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Exploring the Relationship Between EMG Feature Space Characteristics and Control Performance in Machine Learning

Andreas W Franzke, Morten B Kristoffersen, Vinay Jayaram

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |October 9, 2020
    PubMed
    Summary
    This summary is machine-generated.

    Machine learning control performance improves with training, but common Electromyography (EMG) pattern metrics like separability do not change and show little correlation with performance gains.

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    Area of Science:

    • Biomedical Engineering
    • Rehabilitation Engineering
    • Human-Computer Interaction

    Background:

    • Myoelectric machine learning (ML) control performance typically improves with user training.
    • The underlying factors driving these performance enhancements in Electromyography (EMG) patterns remain poorly understood.
    • Existing hypotheses suggest changes in EMG pattern characteristics, such as separability or repeatability, but empirical evidence is limited.

    Purpose of the Study:

    • To investigate the relationship between common EMG feature space metrics (separability, variability, repeatability) and the performance of ML myoelectric control.
    • To determine if changes in these EMG metrics correlate with improvements in both offline and real-time control performance during a learning task.
    • To assess the predictability of real-time control performance based on these EMG metrics.

    Main Methods:

    • Twenty able-bodied participants underwent 15 training blocks over 5 days to learn ML myoelectric control in a virtual environment.
    • Offline and real-time control performance were assessed, alongside changes in three EMG metrics: separability, variability, and repeatability.
    • Correlation analyses were performed between EMG metrics and performance, and L2-regularized linear regression was used to predict real-time performance.

    Main Results:

    • Real-time control performance significantly improved with training.
    • No significant changes were observed in offline performance or any of the assessed EMG metrics (separability, variability, repeatability).
    • A very low correlation was found between separability and real-time performance; other metrics showed no correlation. Real-time performance was not predictable from the EMG metrics.

    Conclusions:

    • The three common EMG feature space metrics (separability, variability, repeatability) do not appear to be directly related to real-time performance improvements in ML myoelectric control.
    • Performance gains during myoelectric control training may be driven by factors other than the investigated EMG pattern characteristics.
    • Further research is needed to identify the specific factors that govern performance improvements in myoelectric control systems.