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    Rejecting myoelectric control decisions based on confidence reduces errors but may also filter correct movements. User error remained constant, suggesting rejection primarily mitigates classifier errors, not user behavior changes.

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    Area of Science:

    • Biomedical Engineering
    • Rehabilitation Engineering
    • Human-Computer Interaction

    Background:

    • Myoelectric pattern recognition improves prosthetic control.
    • Rejecting low-confidence decisions enhances usability.
    • The impact of rejection on error mitigation versus user adaptation is unclear.

    Purpose of the Study:

    • To differentiate the effects of error mitigation and user adaptation in myoelectric control.
    • To quantify the influence of confidence thresholds on systemic and operator errors.
    • To assess the trade-off between error reduction and filtering of correct decisions.

    Main Methods:

    • 24 subjects performed real-time pattern recognition tasks.
    • Rejection was applied at seven confidence thresholds and in a no-rejection condition.
    • Errors were categorized as systemic (classifier) or operator (user).

    Main Results:

    • High rejection thresholds halved overall error rates.
    • Both systemic and operator errors were significantly reduced.
    • User-produced errors remained constant, indicating rejection primarily affects classifier errors.
    • Correct decisions were increasingly filtered at higher rejection thresholds.

    Conclusions:

    • Myoelectric control rejection effectively mitigates classifier errors.
    • Rejection does not significantly alter user error patterns.
    • Excessive rejection may hinder usability by filtering correct commands.
    • Experience level did not influence rejection effectiveness.