Abstract Optically pumped magnetometers (OPM) are quantum sensors that offer new possibilities to measure biomagnetic signals. In magnetomyography (MMG), compared to the current standard surface electromyography (EMG), OPM sensors offer the advantage of contactless measurements of muscle activity. However, little is known about the relative performance of OPM-MMG and EMG, e.g. in their ability to detect and classify finger movements. To address this, we recorded simultaneous OPM-MMG and EMG of finger flexor muscles for the discrimination of individual finger movements. Using a deep learning model for movement classification, we found that both sensor modalities were able to discriminate finger movements with above 89% accuracy. Furthermore, model predictions for the two sensor modalities showed high agreement in movement detection (85% agreement; Cohen’s kappa: 0.45). Our findings show that OPM sensors can be employed for reliable, contactless discrimination of finger movements and incentivize future applications of OPM in magnetomyography.

Discrimination of finger movements by magnetomyography with optically pumped magnetometers