Graph-Variate Signal Analysis
Graph-Variate Signal Analysis
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DOI：
10.1109/tsp.2018.2881658
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发表时间：
2017-03
影响因子：
5.4
通讯作者：
Keith M. Smith;Loukianos Spyrou;J. Escudero
中科院分区：
文献类型：
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作者：
Keith M. Smith;Loukianos Spyrou;J. Escudero
在多变量信号分析中绘制图表正在成为理解不同地点记录的活动相互依赖性的标准方法。在这个方向上的新的研究前沿包括如何评估信号活动的动态变化的重要问题。我们通过定义图形变量信号及其分析方法，以一种新的方式解决这个问题。本质上，图变量信号分析利用可靠的连接信息的图来过滤多变量信号的瞬时双变量函数。这开辟了一个新的和强大的方法来分析联合信号和网络动态样本分辨率。当图连通性估计从多变量信号本身，适当考虑瞬时图信号功能允许一种新的动态连接性测量图变量动态（GVD）的连接性，这是强大的虚假短期依赖。为此，我们提出了适当的功能的相关性，一致性和相位滞后指数。我们表明，我们的方法可以确定信号与一个单一的相关对完全不相关的信号高达128个节点的大小（1 8128加权边缘）。GVD连接也被证明是更强大的比其他GSP的方法在检测一个随机旅行的球体上的三维网格和标准的动态连接，在确定EEG的休息状态和任务相关的活动的差异。我们还展示了它在揭示地球物理伽马射线数据的隐藏深度相关性。我们期望所提出的方法和框架将为各种应用环境中的数据分析提供新的方法。
Incorporating graphs in the analysis of multivariate signals is becoming a standard way to understand the interdependency of activity recorded at different sites. The new research frontier in this direction includes the important problem of how to assess dynamic changes of signal activity. We address this problem in a novel way by defining the graph-variate signal alongside methods for its analysis. Essentially, graph-variate signal analysis leverages graphs of reliable connectivity information to filter instantaneous bivariate functions of the multivariate signal. This opens up a new and robust approach to analyze joint signal and network dynamics at sample resolution. When graph connectivity is estimated from the multivariate signal itself, the appropriate consideration of instantaneous graph signal functions allows for a novel dynamic connectivity measure—graph-variate dynamic(GVD)connectivity—which is robust to spurious short-term dependencies. For this, we present appropriate functions for correlation, coherence and the phase-lag index. We show that our approach can determine signals with a single correlated couple against wholly uncorrelated signals up to 128 nodes in size (1 out of 8128 weighted edges). GVD connectivity is also shown to be more robust than other GSP approaches at detecting a randomly traveling spheroid on a three-dimensional grid and standard dynamic connectivity in determining differences in EEG resting-state and task-related activity. We also demonstrate its use in revealing hidden depth correlations from geophysical gamma ray data. We expect that the methods and framework presented will provide new approaches to data analysis in a variety of applied settings.