STRAPS: A Fully Data-Driven Spatio-Temporally Regularized Algorithm for M/EEG Patch Source Imaging
STRAPS: A Fully Data-Driven Spatio-Temporally Regularized Algorithm for M/EEG Patch Source Imaging
复制标题
STRAPS：一种完全数据驱动的时空正则化算法，用于 M/EEG 斑块源成像
DOI：
10.1142/s0129065715500161
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发表时间：
2015-05
期刊：
影响因子：
--
通讯作者：
Li, Yuanqing
中科院分区：
文献类型：
--
作者：
Yu, Zhu Liang;Wu, Wei;Gu, Zhenghui;Li, Yuanqing
对于基于M/EEG的分布式源成像，基于L2范数的方法在空间扩展源成像中是有效的，而基于L1范数的方法更适合于估计焦点和稀疏源。然而，当源的空间范围先验未知时，使用这两种方法的理由都不充分。通过利用斑块源的时空信息进行贝叶斯推断作为一种自适应信号源成像的工具具有很大的前景，但计算和方法上的限制仍然有待克服。本文在对M/EEG数据进行状态空间建模的基础上，提出了一种完全数据驱动的可伸缩M/EEG斑块源成像算法--STRAPS。与现有算法不同的是，递归惩罚最小二乘(RPLS)过程被用来有效地估计源活动，而不是计算量很大的卡尔曼滤波/平滑。此外，通过经验贝叶斯对表征斑块源时空动态的多变量自回归(MVAR)模型的系数进行了原则性估计。大量的数值实验表明，STRAPS在估计不同空间范围的斑块源的位置、空间范围和幅度方面具有很好的性能。
For M/EEG-based distributed source imaging, it has been established that the L2-norm-based methods are effective in imaging spatially extended sources, whereas the L1-norm-based methods are more suited for estimating focal and sparse sources. However, when the spatial extents of the sources are unknown a priori, the rationale for using either type of methods is not adequately supported. Bayesian inference by exploiting the spatio-temporal information of the patch sources holds great promise as a tool for adaptive source imaging, but both computational and methodological limitations remain to be overcome. In this paper, based on state-space modeling of the M/EEG data, we propose a fully data-driven and scalable algorithm, termed STRAPS, for M/EEG patch source imaging on high-resolution cortices. Unlike the existing algorithms, the recursive penalized least squares (RPLS) procedure is employed to efficiently estimate the source activities as opposed to the computationally demanding Kalman filtering/smoothing. Furthermore, the coefficients of the multivariate autoregressive (MVAR) model characterizing the spatial-temporal dynamics of the patch sources are estimated in a principled manner via empirical Bayes. Extensive numerical experiments demonstrate STRAPS's excellent performance in the estimation of locations, spatial extents and amplitudes of the patch sources with varying spatial extents.
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