IEEE Trans Biomed Eng. 2023 Oct;70(10):2852-2862. doi: 10.1109/TBME.2023.3266575. Epub 2023 Sep 27.
Neural interfacing has played an essential role in advancing our understanding of fundamental movement neurophysiology and the development of human-machine interface. However, direct neural interfaces from brain and nerve recording are currently limited in clinical areas for their invasiveness and high selectivity. Here, we applied the surface electromyogram (EMG) in studying the neural control of movement and proposed a new non-invasive way of extracting neural drive to individual muscles. Sixteen subjects performed isometric contractions to complete six hand tasks. High-density surface EMG signals (256 channels in total) recorded from the forearm muscles were decomposed into motor unit firing trains. The location of each decomposed motor unit was represented by its center of gravity and was put into clustering for distinct muscle regions. All the motor units in the same cluster served as a muscle-specific motor pool from which individual muscle drive could be extracted directly. Moreover, we cross-validated the self-clustered muscle regions by magnetic resonance imaging (MRI) recorded from the subjects' forearms. All motor units that fall within the MRI region are considered correctly clustered. We achieved a clustering accuracy of 95.72% ± 4.01% for all subjects. We provided a new framework for collecting experimental muscle-specific drives and generalized the way of surface electrode placement without prior knowledge of the targeting muscle architecture.
神经接口在推进我们对基本运动神经生理学和人机接口的理解方面发挥了重要作用。然而，由于脑和神经记录的直接神经接口具有侵入性和高选择性，目前在临床领域受到限制。在这里，我们应用表面肌电图（EMG）研究运动的神经控制，并提出了一种新的非侵入性方法来提取单个肌肉的神经驱动。16 名受试者进行等长收缩以完成六个手部任务。从前臂肌肉记录的高密度表面 EMG 信号（总共 256 个通道）被分解为运动单位放电序列。每个分解的运动单位的位置由其重心表示，并放入聚类中以区分不同的肌肉区域。同一聚类中的所有运动单位都作为一个肌肉特异性运动池，可以直接从中提取单个肌肉驱动。此外，我们通过受试者前臂记录的磁共振成像（MRI）对自我聚类的肌肉区域进行了交叉验证。所有落在 MRI 区域内的运动单位都被认为是正确聚类的。我们实现了所有受试者的 95.72%±4.01%的聚类准确率。我们提供了一种新的实验性肌肉特异性驱动收集框架，并推广了表面电极放置的方法，而无需事先了解目标肌肉结构。
