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

Traditional arts and events for mental and social well-being: a scoping review framed by intangible cultural heritage.

The Lancet regional health. Western Pacific·2026
Same author

Combined Method Comprising Low Burden Physiological Measurements with Dry Electrodes and Machine Learning for Classification of Visually Induced Motion Sickness in Remote-Controlled Excavator.

Sensors (Basel, Switzerland)·2024
Same author

Importance of the Features of Event-Related Potentials Used for a Machine Learning-Based Model Applied to Single-Trial Data during Oddball Task.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2021
Same author

Evaluation of the Tiller Switch Layout of a Tractor Using Eye-Fixation Related Potentials.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2020
Same author

Evaluation of a Mental Care System for Patients Recuperating in a Sterile Room after Hematopoietic Cell Transplantation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2020
Same author

Tactile phantom sensation for coaching respiration timing.

IEEE transactions on haptics·2015

Related Experiment Video

Updated: May 14, 2026

Assessment of Audio-Tactile Sensory Substitution Training in Participants with Profound Deafness Using the Event-Related Potential Technique
11:39

Assessment of Audio-Tactile Sensory Substitution Training in Participants with Profound Deafness Using the Event-Related Potential Technique

Published on: September 7, 2022

Input interface using event-related potential P3.

Hidenori Boutani1, Mieko Ohsuga

  • 1Department of Biomedical Engineering, Graduate School of Engineering, Osaka Institute of Technology, Osaka, Japan. d1d12h02@st.oit.ac.jp

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
Summary

This study developed an easy brain-computer interface using electroencephalogram (EEG) signals. It effectively removes eye blink artifacts, enabling character input with just three EEG channels and ten trials.

More Related Videos

How to Find Effects of Stimulus Processing on Event Related Brain Potentials of Close Others when Hyperscanning Partners
09:52

How to Find Effects of Stimulus Processing on Event Related Brain Potentials of Close Others when Hyperscanning Partners

Published on: May 31, 2018

Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials
09:40

Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials

Published on: November 15, 2014

Related Experiment Videos

Last Updated: May 14, 2026

Assessment of Audio-Tactile Sensory Substitution Training in Participants with Profound Deafness Using the Event-Related Potential Technique
11:39

Assessment of Audio-Tactile Sensory Substitution Training in Participants with Profound Deafness Using the Event-Related Potential Technique

Published on: September 7, 2022

How to Find Effects of Stimulus Processing on Event Related Brain Potentials of Close Others when Hyperscanning Partners
09:52

How to Find Effects of Stimulus Processing on Event Related Brain Potentials of Close Others when Hyperscanning Partners

Published on: May 31, 2018

Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials
09:40

Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials

Published on: November 15, 2014

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Developing intuitive brain-computer interfaces (BCIs) is crucial for assistive technologies.
  • Contamination from eye movements and blinks in electroencephalogram (EEG) signals poses a significant challenge for BCI accuracy.
  • Event-related potentials, specifically the P3 component, offer a promising avenue for BCI control.

Purpose of the Study:

  • To develop a simple and user-friendly input interface based on P3 event-related potentials.
  • To implement and evaluate a method for removing eye movement artifacts from EEG signals.
  • To determine the optimal parameters for reliable character input using a P3-based BCI.

Main Methods:

  • Utilized independent component analysis (ICA) un-mixing matrix to remove eye movement artifacts from EEG signals.
  • Employed a support vector machine (SVM) classifier to detect the P3 component and make input character decisions.
  • Investigated the impact of varying EEG channel count, feature vector types, and SVM training data ratios on performance.

Main Results:

  • Identified three specific EEG channels (Fz, Cz, Pz) as sufficient for artifact removal and character decision-making.
  • Demonstrated that an average of ten trials are necessary for reliable input character decisions.
  • Found that a training data ratio of 1:2 for targets and non-targets yielded the best SVM performance.

Conclusions:

  • A simple and effective P3-based BCI is feasible using a minimal number of EEG channels.
  • ICA-based artifact removal significantly enhances the reliability of EEG-based input.
  • Further validation with larger datasets is recommended to confirm these findings for practical BCI applications.