肌肉协同作用的基质因子分解方法评价 萃取

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摘要翻译：
肌肉协同概念提供了一个被广泛接受的范式来打破运动控制的复杂性。为了确定协同作用，不同的基质因子分解技术已被用于一系列领域，如假体控制、生物力学和临床研究。然而，这些矩阵分解技术的相关性仍有待讨论，因为根本没有关于潜在协同作用的事实。在这里，我们评估因式分解技术，并调查影响估计协同效应质量的因素。比较了常用的矩阵分解方法：主成分分析(PCA)、独立成分分析(ICA)、非负矩阵分解(NMF)和二阶盲辨识(SOBI)。公开可用的真实数据被用来评估在手腕运动分类中每种因子分解方法提取的协同作用。利用合成数据集来研究肌肉协同效应稀疏性、噪声水平和通道数对提取的协同效应的影响。结果表明，稀疏协同模型和更多的渠道数量将导致更好的估计协同效应。在不降维的情况下，SOBI比其他因式分解方法显示出更好的结果。这表明，当电极数量有限时，SOBI将是一种替代方案，但在这种情况下，它的性能仍然很差。另外，当通道数大于协同数时，NMF的性能最好。因此，NMF是提取肌肉协同作用的最佳方法。
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英文标题：
《Evaluation of matrix factorisation approaches for muscle synergy
  extraction》
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作者：
Ahmed Ebied, Eli Kinney-Lang, Loukianos Spyrou, Javier Escudero
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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一级分类：Quantitative Biology        数量生物学
二级分类：Quantitative Methods        定量方法
分类描述：All experimental, numerical, statistical and mathematical contributions of value to biology
对生物学价值的所有实验、数值、统计和数学贡献
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英文摘要：
  The muscle synergy concept provides a widely-accepted paradigm to break down the complexity of motor control. In order to identify the synergies, different matrix factorisation techniques have been used in a repertoire of fields such as prosthesis control and biomechanical and clinical studies. However, the relevance of these matrix factorisation techniques is still open for discussion since there is no ground truth for the underlying synergies. Here, we evaluate factorisation techniques and investigate the factors that affect the quality of estimated synergies. We compared commonly used matrix factorisation methods: Principal component analysis (PCA), Independent component analysis (ICA), Non-negative matrix factorization (NMF) and second-order blind identification (SOBI). Publicly available real data were used to assess the synergies extracted by each factorisation method in the classification of wrist movements. Synthetic datasets were utilised to explore the effect of muscle synergy sparsity, level of noise and number of channels on the extracted synergies. Results suggest that the sparse synergy model and a higher number of channels would result in better-estimated synergies. Without dimensionality reduction, SOBI showed better results than other factorisation methods. This suggests that SOBI would be an alternative when a limited number of electrodes is available but its performance was still poor in that case. Otherwise, NMF had the best performance when the number of channels was higher than the number of synergies. Therefore, NMF would be the best method for muscle synergy extraction.
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PDF链接：
https://arxiv.org/pdf/1806.01785

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关键词：Applications Quantitative Optimization Experimental Contribution 因式分解 协同效应 matrix muscle 研究