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

Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Deep Learning Architectures for Code-Modulated Visual Evoked Potentials Detection.

ArXiv·2025
Same author

Explainable artificial intelligence approaches for brain-computer interfaces: a review and design space.

Journal of neural engineering·2024
Same author

Editorial: Translational brain-computer interfaces: From research labs to the market and back.

Frontiers in human neuroscience·2023
Same author

Covariate shift estimation based adaptive ensemble learning for handling non-stationarity in motor imagery related EEG-based brain-computer interface.

Neurocomputing·2020
Same author

Classification of propofol-induced sedation states using brain connectivity analysis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2018
Same author

Current source density estimates improve the discriminability of scalp-level brain connectivity features related to motor-imagery tasks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2018

Related Experiment Video

Updated: May 29, 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

Spelling with non-invasive Brain-Computer Interfaces--current and future trends.

Hubert Cecotti1

  • 1GIPSA-lab, CNRS UMR5216 961, rue de la Houille Blanche, BP 46, 38402 Grenoble Cedex, France. cecotti@psych.ucsb.edu

Journal of Physiology, Paris
|September 14, 2011
PubMed
Summary
This summary is machine-generated.

This review examines Brain-Computer Interface (BCI) spelling strategies, including P300, steady-state visual evoked potentials, and motor imagery. It highlights limitations and practical challenges to manage expectations for BCI applications.

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: May 29, 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

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

Area of Science:

  • Neuroscience and Biomedical Engineering
  • Human-Computer Interaction
  • Machine Learning and Signal Processing

Background:

  • Brain-Computer Interfaces (BCIs) offer a communication channel for individuals with severe motor disabilities by analyzing electroencephalography (EEG) signals.
  • Spelling applications are fundamental BCI uses, serving as a critical communication method for non-verbal individuals and a benchmark for BCI development.

Purpose of the Study:

  • To review current BCI strategies for word spelling, focusing on their inherent limitations.
  • To discuss recent advancements in BCIs utilizing P300, steady-state visual evoked potentials (SSVEPs), and motor imagery (MI) for spelling tasks.
  • To address practical challenges and manage expectations regarding BCI spellers and virtual keyboards for both disabled and healthy users.

Main Methods:

  • Review of existing literature on non-invasive BCIs for spelling.
  • Analysis of BCI paradigms including P300 event-related potentials, SSVEPs, and MI.
  • Evaluation of limitations and pragmatic issues associated with current BCI spelling technologies.

Main Results:

  • Identified key BCI spelling strategies: P300, SSVEP, and MI, each with distinct performance characteristics and limitations.
  • Highlighted challenges in achieving robust and efficient spelling, particularly for certain patient populations.
  • Discussed practical considerations for BCI spellers and virtual keyboards, emphasizing realistic performance expectations.

Conclusions:

  • Current BCI spelling technologies, while promising, face significant limitations that impact their practical usability.
  • Further research is needed to overcome challenges in signal processing, machine learning, and user interface design for effective BCI spellers.
  • Setting realistic expectations is crucial for the successful adoption and development of BCI technology for communication.