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

TL;DR

This study improves non-invasive spinal cord stimulation by using automated algorithms to better target specific muscles, helping people with spinal injuries regain motor control.

Contribution

The study introduces automated algorithms for real-time spinal reflex detection and personalized stimulation in multi-electrode tSCS.

Findings

Automated algorithms enhance rostrocaudal and ipsilateral muscle selectivity in tSCS.

The selectivity-driven approach (SDA) is more effective for targeted muscle recruitment than the ranking-based approach (RBA).

Results challenge assumptions about tSCS selectivity, showing variability in electrode effectiveness across participants.

Abstract

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 14 healthy participants in the supine position using two distinct multi-electrode configurations: midline configuration employs three electrodes aligned rostrocaudally along the spinal midline to target proximal, distal, and all lower limb muscle groups and bilateral configuration employs six electrodes, with three electrodes positioned rostrocaudally and symmetrically on each side of the spinal midline to target six muscle groups (rostrocaudal and ipsilateral). Both setups integrated an automated posterior root-muscle reflex protocol with online spinal reflex detection. Electromyography (EMG) data recorded during stimulation were processed by two independent algorithms: (1) the ranking-based approach (RBA), which applies rule-based hierarchical criteria to rank electrodes based on spinal reflex responses, and (2) selectivity-driven approach (SDA), which computes a selectivity index to quantitatively assess muscle activity. For each target muscle group, the output of the algorithms is the selection of the optimal electrode and stimulation amplitude that achieves the most selective recruitment. We found that both developed approaches contribute to enhancing the rostrocaudal and ipsilateral selectivity in multi-electrode tSCS. We suggest that SDA is more suitable for selectively recruiting target muscle groups, as it quantifies selectivity based on graded EMG responses, while the RBA is well-suited for rapid, generalized applications, such as the conventional single-electrode tSCS to maximize overall muscle activation. Furthermore, our results challenge common assumptions about tSCS selectivity, including rostrocaudal recruitment of proximal/distal muscles and ipsilateral activation. Indeed, in the midline configuration, 9/13 participants showed greater recruitment with the T11–T12 electrode; when in the bilateral configuration, 5/11 had stronger contralateral leg activation in at least one of the electrodes, possibly due to anatomical variability of the spinal cord. These deviations highlight the value of online spinal reflex detection and automated algorithms for optimizing single- and multi-electrode tSCS, reducing reliance on manual electrode placement and accounting for inter-subject variability, thereby enabling more targeted neuromodulation and personalized gait rehabilitation for SCI and other neurological conditions.

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