Bayesian Factorial Linear Gaussian State-Space Models for Biosignal Decomposition
Bayesian Factorial Linear Gaussian State-Space Models for Biosignal Decomposition
复制标题
用于生物信号分解的贝叶斯阶乘线性高斯状态空间模型
DOI：
10.1109/lsp.2006.881515
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发表时间：
2007
影响因子：
3.9
通讯作者：
D. Barber
中科院分区：
文献类型：
--
作者：
S. Chiappa;D. Barber
我们讨论了一种提取单个或多个观察通道下的独立动力系统的方法。特别是，我们搜索一维子信号以帮助分解的可解释性。该方法使用近似贝叶斯分析来自动确定潜在动力学的数量和适当的复杂性，并优先考虑最简单的解决方案。我们将此方法应用于未过滤的脑电图信号，以发现具有优先光谱特性的低复杂性源，证明所提取的源的可解释性优于相关方法
We discuss a method to extract independent dynamical systems underlying a single or multiple channels of observation. In particular, we search for one-dimensional subsignals to aid the interpretability of the decomposition. The method uses an approximate Bayesian analysis to determine automatically the number and appropriate complexity of the underlying dynamics, with a preference for the simplest solution. We apply this method to unfiltered EEG signals to discover low-complexity sources with preferential spectral properties, demonstrating improved interpretability of the extracted sources over related methods