Faculty, Staff and Student Publications

Language

English

Publication Date

5-6-2025

Journal

Journal of Neural Engineering

DOI

10.1088/1741-2552/adc9e3

PMID

40194524

PMCID

PMC12053836

PubMedCentral® Posted Date

5-6-2025

PubMedCentral® Full Text Version

Post-print

Abstract

Objective. Brain-controlled functional electrical stimulation (FES) of the upper limb has been used to restore arm function to paralyzed individuals in the lab. Able-bodied individuals naturally modulate limb stiffness throughout movements and in anticipation of perturbations. Our goal is to develop, via simulation, a framework for incorporating stiffness modulation into the currently-used ‘lookup-table-based’ FES control systems while addressing several practical issues: (1) optimizing stimulation across muscles with overlap in function, (2) coordinating stimulation across joints, and (3) minimizing errors due to fatigue. Our calibration process also needs to account for when current spread causes additional muscles to become activated. Approach. We developed an analytical framework for building a lookup-table-based FES controller and simulated the clinical process of calibrating and using the arm. A computational biomechanical model of a human paralyzed arm responding to stimulation was used for simulations with six muscles controlling the shoulder and elbow in the horizontal plane. Both joints had multiple muscles with overlapping functional effects, as well as biarticular muscles to reflect complex interactions between joints. Performance metrics were collected in silico, and real-time use was demonstrated with a Rhesus macaque using its cortical signals to control the computational arm model in real time.

Main results. By explicitly including stiffness as a definable degree of freedom in the lookup table, our analytical approach was able to achieve all our performance criteria. While using more empirical data during controller parameterization produced more accurate lookup tables, interpolation between sparsely sampled points (e.g. 20° angular intervals) still produced good results with median endpoint position errors of less than 1 cm—a range that should be easy to correct for with real-time visual feedback. Significance. Our simplified process for generating an effective FES controller now makes translating upper limb FES systems into mainstream clinical practice closer to reality.

Keywords

Humans, Animals, Macaca mulatta, Muscle, Skeletal, Arm, Computer Simulation, Electric Stimulation Therapy, Biomechanical Phenomena, functional electrical stimulation (FES), neuromuscular stimulation, arm movement, spinal cord injury, cocontraction, fatigue, brain–machine interfaces (BMI)

Published Open-Access

yes

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