A solution to the dynamical inverse problem of EEG generation using spatiotemporal Kalman filtering
A solution to the dynamical inverse problem of EEG generation using spatiotemporal Kalman filtering
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DOI：
10.1016/j.neuroimage.2004.02.022
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发表时间：
2004-10-01
期刊：
影响因子：
5.7
通讯作者：
Valdés-Sosa, P
中科院分区：
文献类型：
--
作者：
Galka, A;Yamashita, O;Valdés-Sosa, P
提出了一种估计脑电信号动态逆问题解的新方法。与以前的方法不同，我们将这个问题重新解释为状态空间框架下的滤波问题；为了解决这个问题，我们提出了一种新的卡尔曼滤波扩展到时空动力学的情况。可以在大脑灰质离散化的每个体素处重建EEG的分布式生成器的时间演变。通过将具有邻域相互作用的线性自回归模型拟合到EEG时间序列，可以探索具有更高分辨率和局部化能力的新类型的逆解。为了对给定数据进行模型比较和参数估计，我们采用了似然最大化方法。对于瞬时逆解和动态逆解，我们导出了每个体素上随时间变化的估计误差的估计式。通过对模拟和临床脑电记录的应用，证明了该算法的有效性。结果表明，通过选择适当的动力学模型，与通常的瞬时逆解相比，可以获得质量有很大改善的逆解。(C)2004 Elsevier Inc.保留所有权利。
We present a new approach for estimating solutions of the dynamical inverse problem of EEG generation. In contrast to previous approaches, we reinterpret this problem as a filtering problem in a state space framework; for the purpose of its solution, we propose a new extension of Kalman filtering to the case of spatiotemporal dynamics. The temporal evolution of the distributed generators of the EEG can be reconstructed at each voxel of a discretisation of the gray matter of brain. By fitting linear auto regressive models with neighbourhood interactions to EEG time series, new classes of inverse solutions with improved resolution and localisation ability can be explored. For the purposes of model comparison and parameter estimation from given data, we employ a likelihood maximisation approach. Both for instantaneous and dynamical inverse solutions, we derive estimators of the time-dependent estimation error at each voxel. The performance of the algorithm is demonstrated by application to simulated and clinical EEG recordings. It is shown that by choosing appropriate dynamical models, it becomes possible to obtain inverse solutions of considerably improved quality, as compared to the usual instantaneous inverse solutions. (C) 2004 Elsevier Inc. All rights reserved.