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Updated: May 17, 2026

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Mapper-Based Topological EEG Modeling for Task Representation in Robot-Assisted Surgery.

Yushi Liu, Yan Yan, Xiaoyu Li

    IEEE Transactions on Bio-Medical Engineering
    |May 15, 2026
    PubMed
    Summary
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    A new Mapper-based topological framework models neural states during robot-assisted surgery (RAS) using electroencephalography (EEG). This approach enhances task discrimination and provides interpretable analysis for personalized surgical training.

    Area of Science:

    • Neuroscience
    • Robotics
    • Data Science

    Background:

    • Robot-assisted surgery (RAS) needs objective task analysis frameworks.
    • Electroencephalography (EEG) captures surgical behavior but lacks detailed neural representation insights.
    • Existing methods offer limited understanding of task-related neural organization.

    Purpose of the Study:

    • Introduce a Mapper-based topological EEG modeling framework for RAS.
    • Characterize neural state spaces and transitions during surgical tasks.
    • Enhance interpretability and analysis of EEG data in RAS.

    Main Methods:

    • Utilized EEG recordings from an RAS dataset.
    • Extracted phase-locking value (PLV) connectivity features across frequency bands.

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  • Applied Mapper algorithm for topological modeling of neural states.
  • Incorporated persistent homology and BrainNetCNN for comparison.

    • Main Results:

    • The framework consistently identified coherent, separable task-related neural structures.
    • Frequency-dependent neural topology improved task discrimination, especially in higher bands.
    • Achieved 94.78% accuracy in six-class task modeling.
    • Mapper demonstrated superior structural consistency and organization compared to baselines.
    • Significant inter-subject variability in neural topology was observed.

    Conclusions:

    • Mapper-based topological modeling offers an interpretable foundation for RAS task representation.
    • The framework enables personalized EEG-based analysis in RAS.
    • Potential for individualized monitoring of surgical task execution and training analysis.