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

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

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: May 20, 2025

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation
11:06

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

Published on: April 12, 2016

10.4K

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

Sefa Aydin1, Mesut Melek1, Levent Gökrem2

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

Micromachines
|March 27, 2025
PubMed
Summary

This study introduces a novel brain-computer interface (BCI) that uses moving objects instead of visual stimuli to protect eye health. This innovative approach enhances user safety and accessibility for individuals with mobility impairments.

Keywords:
Emotiv Epocbrain–computer interfaceclassificationelectroencephalographyelectrooculographymachine learning

More Related Videos

In Vivo Wireless Optogenetic Control of Skilled Motor Behavior
07:52

In Vivo Wireless Optogenetic Control of Skilled Motor Behavior

Published on: November 22, 2021

3.2K
An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

13.7K

Related Experiment Videos

Last Updated: May 20, 2025

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation
11:06

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

Published on: April 12, 2016

10.4K
In Vivo Wireless Optogenetic Control of Skilled Motor Behavior
07:52

In Vivo Wireless Optogenetic Control of Skilled Motor Behavior

Published on: November 22, 2021

3.2K
An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

13.7K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Brain-computer interface (BCI) systems facilitate communication and device control for individuals with severe motor disabilities.
  • Current BCI systems often rely on visual evoked potential (VEP) and P300 methods, which can cause eye strain due to prolonged visual stimuli.
  • There is a need for BCI systems that minimize visual discomfort and eye health risks for long-term users.

Purpose of the Study:

  • To propose and evaluate a novel BCI approach that reduces the negative impact of visual stimuli on user eye health.
  • To introduce a method using moving objects and a blinking LED as an activation condition, replacing traditional focused visual stimuli.
  • To enhance the safety and usability of BCI systems for individuals with mobility impairments.

Main Methods:

  • A new BCI paradigm was developed, utilizing moving objects with distinct trajectories as the primary interaction method.
  • A 7 Hz blinking light-emitting diode (LED) was incorporated as an activation condition to prevent false triggers from involuntary eye movements.
  • Data were collected in two phases (LED on/off), processed using Butterworth filtering and power spectral density (PSD) analysis.
  • Two classification phases were conducted: LED detection and classification of moving objects, employing the random forest (RF) algorithm.

Main Results:

  • The random forest (RF) classifier achieved 99.57% accuracy in detecting the LED, confirming its effectiveness as an activation condition.
  • In the second phase, classifying moving objects yielded a high accuracy rate of 97.89%.
  • The proposed BCI system demonstrated a significant information transfer rate (ITR) of 36.75 bits/min, indicating efficient communication.

Conclusions:

  • The novel BCI approach effectively minimizes visual stimulus disadvantages, thereby protecting user eye health.
  • The integration of moving objects and a blinking LED provides a safe and reliable alternative to traditional visual evoked potential (VEP) and P300 methods.
  • This research offers a promising advancement in creating more comfortable and accessible BCI systems for users with mobility impairments.