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

Automatic sleep staging based on 24/7 EEG SubQ (UNEEG medical) data displays strong agreement with polysomnography in healthy adults.

Sleep health·2024
Same author

Mortality risk assessment using deep learning-based frequency analysis of electroencephalography and electrooculography in sleep.

Sleep·2024
Same author

Reduced P300 amplitude in children and adolescents with autism is associated with slowed processing speed, executive difficulties, and social-communication problems.

Autism : the international journal of research and practice·2024
Same author

The Ultra-Long-Term Sleep study: Design, rationale, data stability and user perspective.

Journal of sleep research·2024
Same author

A Deep Transfer Learning Approach for Sleep Stage Classification and Sleep Apnea Detection Using Wrist-Worn Consumer Sleep Technologies.

IEEE transactions on bio-medical engineering·2024
Same author

Fully Automated Detection of Isolated Rapid-Eye-Movement Sleep Behavior Disorder Using Actigraphy.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2023

Related Experiment Video

Updated: May 7, 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

12.5K

Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training.

Thomas Bender, Troels W Kjaer, Carsten E Thomsen

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 11, 2013
    PubMed
    Summary

    This study introduces a simple semi-supervised brain-computer interface (BCI) using steady-state visual evoked potentials (SSVEPs). Tri-training significantly improved the accuracy of this SSVEP-BCI system.

    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

    3.6K
    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

    9.8K

    Related Experiment Videos

    Last Updated: May 7, 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

    12.5K
    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

    3.6K
    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

    9.8K

    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Computer Science

    Background:

    • Brain-computer interfaces (BCIs) enable communication and control through brain signals.
    • Steady-state visual evoked potentials (SSVEPs) are a common BCI modality.
    • Semi-supervised learning offers a way to improve BCI performance with limited labeled data.

    Purpose of the Study:

    • To present a novel, computationally simple, semi-supervised SSVEP-based BCI.
    • To evaluate the effectiveness of tri-training in enhancing BCI accuracy.

    Main Methods:

    • Implemented a tri-training based semi-supervised learning framework.
    • Utilized autocorrelation-based features and a Naïve-Bayes classifier (NBC).
    • Employed a system with nine characters, a 100 Hz monitor, three scalp electrodes, and two PCs.

    Main Results:

    • Preliminary tests on nine healthy subjects demonstrated improved accuracy with tri-training.
    • The tri-training approach enhanced the performance of the SSVEP-BCI.

    Conclusions:

    • Tri-training is an effective method for improving SSVEP-BCI accuracy.
    • The proposed system offers a computationally simple and effective solution for SSVEP-BCI.