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    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Medical Imaging

    Background:

    • Simultaneous measurement of spontaneous mechanical activities in human musculature (SMAM) and their underlying electrical signals (sEMG) is challenging.
    • Mechanical muscle activity follows electrical neuromuscular activity with a delay of tens of milliseconds.
    • Accurate timing is crucial for correlating these signals during MRI.

    Purpose of the Study:

    • To develop a low-cost, standalone system for simultaneous surface electromyography (sEMG) measurements during diffusion-weighted magnetic resonance imaging (DW-MRI).
    • To implement real-time, model-based sEMG activity detection to control the MR imaging sequence.
    • To investigate the performance of a multilayer perceptron (MLP) with sequential forward selection (SFS) for sEMG detection.

    Main Methods:

    • Integration of a microcontroller system for real-time sEMG detection.
    • Utilizing a multilayer perceptron (MLP) with sequential forward selection (SFS) for signal analysis.
    • Employing five time-domain features for sEMG activity detection.

    Main Results:

    • The MLP model achieved an area under the curve (AUC) of 0.933 in detecting small sEMG activities.
    • The integrated system demonstrated fast, real-time sEMG detection with flexible trigger time settings.
    • A minimal time delay of 7.2±1.7 ms was observed between sEMG detection and MR sequence initiation.

    Conclusions:

    • The developed system enables simultaneous sEMG and DW-MRI acquisition with precise temporal control.
    • Real-time sEMG detection using MLP significantly enhances the study of spontaneous muscle contractions during MRI.
    • This approach offers a flexible and accurate method for investigating neuromuscular activity in conjunction with advanced imaging techniques.