Automated Selectivity-Driven Algorithm for Transcutaneous Spinal Cord Stimulation

Spinal cord injury (SCI) impairs motor function and quality of life. Transcutaneous Spinal Cord Stimulation (tSCS) is a non-invasive method to restore motor function by activating spinal circuits below the lesion. However, its effectiveness is limited by individual variability, reliance on manual electrode placement, and offline muscle analysis. To address these challenges, we developed an online method for detecting spinal reflexes and muscle responses using two automated algorithms: the Ranking-Based Approach (binary method) and the Automated Selectivity-Driven Approach (leveraging a selectivity index), both designed to optimize electrode placement and stimulation amplitude for precise activation of proximal or distal muscles. In a study with six healthy participants in the supine position, the posterior root muscle test was performed automatically using tSCS across three rostrocaudal spinal electrodes. Our findings reinforce the evidence of the rostrocaudal tSCS selectivity, with rostral electrodes activating proximal muscles and caudal ones targeting distal muscles. Both algorithms identified optimal electrode positions and stimulation amplitudes, enhancing tSCS selectivity for lower-limb muscles. Our results suggest that the Automated SelectivityDriven Approach is more appropriate for increasing selectivity for targeted muscle recruitment. These results highlight the potential of automated methods to improve tSCS selectivity for SCI rehabilitation and other conditions.