Background: Significant progress has been made towards the clinical application of human-machine interfaces (HMIs) based on elec-tromyographic (EMG) pattern recognition for various rehabilitation purposes. Making this technology practical and available to pati-ents with motor deficits requires overcoming real-world challenges, such as physical and physiological changes, that result in vari-ations in EMG signals and systems that are unreliable for long-term use. In this study, we aimed to address these challenges by investigating the stability of time-domain EMG features during changes in the EMG signals and identifying the feature sets that would provide the most robust EMG pattern recognition. Methods: Variations in EMG signals were introduced during physical experi-ments. We identified three disturbances that commonly affect EMG signals: EMG electrode location shift, variation in muscle contrac-tion effort, and muscle fatigue. The impact of these disturbances on individual features and combined feature sets was quantified by changes in classification performance. The robustness of feature sets was evaluated by a stability index developed in this study. Results: Muscle fatigue had the smallest effect on the studied EMG features, while electrode location shift and varying effort le-vel significantly reduced the classification accuracy for most of the features. Under these disturbances, the most stable EMG fea-ture set with combination of four features produced at least 16.0% higher classification accuracy than the least stable set. EMG autoregression coefficients and cepstrum coefficients showed the most robust classification performance of all studied time-domain features. Conclusions: Selecting appropriate EMG feature combinations can overcome the impact of the studied disturbances on EMG pattern classification to a certain extent; however, this simple solution is still inadequate. Stabilizing electrode contact loca-tions and developing effective classifier training strategies are suggested to further improve the robustness of HMIs based on EMG pattern recognition.

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