Marius Oßwald

The paper examines how motor unit recruitment and firing rates change with different hand tasks, showing task-dependent variations in neural drive distribution.

CRD improves MU yield from HD-sEMG by decomposing residual signals, identifying previously undetected low-amplitude MUs across multiple datasets.

The paper investigates motor unit control during hand movements, finding that synergistic grasps are controlled non-synergistically by distinct motor units.

This paper develops a deep learning method to predict hand kinematics and forces from EMG signals, showing accurate prediction of unseen movements and learned anatomical neural features.