Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Dec 23, 2025

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

Comparison of Different Visual Feedback Methods for SSVEP-Based BCIs.

Mihaly Benda¹, Ivan Volosyak¹

¹Faculty of Technology and Bionics, Rhine-Waal University of Applied Sciences, 47533 Kleve, Germany.

|April 25, 2020

Summary

This summary is machine-generated.

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

Streamlining cVEP Paradigms: Effects of a Minimized Electrode Montage on Brain-Computer Interface Performance.

Brain sciences·2025

Same author

Evaluation of Different Visual Feedback Methods for Brain-Computer Interfaces (BCI) Based on Code-Modulated Visual Evoked Potentials (cVEP).

Brain sciences·2024

Same author

cVEP Training Data Validation-Towards Optimal Training Set Composition from Multi-Day Data.

Brain sciences·2022

Same author

Comparison of Modern Highly Interactive Flicker-Free Steady State Motion Visual Evoked Potentials for Practical Brain-Computer Interfaces.

Brain sciences·2020

Same author

Five Shades of Grey: Exploring Quintary <i>m</i>-Sequences for More User-Friendly c-VEP-Based BCIs.

Computational intelligence and neuroscience·2020

Same author

Dynamic time window mechanism for time synchronous VEP-based BCIs-Performance evaluation with a dictionary-supported BCI speller employing SSVEP and c-VEP.

PloS one·2019

Same journal

Anterior Cingulate Cortex Mediates State-Dependent Prioritization of Distressed Conspecifics.

Brain sciences·2026

Same journal

Hemispherotomy for Pediatric Post-Traumatic Epilepsy.

Brain sciences·2026

Same journal

When Robots Learn: Artificial Intelligence and the Next Human-Centered Era of Neurorehabilitation.

Brain sciences·2026

Same journal

The Association Between Changes in White Matter Microstructure and Cognitive Function in Older Adults with Mild Cognitive Impairment.

Brain sciences·2026

Same journal

Beyond Ventricular Enlargement: Multimodal MRI Assessment Improves Surgical Decision-Making in Normal Pressure Hydrocephalus.

Brain sciences·2026

Same journal

The Effects of Personalized Observation, Execution, and Mental Imagery (POEM) Therapy in Logopenic Primary Progressive Aphasia: A Telepractice-Based Single-Case Study.

Brain sciences·2026

See all related articles

This study explored visual feedback for brain-computer interface (BCI) spellers. Optimal feedback significantly improved information transfer rates for some users, enhancing BCI speed without sacrificing accuracy.

Area of Science:

Neuroscience
Human-Computer Interaction
Biomedical Engineering

Background:

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) are used for communication.
Online visual feedback significantly impacts BCI performance in SSVEP spellers.
Previous research indicated stimulus size and contrast changes are crucial feedback elements.

Purpose of the Study:

To compare various visual feedback methods for SSVEP-based BCI speller applications.
To investigate the effects of stimulus size and contrast variations on BCI performance.
To determine optimal feedback strategies for individual users to enhance BCI speed and accuracy.

Main Methods:

A 4-target SSVEP speller interface was utilized with 24 participants.

Keywords:

brain–computer interface (BCI)electroencephalography (EEG)steady-state visual evoked potential (SSVEP)user feedback

More Related Videos

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

Objectively Assessing Sports Concussion Utilizing Visual Evoked Potentials

Objectively Assessing Sports Concussion Utilizing Visual Evoked Potentials

Published on: April 27, 2021

Related Experiment Videos

Last Updated: Dec 23, 2025

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

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

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

Objectively Assessing Sports Concussion Utilizing Visual Evoked Potentials

Objectively Assessing Sports Concussion Utilizing Visual Evoked Potentials

Published on: April 27, 2021

Five feedback scenarios were tested: no feedback, size increasing/decreasing, and contrast increasing/decreasing.

Participants spelled six-letter words, requiring at least 18 selections per scenario.

Main Results:

No visual feedback was generally superior to other tested feedback modalities.
Six participants achieved significantly higher Information Transfer Rates (ITRs) with optimized feedback compared to no feedback.
The average ITR improvement using the individually fastest feedback method was 46.52%.

Conclusions:

Individualized visual feedback optimization can substantially enhance BCI speller performance.
Tailoring feedback strategies can improve BCI speed without compromising accuracy.
This research provides critical insights for optimizing BCI experimental design and user experience.