Structured modulations in high-density EMG patterns from a single muscle enable simultaneous control of natural and...

Identifying and exploiting independent neural inputs within a single muscle is a key step toward improving neuromuscular interfaces for motor augmentation and rehabilitation. Recent evidence suggests that motor neurons within the same pool can receive multiple, independent inputs, allowing flexible modulation of muscle activity. Harnessing this neural flexibility could enable the control of supplementary degrees of freedom (SuDoF) without interfering with the performance of natural motor tasks. However, current methods rely on motor unit decomposition, which is technically challenging and impractical for daily-life applications. This study introduces a decomposition-free approach to detect and quantify distinct neural inputs directly from high-density surface electromyography (HDsEMG) and assesses the feasibility of extracting from the modulation of these neural inputs a signal for simultaneous control of natural and SuDoF.

Fifteen participants performed reaching and orienting tasks in a virtual environment moving a cursor by generating isometric force at the hand (force control) and rotating the cursor by modulating the pattern of HDsEMG signals recorded using a 64-channel grid from the biceps brachii (SuDoF control). HDsEMG signals were represented as vectors in a multidimensional EMG space, and its deviation from a reference vector associated with pure force control was used to control the cursor orientation. Participants reached eight planar targets with a circular cursor using only force control and reached and matched the orientation of eight elliptical targets with an elliptical cursor using force and SuDoF control. Performance across conditions was analyzed using linear mixed-effects models.

Participants successfully reached targets in 89 ± 10% of force and SuDoF control trials, with cursor rotations exceeding those observed in force-only trials in 84 ± 31% of cases. Participants primarily modulated contraction amplitude but maintained distinct directional shifts in HDsEMG patterns, compatible with the modulation of different neural inputs within the motor neuron pool. However, these modulations may also partly reflect changes in overall muscle activation and recruitment.

This study presents a decomposition-free method to detect structured modulations in HDsEMG patterns within a single muscle. The observed modulations are compatible with the presence of distinct neural inputs and enabled concurrent control of natural and SuDoF, advancing neuromuscular interface design for assistive and rehabilitative technologies.