Influence of Selectivity Metric Choice on Motor Restoration With Neural Electrical Stimulation

Implantable neural devices can restore functional movement in patients with motor impairments through electrical stimulation. However, determining optimal stimulation parameters after the implantation remains a key challenge. Selectivity metrics, which quantify the balance between intended and unintended muscle activation, are commonly employed to guide parameter optimization. Each selectivity measure exhibits specific mathematical properties that affect their suitability as objective functions for optimization algorithms. In this study, we conducted a comparison of different selectivity definitions using computational modeling of the median nerve derived from human cadaveric histological data and implanted with two transverse intraneural multichannel electrodes (TIMEs). We evaluated four selectivity metrics and analyzed their effects on optimization outcomes. Our results show that the choice of the target selectivity measure influenced optimization performance, even when assessed using other selectivity definitions. Our findings underscore the importance of carefully selecting selectivity criteria to improve clinical optimization of neural stimulation protocols in patients with motor disabilities.