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

Prefrontal fNIRS hemodynamic correlates of attentional load during rapid serial visual presentation tasks.

Frontiers in human neuroscience·2026
Same author

MEG Working Memory N-Back Task Revealed Functional Deficits in Children with Mild Traumatic Brain Injury.

Journal of neurotrauma·2026
Same author

Fast BCIs: Leveraging Dual-Scale Time Windows with Test-Time Adaptation to Enhance Accuracy.

IEEE transactions on bio-medical engineering·2026
Same author

Unified Online Adaptation Framework for Correlation Analysis-based Spatial Filtering Methods in SSVEP-based BCIs.

IEEE journal of biomedical and health informatics·2026
Same author

Predicting Attention Decline: An Integrated Beta-Band and SSVEP Approach for Visual Brain-Computer Interfaces.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

Brain-Body Coupling in Listening to Metronomic Sounds and Music.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Related Experiment Video

Updated: May 7, 2026

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

Developing stimulus presentation on mobile devices for a truly portable SSVEP-based BCI.

Yu-Te Wang, Yijun Wang, Chung-Kuan Cheng

    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 developed a mobile brain-computer interface (BCI) using steady-state visual evoked potentials (SSVEP) for practical daily use. The portable SSVEP BCI system achieved high accuracy and an information transfer rate of 33.87 bits/min.

    More Related Videos

    Objectively Assessing Sports Concussion Utilizing Visual Evoked Potentials
    12:11

    Objectively Assessing Sports Concussion Utilizing Visual Evoked Potentials

    Published on: April 27, 2021

    3.5K
    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

    12.5K

    Related Experiment Videos

    Last Updated: May 7, 2026

    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
    Objectively Assessing Sports Concussion Utilizing Visual Evoked Potentials
    12:11

    Objectively Assessing Sports Concussion Utilizing Visual Evoked Potentials

    Published on: April 27, 2021

    3.5K
    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

    12.5K

    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Human-Computer Interaction

    Background:

    • Traditional steady-state visual evoked potentials (SSVEP)-based brain-computer interfaces (BCIs) often rely on bulky desktop setups.
    • There is a growing need for portable and accessible BCI solutions for widespread adoption in daily life.

    Purpose of the Study:

    • To develop and evaluate a practical, portable, and ubiquitous SSVEP-based BCI system utilizing mobile devices.
    • To assess the performance and accuracy of a mobile SSVEP BCI compared to traditional desktop systems.

    Main Methods:

    • Integration of visual stimulus presentation and real-time electroencephalogram (EEG) data processing on mobile devices (tablets/smartphones).
    • Analysis of power spectrum density (PSD) of EEG signals elicited by visual stimuli on the mobile BCI.
    • Online testing of the tablet-based SSVEP BCI system with human subjects.

    Main Results:

    • The mobile SSVEP BCI system demonstrated comparable accuracy to previous laptop/desktop-based systems.
    • EEG signal analysis revealed effective signal processing capabilities on the mobile platform.
    • An average information transfer rate (ITR) of 33.87 bits/min was achieved in online tests with three subjects.

    Conclusions:

    • The integration of SSVEP BCI functionalities onto mobile devices significantly enhances practicability and ubiquity.
    • This development paves the way for truly practical and accessible SSVEP BCIs for real-world applications.
    • Mobile SSVEP BCIs offer a promising avenue for future assistive technologies and human-computer interaction.