Improving muscle recruitment via multi-electrode transcutaneous spinal cord stimulation using automated selectivity-driven algorithms.

Spinal cord injury (SCI) severely impairs motor function and quality of life. Transcutaneous spinal cord stimulation (tSCS) has emerged as a promising non-invasive neuromodulation technique to restore voluntary motor function by engaging spinal circuits below the lesion. While standard tonic tSCS with a single electrode at T11-T12 offers limited gait-specific selectivity, recent studies show that multi-electrode configurations can recruit better proximal and distal muscles on the ipsilateral side. However, clinical translation of such approaches is still limited due to individual variability and the need for time-consuming calibration procedures that rely on manual electrode placement and offline analysis. We aim to enhance the selectivity of tSCS in multi-electrode configurations and to implement online spinal reflex detection and automated algorithms for personalizing stimulation parameters, enabling selective activation of target muscle groups. We propose an automated protocol with online spinal reflex detection and muscle response analysis and developed two algorithms based on near-instantaneously generated online data to determine the optimal electrode position and stimulation amplitude to maximize the selective recruitment of target muscle groups. The approach was tested in