Synergistic Voluntary Muscle Signal Characteristic Analysis and Comparison of OPM-MMG and sEMG

Optically pumped magnetometers (OPMs) are quantum sensors enabling novel biomagnetic measurements. Integrating OPM-based magnetomyography (MMG) with conventional surface electromyography (sEMG) offers enhanced functional assessment of voluntary muscle activity (VMA), with promising applications in human-machine interaction, neuromuscular diagnosis, and rehabilitation. However, the synergistic spontaneous muscle signals exhibit highly complex interference phases, while both sEMG and MMG are characterized by nonstationary randomness and temporal variability. These properties pose significant challenges to the quantitative comparison and consistency validation of sEMG and MMG signal features. In this study, for the first time, a high-precision, real-time synchronous measurement system combining sEMG and OPM-MMG was proposed to capture compound muscle action potentials (CMAPs) produced by synergistic skeletal muscle activation. In the proposed system, the magnetically compatible electrode configuration and the modulation-based time-delay compensation technique ensure spatiotemporally aligned conditions for multimodal signal acquisition. Moreover, the multivariate feature analysis method optimized by the Thresholded Gaussian Filtering-Amplitude Probability Distribution Function (TGF-APDF) overcomes the inherent noise and complexity associated with direct comparisons of raw signals. Experimental results reveal a minimal latency difference of 0.0018 (±0.0046) s and amplitude probability distribution deviation under 5% ( p < 0.01 ) between sEMG and MMG. Meanwhile, significant differences and variability are observed in dominant modal distributions across channels. These findings demonstrate complementary strengths of the two modalities: sEMG excels in temporal resolution, while MMG provides superior spatial resolution. This work advances the multimodal assessment of muscle function, offering new insights for neuromuscular disease evaluation and motivating future applications leveraging OPM-MMG technology.