Cleveland Clinic, Cleveland, OH, United States of America.
Case Western Reserve University, Cleveland, OH, United States of America.
J Neural Eng. 2021 Nov 2;18(5). doi: 10.1088/1741-2552/ac2f7a.
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.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 aof 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.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.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.
皮层内记录现在已经与手臂/手部肌肉的功能性电刺激 (FES) 相结合，以证明脊髓损伤后上肢功能的恢复。然而，对于从大脑解码的每个期望的肢体位置，有多种肌肉刺激水平的组合可以产生该位置。这项模拟研究的目的是探索在 FES 期间调节拮抗肌的共同激活程度如何影响到达性能和能量使用。通过共同收缩拮抗肌使肢体僵硬，使肢体更能抵抗扰动。最小化共同收缩可以节省能量并减少疲劳。先前通过 FES 进行的到达演示使用针对每个关节的固定经验推导查找表，该查找表定义了将肢体定位在大脑在每个时间步解码的期望关节角度的肌肉刺激水平。本研究通过模拟扩展了之前的工作：(a) 使用具有不同共同收缩水平的查找表而不是针对每个关节的单个固定查找表来测试使用控制手臂到达的可行性，(b) 使用仅从大脑解码的期望运动信息自动在这些不同的共同收缩表之间切换的简单函数进行优化，以及 (c) 比较在使用优化函数自动调节到达过程中的共同收缩与使用最佳固定共同收缩水平时的能量节省和运动性能。
我们的数据表明，通过使用我们的多表方法和简单函数来动态调节肢体刚度，并根据解码的末端速度及其导数来确定共同收缩水平，可以显著提高能量使用和/或运动性能。通过证明调节共同收缩可以在保持甚至提高运动性能的同时减少能量使用，本研究使脑控 FES 成为瘫痪后恢复到达的更可行选择。
