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: May 24, 2026

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

Targeting an efficient target-to-target interval for P300 speller brain-computer interfaces.

Jing Jin1, Eric W Sellers, Xingyu Wang

  • 1Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, People's Republic of China. jinjingat@gmail.com

Medical & Biological Engineering & Computing
|February 22, 2012
PubMed
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

The cognitive tasks and event-related potentials associated childhood adversity: A systematic review.

Neuroscience and biobehavioral reviews·2024
Same author

Performance Monitoring and Cognitive Inhibition during a Speech-in-Noise Task in Older Listeners.

Seminars in hearing·2023
Same author

Impact of Effortful Word Recognition on Supportive Neural Systems Measured by Alpha and Theta Power.

Ear and hearing·2022
Same author

A comprehensive review of EEG-based brain-computer interface paradigms.

Journal of neural engineering·2018
Same author

Independent home use of a brain-computer interface by people with amyotrophic lateral sclerosis.

Neurology·2018
Same author

Evaluating Brain-Computer Interface Performance in an ALS Population: Checkerboard and Color Paradigms.

Clinical EEG and neuroscience·2017
Same journal

Robot-assisted laser osteotomy system based on dose-effect model-guided feedforward control.

Medical & biological engineering & computing·2026
Same journal

A randomized multi-window 3D deep learning approach for intracranial hemorrhage detection on non-contrast head CT.

Medical & biological engineering & computing·2026
Same journal

A novel SE-ResNet architecture for continuous estimation of wrist and hand movements from HD-sEMG.

Medical & biological engineering & computing·2026
Same journal

Anti-aliasing-enhanced WaveUNet for clinically reliable 12-lead ECG reconstruction from limited 3-lead input.

Medical & biological engineering & computing·2026
Same journal

Deep multi-modal features based spatio-temporal video regression for non-invasive hemoglobin estimation.

Medical & biological engineering & computing·2026
Same journal

Reduced mechanical strength correlates with decreased elastin content in aortic intima-media tissue: association with dissection in human ascending aortas.

Medical & biological engineering & computing·2026
See all related articles

New brain-computer interface (BCI) flash patterns optimize target-to-target intervals (TTI). The 18-flash pattern balances high accuracy and information transfer rate for effective BCI communication.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Longer target-to-target intervals (TTI) in P300 brain-computer interfaces (BCI) enhance signal amplitude, improving classification accuracy.
  • However, extended TTIs increase trial duration, negatively impacting the information transfer rate (ITR).

Purpose of the Study:

  • To investigate novel flash patterns for a P300 BCI to optimize TTI.
  • To assess the impact of different TTIs on BCI performance, balancing accuracy and speed.

Main Methods:

  • A P300 BCI utilizing a 7 × 12 matrix was employed.
  • Three new flash patterns (16-, 18-, and 21-flash) with varying TTIs were designed and tested.
  • These patterns aimed to minimize TTI, reduce repetition blindness, and analyze temporal relationships between flashes.

More Related Videos

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

Study Design for Navigated Repetitive Transcranial Magnetic Stimulation for Speech Cortical Mapping
09:16

Study Design for Navigated Repetitive Transcranial Magnetic Stimulation for Speech Cortical Mapping

Published on: March 24, 2023

Related Experiment Videos

Last Updated: May 24, 2026

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 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

Study Design for Navigated Repetitive Transcranial Magnetic Stimulation for Speech Cortical Mapping
09:16

Study Design for Navigated Repetitive Transcranial Magnetic Stimulation for Speech Cortical Mapping

Published on: March 24, 2023

Main Results:

  • The 16-flash pattern demonstrated the lowest classification accuracy.
  • The 18-flash pattern achieved a significantly higher information transfer rate (ITR) compared to the 21-flash pattern.
  • Both the 18- and 21-flash patterns delivered high accuracy and ITR across all participants.

Conclusions:

  • Optimized flash patterns can effectively manage TTI in P300 BCIs.
  • The 18-flash pattern presents a favorable trade-off between accuracy and ITR, suggesting its potential for practical BCI applications.