神经假体
肌电图
脑-机接口
信号(编程语言)
计算机科学
生物医学工程
电极阵列
电动机控制
外围设备
接口(物质)
传出的
抓住
残余物
假手
物理医学与康复
电极
人工智能
医学
解剖
化学
传入的
物理化学
气泡
最大气泡压力法
并行计算
程序设计语言
操作系统
精神科
算法
脑电图
作者
Philip Vu,Alex K. Vaskov,Christina Lee,Ritvik R Jillala,Dylan Wallace,Alicia J. Davis,Theodore A. Kung,Stephen W.P. Kemp,Deanna H. Gates,Cynthia A. Chestek,Paul S. Cederna
出处
期刊:Journal of Neural Engineering
[IOP Publishing]
日期:2023-04-01
卷期号:20 (2): 026039-026039
被引量:12
标识
DOI:10.1088/1741-2552/accb0c
摘要
Abstract Objective. Extracting signals directly from the motor system poses challenges in obtaining both high amplitude and sustainable signals for upper-limb neuroprosthetic control. To translate neural interfaces into the clinical space, these interfaces must provide consistent signals and prosthetic performance. Approach. Previously, we have demonstrated that the Regenerative Peripheral Nerve Interface (RPNI) is a biologically stable, bioamplifier of efferent motor action potentials. Here, we assessed the signal reliability from electrodes surgically implanted in RPNIs and residual innervated muscles in humans for long-term prosthetic control. Main results. RPNI signal quality, measured as signal-to-noise ratio, remained greater than 15 for up to 276 and 1054 d in participant 1 (P1), and participant 2 (P2), respectively. Electromyography from both RPNIs and residual muscles was used to decode finger and grasp movements. Though signal amplitude varied between sessions, P2 maintained real-time prosthetic performance above 94% accuracy for 604 d without recalibration. Additionally, P2 completed a real-world multi-sequence coffee task with 99% accuracy for 611 d without recalibration. Significance. This study demonstrates the potential of RPNIs and implanted EMG electrodes as a long-term interface for enhanced prosthetic control.
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