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    The automatic progressive FastICA peel-off (APFP) method successfully decomposes intramuscular electromyogram (EMG) signals. This technique accurately identifies motor units even with limited electrodes, enhancing EMG analysis.

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    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Signal Processing

    Background:

    • High-density surface electromyogram (EMG) decomposition commonly uses blind source separation.
    • Progressive FastICA peel-off (PFP) is a recent blind source separation method for surface EMG.
    • Intramuscular EMG decomposition presents unique challenges due to fewer electrodes.

    Purpose of the Study:

    • To explore a novel application of PFP for automatic decomposition of multi-channel intramuscular EMG signals.
    • To modify the APFP framework for increased decomposition yield with limited intramuscular electrodes.
    • To evaluate the accuracy and efficiency of the modified APFP for intramuscular EMG decomposition.

    Main Methods:

    • Utilized an open-access multi-channel intramuscular EMG dataset from the brachioradialis muscle.
    • Applied a modified automatic PFP (APFP) framework to decompose the intramuscular EMG signals.
    • Compared APFP decomposition results with manual identification using EMGLAB.

    Main Results:

    • Successfully decomposed 131 motor units from 10 intramuscular EMG signals using APFP.
    • 128 of the 131 motor units were validated through manual identification.
    • Achieved a high average matching rate of discharge instants at (98.71 ± 1.73)% for common motor units.

    Conclusions:

    • The APFP framework demonstrates high accuracy for automatic decomposition of multi-channel intramuscular EMG.
    • This method is effective even with a relatively small number of recording channels compared to high-density surface EMG.
    • APFP offers a promising tool for efficient and accurate intramuscular EMG analysis.