

Last Updated: Jan 10, 2026

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients
06:11

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients

Published on: April 18, 2025

1.5K
SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

13.7K
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

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Traditional visual stimulus-based Brain-Computer Interface (BCI) systems can negatively impact user eye health due to visual fatigue.
  • There is a need for BCI systems that offer secure activation and maintain stability across varying physiological and psychological conditions.
  • Integrating Electroencephalography (EEG) and Electrooculography (EOG) signals presents a promising avenue for developing advanced BCI functionalities.

Purpose of the Study:

  • To introduce a hybrid BCI system integrating EEG and EOG signals with a novel, secure activation mechanism.
  • To minimize visual fatigue and eye strain associated with conventional visual stimulus BCI systems.
  • To enhance system stability and accuracy by reducing inter-session variance.

Main Methods:

  • Development of a hybrid BCI system utilizing Electroencephalography (EEG) and Electrooculography (EOG) signals.
  • Implementation of a 7 Hz LED stimulus for safe system activation, reducing visual fatigue.
  • Application of the Correlation Alignment (CORAL) method for inter-session variance reduction and Bootstrap Aggregating for classification.

Main Results:

  • The hybrid BCI system demonstrated increased accuracy from 81.54% to 94.29% after applying the CORAL method.
  • The system effectively reduced visual fatigue while maintaining command generation through moving objects.
  • The proposed system showed robust stability and adaptability to users' changing cognitive states.

Conclusions:

  • The hybrid BCI system offers a safe and effective activation mechanism, mitigating eye health concerns.
  • The integration of CORAL and Bootstrap Aggregating significantly enhances BCI system accuracy and stability.
  • This approach provides a practical and high-performing BCI solution, comparable or superior to existing systems, even with fewer EEG channels.