Decoding a new neural-machine interface for control of artificial limbs
Decoding a new neural-machine interface for control of artificial limbs
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DOI：
10.1152/jn.00178.2007
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发表时间：
2007-11-01
影响因子：
2.5
通讯作者：
Kuiken, Todd A.
中科院分区：
文献类型：
--
作者：
Zhou, Ping;Lowery, Madeleine M.;Kuiken, Todd A.
在这项研究中提出了一个分析的电机控制信息内容提供了一个神经机接口（NMI）在四个科目。我们已经开发了一种新的神经肌肉修复术，称为靶向肌肉神经再支配（TMR），以改善截肢者的假肢功能。TMR涉及将残余的截肢神经转移到截肢者的无功能肌肉。神经支配的肌肉作为生物放大器的运动指令在截肢的神经和表面肌电图（EMG）可以用来提高控制的机器人arm. Although初步临床成功与TMR已被看好，自由度的机器人手臂，可以控制的数量受到限制的神经支配的肌肉部位的数量。在这项研究中，我们评估有多少控制信息可以从神经支配的肌肉使用高密度表面肌电信号电极阵列记录表面肌电信号的神经支配的肌肉。然后，我们应用模式分类技术的表面肌电信号。在16个预期手臂、手和手指/拇指运动的分类中实现了高准确性。所需的EMG通道和计算需求的初步分析表明，这些方法的临床可行性。这项研究表明，TMR结合模式识别技术有可能进一步改善假肢的功能。此外，研究结果表明，中央电机控制系统是能够引起复杂的传出命令失踪的肢体，在没有外周反馈和不重新训练的途径。
An analysis of the motor control information content made available with a neural-machine interface (NMI) in four subjects is presented in this study. We have developed a novel NMI-called targeted muscle reinnervation (TMR)-to improve the function of artificial arms for amputees. TMR involves transferring the residual amputated nerves to nonfunctional muscles in amputees. The reinnervated muscles act as biological amplifiers of motor commands in the amputated nerves and the surface electromyogram (EMG) can be used to enhance control of a robotic arm. Although initial clinical success with TMR has been promising, the number of degrees of freedom of the robotic arm that can be controlled has been limited by the number of reinnervated muscle sites. In this study we assess how much control information can be extracted from reinnervated muscles using high-density surface EMG electrode arrays to record surface EMG signals over the reinnervated muscles. We then applied pattern classification techniques to the surface EMG signals. High accuracy was achieved in the classification of 16 intended arm, hand, and finger/thumb movements. Preliminary analyses of the required number of EMG channels and computational demands demonstrate clinical feasibility of these methods. This study indicates that TMR combined with pattern-recognition techniques has the potential to further improve the function of prosthetic limbs. In addition, the results demonstrate that the central motor control system is capable of eliciting complex efferent commands for a missing limb, in the absence of peripheral feedback and without retraining of the pathways involved.