Distributed processing of movement signaling

Improving reaching with functional electrical stimulation by incorporating stiffness modulation

Objective.Intracortical recordings have now been combined with functional electrical stimulation (FES) of arm/hand muscles to demonstrate restoration of upper-limb function after spinal cord injury. However, for each desired limb position decoded from the brain, there are multiple combinations of muscle stimulation levels that can produce that position. The objective of this simulation study is to explore how modulating the amount of coactivation of antagonist muscles during FES can impact reaching performance and energy usage. Stiffening the limb by cocontracting antagonist muscles makes the limb more resistant to perturbation. Minimizing cocontraction saves energy and reduces fatigue.Approach.Prior demonstrations of reaching via FES used a fixed empirically-derived lookup table for each joint that defined the muscle stimulation levels that would position the limb at the desired joint angle decoded from the brain at each timestep. This study expands on that previous work by using simulations to: (a) test the feasibility of controlling arm reaching using asuiteof lookup tables with varying levels of cocontraction instead of a single fixed lookup table for each joint, (b) optimize a simple function for automatically switching between these different cocontraction tables using only the desired kinematic information already being decoded from the brain, and (c) compare energy savings and movement performance when using the optimized function to automatically modulate cocontraction during reaching versus using the best fixed level of cocontraction.Main results.Our data suggests energy usage and/or movement performance can be significantly improved by dynamically modulating limb stiffness using our multi-table method and a simple function that determines cocontraction level based on decoded endpoint speed and its derivative.Significance.By demonstrating how modulating cocontraction can reduce energy usage while maintaining or even improving movement performance, this study makes brain-controlled FES a more viable option for restoration of reaching after paralysis.

Brain-Machine Interfaces: Lessons for Prosthetic Hand Control

Brain-machine interfaces (BMI) are being developed to restore upper limb function for persons with spinal cord injury or other motor degenerative conditions. BMI and implantable sensors for myoelectric prostheses directly extract information from the central or peripheral nervous system to provide users with high fidelity control of their prosthetic device. Control algorithms have been highly transferable between the 2 technologies but also face common issues. In this review of the current state of the art in each field, the authors point out similarities and differences between the 2 technologies that may guide the implementation of common solutions to these challenges.