IEEE Trans Neural Syst Rehabil Eng. 2014 May;22(3):623-33. doi: 10.1109/TNSRE.2013.2282898. Epub 2013 Oct 10.
Previous research proposed the extraction of myoelectric control signals by linear factorization of multi-channel electromyogram (EMG) recordings from forearm muscles. This paper further analyses the theoretical basis for dimensionality reduction in high-density EMG signals from forearm muscles. Moreover, it shows that the factorization of muscular activation patterns in weights and activation signals by non-negative matrix factorization (NMF) is robust with respect to the channel configuration from where the EMG signals are obtained. High-density surface EMG signals were recorded from the forearm muscles of six individuals. Weights and activation signals extracted offline from 10 channel configurations with varying channel numbers (6, 8, 16, 192 channels) were highly similar. Additionally, the method proved to be robust against electrode shifts in both transversal and longitudinal direction with respect to the muscle fibers. In a second experiment, six subjects directly used the activation signals extracted from high-density EMG for online goal-directed control tasks involving simultaneous and proportional control of two degrees-of-freedom of the wrist. The synergy weights for this control task were extracted from a reference configuration and activation signals were calculated online from the reference configuration as well as from the two shifted configurations, simulating electrode shift. Despite the electrode shift, the task completion rate, task completion time, and execution efficiency were generally not statistically different among electrode configurations. Online performances were also mostly similar when using either 6, 8, or 16 EMG channels. The robustness of the method to the number and location of channels, proved both offline and online, indicates that EMG signals recorded from forearm muscles can be approximated as linear instantaneous mixtures of activation signals and justifies the use of linear factorization algorithms for extracting, in a minimally supervised way, control signals for simultaneous multi-degree of freedom prosthesis control.
先前的研究提出通过线性分解前臂肌肉多通道肌电图 (EMG) 记录来提取肌电控制信号。本文进一步分析了从前臂肌肉高密度 EMG 信号中进行降维的理论基础。此外，它表明，通过非负矩阵分解 (NMF) 对肌肉激活模式的权重和激活信号进行分解对于从获取 EMG 信号的通道配置具有鲁棒性。从六个人的前臂肌肉记录了高密度表面 EMG 信号。从具有不同通道数（6、8、16、192 个通道）的 10 种通道配置中离线提取的权重和激活信号非常相似。此外，该方法在相对于肌肉纤维的横向和纵向电极移位方面被证明具有鲁棒性。在第二个实验中，六位受试者直接使用从高密度 EMG 中提取的激活信号进行在线目标导向控制任务，涉及手腕两个自由度的同时和比例控制。参考配置中提取了用于此控制任务的协同权重，并在线从参考配置以及两个移位配置计算激活信号，模拟电极移位。尽管存在电极移位，但电极配置之间的任务完成率、任务完成时间和执行效率在统计学上并没有显著差异。当使用 6、8 或 16 个 EMG 通道时，在线性能也大多相似。该方法对通道数量和位置的鲁棒性，无论是离线还是在线，都表明从前臂肌肉记录的 EMG 信号可以近似为激活信号的线性瞬时混合，并证明了在线性能也大多相似，使用 6、8 或 16 个 EMG 通道时，在线性能也大多相似。使用线性分解算法以最小监督的方式提取用于同时多自由度假肢控制的控制信号是合理的。
