摘要翻译：
尽管多通道肌电图(EMG)数据集是自然存在的多向结构，但高阶张量分解很少用于肌肉活动分析。在这里，我们试图证明和讨论张量分解作为一个框架的潜力，以估计肌肉协同效应，从$3^{rd}$阶肌电张量堆叠重复多通道肌电图为几个任务。我们比较了两个最广泛的张量分解模型--平行因子分析(PARAFAC)和塔克--在手腕的三个主要自由度（DoFs）的肌肉协同分析中使用公开的第一个Ninapro数据库。此外，我们提出了一种基于张量分解幂的约束塔克分解(COSTD)方法来有效地提取协同效应。该方法是从两个生物力学相关的任务中提出的一种用于共享和任务特定协同估计的直接新方法。我们的方法与当前的标准方法进行了比较，该方法重复地将非负矩阵分解(NMF)应用于一系列运动。结果表明，与PARAFAC和Tucker方法相比，COSTD方法更适合于协同提取。此外，利用肌肉活动的多向结构，所提出的方法成功地识别了所有三个DoFs张量的共享和特定任务的协同作用。与常用的NMF不同，这些方法对任务重复信息的混乱有很强的鲁棒性。总之，我们演示了如何使用张量来表征肌肉活动，并开发了一种新的用于肌肉协同作用提取的COSTD方法，该方法可以用于共享和特定任务的协同作用识别。我们期望这项研究将为开发基于高阶技术的新型肌肉活动分析方法铺平道路。
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英文标题：
《Muscle Activity Analysis using Higher-Order Tensor Models: Application
  to Muscle Synergy Identification》
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作者：
Ahmed Ebied, Eli Kinney-lang, Loukianos Spyrou and Javier Escudero
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最新提交年份：
2019
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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：
  Higher-order tensor decompositions have hardly been used in muscle activity analysis despite multichannel electromyography (EMG) datasets naturally occurring as multi-way structures. Here, we seek to demonstrate and discuss the potential of tensor decompositions as a framework to estimate muscle synergies from $3^{rd}$-order EMG tensors built by stacking repetitions of multi-channel EMG for several tasks. We compare the two most widespread tensor decomposition models -- Parallel Factor Analysis (PARAFAC) and Tucker -- in muscle synergy analysis of the wrist's three main Degree of Freedoms (DoFs) using the public first Ninapro database. Furthermore, we proposed a constrained Tucker decomposition (consTD) method for efficient synergy extraction building on the power of tensor decompositions. This method is proposed as a direct novel approach for shared and task-specific synergy estimation from two biomechanically related tasks. Our approach is compared with the current standard approach of repetitively applying non-negative matrix factorisation (NMF) to a series of movements. The results show that the consTD method is suitable for synergy extraction compared to PARAFAC and Tucker. Moreover, exploiting the multi-way structure of muscle activity, the proposed methods successfully identified shared and task-specific synergies for all three DoFs tensors. These were found to be robust to disarrangement with regard to task-repetition information, unlike the commonly used NMF. In summary, we demonstrate how to use tensors to characterise muscle activity and develop a new consTD method for muscle synergy extraction that could be used for shared and task-specific synergies identification. We expect that this study will pave the way for the development of novel muscle activity analysis methods based on higher-order techniques.
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PDF链接：
https://arxiv.org/pdf/1806.01783