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 Experiment Video

Updated: Jun 18, 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

Optimization of SSVEP brain responses with application to eight-command Brain-Computer Interface.

Hovagim Bakardjian1, Toshihisa Tanaka, Andrzej Cichocki

  • 1Laboratory for Advanced Brain Signal Processing, Brain Science Institute, RIKEN, Wako-shi, Saitama, Japan. hova@brain.riken.jp

Neuroscience Letters
|November 26, 2009
PubMed
Summary

Optimizing Steady-State Visual Evoked Potentials (SSVEP) stimuli enhances Brain-Computer Interface (BCI) performance. This study identified optimal frequencies and dynamics for robust BCI control, achieving 98% success rates.

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

EDSF-Net : An enhanced dynamic spatiotemporal-frequency attention network for robust EEG decoding in motor imagery.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Breaking the Depth Barrier in Motor Imagery Classification via a Residual Depthwise-Separable Network.

IEEE transactions on cybernetics·2026
Same author

Enhancing Target Recognition Performance in SSVEP-Based Brain-Computer Interfaces via Deep Neural Networks With Pyramid Squeeze Attention.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

BR-SFDA: A Source-Target Bidirectional Refined SFDA for Privacy Preserving EEG-based BCIs.

IEEE journal of biomedical and health informatics·2026
Same author

Enhancing the Capability and Accuracy of Motor Imagery Classification: A Deep Neural Network-Powered Multifaceted Strategy Model.

IEEE transactions on cybernetics·2026
Same author

TBMSCCN: Two-Branch Multi-Scale Convolutional Correlation Network for Steady-State Visual Evoked Potential Classification.

IEEE transactions on bio-medical engineering·2026

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Steady-State Visual Evoked Potentials (SSVEP) are crucial for Brain-Computer Interfaces (BCI).
  • Optimizing SSVEP stimuli is key to maximizing BCI efficiency and command recognition.
  • Previous BCI systems utilized limited SSVEP parameters.

Purpose of the Study:

  • To optimize brain responses to reversing visual patterns in an SSVEP paradigm.
  • To determine optimal neurophysiological parameters for SSVEP stimuli.
  • To define onset-delay and limitations of SSVEP stimuli in BCI applications.

Main Methods:

  • Investigated SSVEP frequency response across 32 frequencies (5-84 Hz).
  • Analyzed time dynamics of brain responses at 8, 14, and 28 Hz.

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

Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Related Experiment Videos

Last Updated: Jun 18, 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

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

Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

  • Evaluated an eight-command BCI system with frequency-optimized stimuli and dynamic neurofeedback.

    • Main Results:

    • Pattern reversal stimulation between 5.6-15.3 Hz yielded strongest SSVEP responses, peaking at 12 Hz.
    • Weaker SSVEP response maxima observed at 28 Hz and 42 Hz.
    • Optimized BCI system achieved 98% success rate with a 3.4s time delay, demonstrating robustness with dense, dynamic patterns.

    Conclusions:

    • SSVEP response dynamics are non-stationary and frequency-dependent post-stimulation onset.
    • Optimizing SSVEP stimuli, alongside analysis algorithms, is essential for maximizing BCI performance.
    • The findings support the development of more efficient and responsive BCI systems.