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

Area of Science:

• Neuroscience
• Biomedical Engineering
• Rehabilitation Science

Background:

• Spinal cord injury (SCI) significantly impacts motor function and quality of life.
• Transcutaneous spinal cord stimulation (tSCS) is a non-invasive neuromodulation technique for motor function restoration.
• Current tSCS methods, particularly single-electrode configurations, have limited muscle recruitment selectivity, hindering clinical application.

Purpose of the Study:

• To enhance the selectivity of multi-electrode tSCS configurations.
• To implement online spinal reflex detection and automated algorithms for personalized stimulation parameters.
• To enable selective activation of target muscle groups for improved gait rehabilitation.

Main Methods:

• Developed an automated protocol with online spinal reflex detection and muscle response analysis.
• Tested two multi-electrode configurations (midline and bilateral) in 14 healthy participants.
• Employed two algorithms: Ranking-Based Approach (RBA) and Selectivity-Driven Approach (SDA) for optimizing electrode position and stimulation amplitude.

Main Results:

• Both RBA and SDA enhanced rostrocaudal and ipsilateral selectivity in multi-electrode tSCS.
• SDA demonstrated superior suitability for selective muscle group recruitment by quantifying graded EMG responses.
• Results challenged assumptions on tSCS selectivity, revealing inter-subject variability in muscle recruitment patterns.

Conclusions:

• Automated algorithms and online reflex detection are crucial for optimizing tSCS, reducing manual calibration.
• The developed methods account for inter-subject variability, enabling more targeted neuromodulation.
• This approach facilitates personalized gait rehabilitation for individuals with SCI and other neurological conditions.