Matrix factorization algorithms for the identification of muscle synergies: Evaluation on simulated and experimental data sets
Matrix factorization algorithms for the identification of muscle synergies: Evaluation on simulated and experimental data sets
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DOI：
10.1152/jn.00222.2005
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发表时间：
2006-04-01
影响因子：
2.5
通讯作者：
d'Avella, A
中科院分区：
文献类型：
--
作者：
Tresch, MC;Cheung, VCK;d'Avella, A
最近的几项研究使用矩阵分解算法来评估行为可能是通过少数肌肉协同作用的组合产生的假设。虽然这些研究的基本结论基本一致，但它们使用了一系列不同的算法，这使得它们的解释和整合变得困难。因此，我们比较了这些不同的算法在模拟和实验数据集上的性能。我们专注于这些算法识别数据集背后的协同作用集的能力。所有数据集均由非负值组成，反映了肌肉激活模式的非负数据。我们发现，主成分分析（PCA）的性能一般低于其他算法在识别肌肉协同作用。方差最大旋转的因子分析（FA）优于PCA，并且通常与独立成分分析（伊卡）和非负矩阵因子分解（NMF）处于相同的水平。伊卡表现得很好的数据集上损坏的恒定方差高斯噪声，但受损的数据集与信号依赖性噪声和协同激活系数相关时。非负矩阵分解（NMF）在具有信号依赖噪声的数据集上的表现与伊卡和FA相似，并且在数据集上通常是稳健的。最好的算法是伊卡应用到PCA定义的子空间（ICAPCA）和一个版本的概率伊卡与非负约束（pICA）。我们还评估了一些常用的标准，以确定数据集的协同效应的数量，发现只有基于因子分析的似然比确定了在某些情况下具有信号依赖噪声的数据集的协同效应的正确数量。然后，我们提出了一个特设程序，发现它能够在大量情况下确定正确的数字。最后，我们将这些方法应用到实验获得的数据集。性能最好的算法（FA，伊卡，NMF，ICAPCA，pICA）确定的协同作用非常相似。基于这些结果，我们讨论了使用因式分解算法来分析肌肉激活模式的指导方针。更一般地说，几种算法识别模拟数据中正确的肌肉协同作用和激活系数的能力，以及应用于生理数据集时的一致性，表明特定算法发现的肌肉协同作用不是该算法的伪影，而是反映了行为背后的肌肉激活模式组织的基本方面。
Several recent studies have used matrix factorization algorithms to assess the hypothesis that behaviors might be produced through the combination of a small number of muscle synergies. Although generally agreeing in their basic conclusions, these studies have used a range of different algorithms, making their interpretation and integration difficult. We therefore compared the performance of these different algorithms on both simulated and experimental data sets. We focused on the ability of these algorithms to identify the set of synergies underlying a data set. All data sets consisted of nonnegative values, reflecting the nonnegative data of muscle activation patterns. We found that the performance of principal component analysis (PCA) was generally lower than that of the other algorithms in identifying muscle synergies. Factor analysis ( FA) with varimax rotation was better than PCA, and was generally at the same levels as independent component analysis (ICA) and nonnegative matrix factorization (NMF). ICA performed very well on data sets corrupted by constant variance Gaussian noise, but was impaired on data sets with signal-dependent noise and when synergy activation coefficients were correlated. Nonnegative matrix factorization ( NMF) performed similarly to ICA and FA on data sets with signal-dependent noise and was generally robust across data sets. The best algorithms were ICA applied to the subspace defined by PCA (ICAPCA) and a version of probabilistic ICA with nonnegativity constraints ( pICA). We also evaluated some commonly used criteria to identify the number of synergies underlying a data set, finding that only likelihood ratios based on factor analysis identified the correct number of synergies for data sets with signal-dependent noise in some cases. We then proposed an ad hoc procedure, finding that it was able to identify the correct number in a larger number of cases. Finally, we applied these methods to an experimentally obtained data set. The best performing algorithms ( FA, ICA, NMF, ICAPCA, pICA) identified synergies very similar to one another. Based on these results, we discuss guidelines for using factorization algorithms to analyze muscle activation patterns. More generally, the ability of several algorithms to identify the correct muscle synergies and activation coefficients in simulated data, combined with their consistency when applied to physiological data sets, suggests that the muscle synergies found by a particular algorithm are not an artifact of that algorithm, but reflect basic aspects of the organization of muscle activation patterns underlying behaviors.