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Related Experiment Video

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RAP2G: Relation-Aware Progressive Pseudo-label Generation for Cross-subject MI-EEG Recognition.

Xinhui Li, Xun Song, Qiangyu Ma

    IEEE Transactions on Bio-Medical Engineering
    |July 14, 2026
    PubMed
    Summary

    We developed a new method for motor imagery electroencephalography (MI-EEG) classification that improves brain-computer interface (BCI) accuracy across individuals. This relation-aware approach enhances label-free adaptation for more reliable BCI systems.

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    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

    Published on: October 24, 2012

    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Machine Learning

    Background:

    • Brain-computer interfaces (BCIs) rely on motor imagery electroencephalography (MI-EEG) classification.
    • Inter-subject variability poses a significant challenge for MI-EEG accuracy.
    • Unsupervised domain adaptation (UDA) methods, using pseudo-labeling, attempt to address this but have limitations.

    Purpose of the Study:

    • To overcome limitations in existing pseudo-labeling techniques for cross-subject MI-EEG classification.
    • To develop a novel UDA framework for robust MI-EEG classification across diverse individuals.
    • To enhance the reliability and practicality of BCI systems.

    Main Methods:

    • Proposed the relation-aware progressive pseudo-label generation (RAP2G) method, a novel UDA framework.
    • Combined Optimal Transport (OT) with structure-aware regularization and dynamic pseudo-label selection.
    • Leveraged feature similarity and OT confidence for adaptive pseudo-label generation and selection.

    Main Results:

    • RAP2G demonstrated superior performance compared to state-of-the-art UDA techniques and baselines.
    • Ablation studies validated the effectiveness of the structure-aware component.
    • Visualizations revealed improved feature separability and attention maps consistent with motor cortex organization.

    Conclusions:

    • RAP2G offers an effective solution for robust cross-subject MI-EEG classification.
    • The method enhances label-free adaptation, supporting more reliable BCI systems for biomedical applications.