Neuromuscular learning and control of auricular muscles for human–machine interfaces

The auricular muscle (AM) is a promising yet understudied candidate for human–machine interfaces (HMIs) with the potential to improve motor functions and expand user capabilities. This study investigates the potential of AM-based control for motor augmentation. We conducted an in-depth neural and behavioral assessment to evaluate the feasibility and effectiveness of AM-based control. Over ten sessions, eight participants engaged in biofeedback and cursor control tasks designed to refine AM contractions and enhance independent muscle control. Our structured training protocol, which divided tasks into simpler subcomponents, facilitated de novo motor learning by combining real-time biofeedback with progressive control tasks. Participants achieved success rates of 86.67 ± 10.00% in coordination tasks and developed effective cursor control strategies while managing concurrent cognitive demands, reflecting their ability to handle increased cognitive loads without compromising performance. Corticomuscular coherence (CMC) analyses indicated a progressive increase in connectivity between the primary motor cortex and the AM, accompanied by a reduction in motor preparation correlates as evidenced by the beta rhythm’s event-related desynchronization (ERD). These findings on CMC and beta ERD support a functional adaptation of the AM for HMI use, demonstrating the potential of AM-based HMIs to augment motor capabilities and provide a new approach to human–machine interfacing.