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Towards subject-centered co-adaptive brain-computer interfaces based on backward optimal transport.

Victoria Peterson1,2, Valeria Spagnolo1, Catalina María Galván1,2

  • 1Instituto de Matemática Aplicada del Litoral, IMAL, UNL, CONICET, Santa Fe, Argentina.

Journal of Neural Engineering
|May 21, 2025
PubMed
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This summary is machine-generated.

This study introduces a novel method to assess motor imagery brain-computer interface (MI-BCI) skills during co-adaptive learning. The supportive backward adaptation (SBA) approach enhances BCI performance by adapting to users and providing real-time skill feedback.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Motor imagery brain-computer interfaces (MI-BCIs) require extensive practice and are sensitive to cross-session variability in electroencephalography (EEG) data.
  • Co-adaptive systems, where both the user and the BCI algorithm learn simultaneously, are crucial for improving MI-BCI control.
  • Real-time assessment of user self-modulation skills is essential for effective co-adaptive BCI training.

Purpose of the Study:

  • To develop a method for online assessment of motor imagery (MI) modulation capability.
  • To create a framework for co-adaptive BCIs that enhances both user performance and system accuracy.
  • To provide users with real-time feedback on their MI modulation skills.

Main Methods:

  • Utilized backward optimal transport for domain adaptation to enable across-session MI-BCI use without retraining classifiers.
Keywords:
BCI skillsOptimal transportco-adaptive BCIdomain adaptationuser-centered BCI

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  • Defined a supportive backward adaptation (SBA) method guided by cued labels.
  • Proposed the model effort required for trial adaptation as an online metric for MI modulation skills.
  • Validated the metric using Riemannian distinctiveness metrics on real and simulated EEG data.

    • Main Results:

    • The model effort metric derived from SBA effectively evaluates the discriminability of EEG patterns related to MI tasks.
    • This metric showed a significant correlation with established Riemannian distinctiveness metrics.
    • Demonstrated the metric's ability to inform about the discriminability and stability of EEG patterns.

    Conclusions:

    • Introduced a novel framework for co-adaptive BCI learning that integrates data adaptation with user skill assessment.
    • The SBA approach facilitates session-to-session adaptation and improves MI-BCI performance.
    • The proposed method empowers users with feedback on their MI modulation strategy, advancing user-centered BCI development.