Influence of spatio-temporal filtering on hand kinematics estimation from high-density EMG signals - Simpetru, Raul; Cnejevici, Vlad; Farina, Dario; Del Vecchio, Alessandro (Journal of Neural Engineering, 2024)
Objective. Surface electromyography (sEMG) is a non-invasive technique that records the electrical signals generated by muscles through electrodes placed on the skin. sEMG is the state-of-the-art method used to control active upper limb prostheses because of the association between its amplitude and the neural drive sent from the spinal cord to muscles. However, accurately estimating the kinematics of a freely moving human hand using sEMG from extrinsic hand muscles remains a challenge. Deep learning has been recently successfully applied to this problem by mapping raw sEMG signals into kinematics. Nonetheless, the optimal number of EMG signals and the type of pre-processing that would maximize performance have not been investigated yet. Approach. Here, we analyze the impact of these factors on the accuracy in kinematics estimates. For this purpose, we processed monopolar sEMG signals that were originally recorded from 320 electrodes over the forearm muscles of 13 subjects. We used a previously published deep learning method that can map the kinematics of the human hand with real-time resolution. Main results. While myocontrol algorithms essentially use the temporal envelope of the EMG signal as the only EMG feature, we show that our approach requires the full bandwidth of the signal in the temporal domain for accurate estimates. Spatial filtering however, had a smaller impact and low-order spatial filters may be suitable. Moreover, reducing the number of channels by ablation resulted in large performance losses. The highest accuracy was reached with the highest number of available sensors (n = 320). Importantly and unexpected, our results suggest that increasing the number of channels above those used in this study may further enhance the accuracy in predicting the kinematics of the human hand. Significance. We conclude that full bandwidth high-density EMG systems of hundreds of electrodes are needed for accurate kinematic estimates of the human hand.<br

MyoGestic: EMG interfacing framework for decoding multiple spared motor dimensions in individuals with neural lesions - Simpetru, Raul; Braun, Dominik; Simon, Arndt; März, Michael; Cnejevici, Vlad; Souza de Oliveira, Daniela; Weber, Nico; Walter, Jonas; Franke, Jörg; Höglinger, Daniel; Prahm, Cosima; Ponfick, Matthias; et al. (Science Advances, 2025)
Restoring motor function in individuals with spinal cord injuries (SCIs), strokes, or amputations is a crucial challenge. Recent studies show that spared motor neurons can still be voluntarily controlled using surface electromyography (EMG), even without visible movement. To harness these signals, we developed a wireless, high-density EMG bracelet and a software framework, MyoGestic. Our system enables rapid adaptation of machine learning models to users’ needs, allowing real-time decoding of spared motor dimensions. In our study, we successfully decoded motor intent from two participants with traumatic SCI, two with spinal stroke, and three with amputations in real time, achieving multiple controllable motor dimensions within minutes. The decoded neural signals could control a digitally rendered hand, an orthosis, a prosthesis, or a two-dimensional cursor. MyoGestic’s participant-centered approach allows a collaborative and iterative development of myocontrol algorithms, bridging the gap between researcher and participant, to advance intuitive EMG interfaces for neural lesion