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

Electroencephalography-Based Brain-Computer Interface System Using Tongue Movement Imagery for Wheelchair Control.

Sensors (Basel, Switzerland)·2026
Same author

Interlimb Gait Trajectory Synchronization from Inertial Measurement Unit (IMU) with ResUNet.

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

Machine Learning-Based Optimization of tFUS Transducer Positioning for Targeted Visual Cortex Neuromodulation.

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

Autism spectrum disorder disrupts brain network connectivity maturation during childhood development.

Scientific reports·2025
Same author

Investigation of Personalized Visual Stimuli via Checkerboard Patterns Using Flickering Circles for SSVEP-Based BCI System.

Sensors (Basel, Switzerland)·2025
Same author

Modified stereotactic neurosurgery techniques for rodent surgery enhance survival and reduce surgery time in a severe traumatic brain injury model.

Scientific reports·2025

Related Experiment Video

Updated: Mar 14, 2026

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

13.8K

A multi-command SSVEP-based BCI system based on single flickering frequency half-field steady-state visual

Yunyong Punsawad1, Yodchanan Wongsawat2

  • 1Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Nakornpathom, 73170, Thailand.

Medical & Biological Engineering & Computing
|September 22, 2016
PubMed
Summary

This study introduces a novel half-field visual stimulation for brain-computer interfaces (BCIs) using steady-state visual evoked potentials (SSVEPs). This method enhances command capacity and reduces eye fatigue for improved BCI performance.

Keywords:
Brain–computer interfaceElectroencephalogramHalf-fieldSSVEP

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

3.4K
A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

9.0K

Related Experiment Videos

Last Updated: Mar 14, 2026

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

13.8K
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

3.4K
A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

9.0K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Steady-state visual evoked potentials (SSVEPs) are crucial for brain-computer interface (BCI) machine control.
  • Existing SSVEP methods suffer from eye fatigue and reduced accuracy with multi-command requirements.

Purpose of the Study:

  • To propose a novel half-field steady-state visual stimulation pattern and paradigm to increase command capabilities in SSVEP-based BCIs.
  • To reduce user eye fatigue and improve long-term BCI accuracy.

Main Methods:

  • Developed a half-field visual stimulation pattern generating four commands from a single frequency.
  • Implemented a new feature extraction and decision-making algorithm for SSVEP signal processing.
  • Evaluated signal extraction from occipital areas with parietal reference electrodes.

Main Results:

  • The proposed stimulation pattern achieved an average classification accuracy of approximately 75%.
  • The system demonstrated reduced visual fatigue compared to existing SSVEP methods.
  • Occipital signal extraction with parietal reference yielded superior results.

Conclusions:

  • The novel half-field SSVEP stimulation pattern effectively increases command numbers and reduces user fatigue.
  • The proposed feature extraction and decision-making algorithm enhance BCI performance.
  • The system is applicable for real-time applications like television control.