IEEE Trans Neural Syst Rehabil Eng. 2017 Nov;25(11):2035-2045. doi: 10.1109/TNSRE.2017.2700890. Epub 2017 May 3.
We propose a novel decomposition method for electromyographic signals based on blind source separation. Using the cyclostationary properties of motor unit action potential trains (MUAPt), it is shown that the MUAPt can be decomposed by joint diagonalization of the cyclic spatial correlation matrix of the observations. After modeling the source signals, we provide the proof of orthogonality of the sources and of their delayed versions in a cyclostationary context. We tested the proposed method on simulated signals and showed that it can decompose up to six sources with a probability of correct detection and classification >95%, using only eight recording sites. Moreover, we tested the method on experimental multi-channel signals recorded with thin-film intramuscular electrodes, with a total of 32 recording sites. The rate of agreement of the decomposed MUAPt with those obtained by an expert using a validated tool for decomposition was >93%.
我们提出了一种基于盲源分离的肌电信号分解新方法。利用运动单位动作电位序列（MUAPt）的循环平稳特性，表明可以通过对观测的循环空间相关矩阵进行联合对角化来分解 MUAPt。在对源信号进行建模后，我们在循环平稳的情况下提供了源及其延迟版本正交性的证明。我们在模拟信号上测试了所提出的方法，结果表明，使用仅 8 个记录电极，它可以以 >95%的概率正确检测和分类多达 6 个源。此外，我们在使用薄膜肌内电极记录的实验多通道信号上测试了该方法，总共有 32 个记录电极。与专家使用经过验证的分解工具获得的分解 MUAPt 的一致性率>93%。
