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 Videos

Learning from label proportions in brain-computer interfaces: Online unsupervised learning with guarantees.

David Hübner1, Thibault Verhoeven2, Konstantin Schmid1

  • 1Brain State Decoding Lab, Cluster of Excellence BrainLinks-BrainTools, Department of Computer Science, Albert-Ludwigs-University, Freiburg, Germany.

Plos One
|April 14, 2017
PubMed
Summary

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

Towards robust foundation models for digital pathology.

Nature communications·2026
Same author

Beyond attention heatmaps: How to get better explanations for multiple instance learning models in histopathology.

Medical image analysis·2026
Same author

AI-based discovery of functional boundaries in the human brain from intraoperative electrophysiology.

medRxiv : the preprint server for health sciences·2026
Same author

Modeling attention and binding in the brain through bidirectional recurrent gating.

Nature communications·2026
Same author

Interpretable and granular video-based quantification of motor characteristics from the finger-tapping test in Parkinson's disease.

NPJ Parkinson's disease·2026
Same author

How simple can you go? An off-the-shelf transformer approach to molecular dynamics.

The Journal of chemical physics·2026

This study introduces Learning from Label Proportions (LLP), a new unsupervised method for brain-computer interfaces (BCIs). LLP eliminates calibration time for event-related potential (ERP) BCIs, achieving high accuracy without prior subject training.

Area of Science:

  • Neuroscience
  • Computer Science
  • Machine Learning

Background:

  • Traditional brain-computer interfaces (BCIs) require extensive calibration data for new users.
  • Existing methods for reducing calibration time, like classifier transfer or unsupervised adaptation, lack theoretical guarantees.
  • Event-related potential (ERP) based BCIs are a common but calibration-intensive technology.

Purpose of the Study:

  • To develop a reliable, unsupervised, and calibrationless decoding method for ERP-based BCIs.
  • To guarantee the recovery of true class means in BCI data.
  • To modify the ERP paradigm to integrate seamlessly with machine learning decoders.

Main Methods:

  • Introduced Learning from Label Proportions (LLP) as an unsupervised classification approach for ERP-BCIs.

Related Experiment Videos

  • Developed a visual ERP speller system tailored for LLP requirements.
  • Evaluated LLP through simulations on artificial datasets and an online BCI study with 13 participants.

    • Main Results:

    • LLP is theoretically guaranteed to minimize the loss function, similar to supervised classifiers.
    • Simulations and online study demonstrated strong performance of LLP.
    • An average of 84.5% character spelling accuracy was achieved in the online study without prior calibration.

    Conclusions:

    • LLP is the first unsupervised decoder for ERP BCIs with a guarantee to find the optimal decoder.
    • LLP effectively eliminates the need for tedious calibration sessions.
    • LLP's complementary principles offer potential enhancements when combined with existing unsupervised BCI methods.