Recursive penalized least squares solution for dynamical inverse problems of EEG generation
Recursive penalized least squares solution for dynamical inverse problems of EEG generation
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DOI：
10.1002/hbm.20000
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发表时间：
2004-04
影响因子：
4.8
通讯作者：
O. Yamashita;A. Galka;T. Ozaki;R. Biscay;P. Valdés-Sosa
中科院分区：
文献类型：
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作者：
O. Yamashita;A. Galka;T. Ozaki;R. Biscay;P. Valdés-Sosa
在脑电图 (EEG) 生成的动态逆问题中，假设电流分布具有特定的动态，我们可以通过将问题转换为状态空间表示并假设动态的特定类参数模型来对解决方案施加一般时空约束。基于 II 类似然的赤池贝叶斯信息准则 (ABIC) 用于估计参数并评估模型。此外，还引入了动态低分辨率脑电磁断层扫描（LORETA），这是一种估计电流分布的新方法。递归惩罚最小二乘法 (RPLS) 步骤构成了我们实现的主要元素。为了获得改进的逆解，动态 LORETA 利用空间和时间信息，而 LORETA 仅使用空间信息。当动态LORETA应用于模拟脑电图数据时，与LORETA相比，性能有了相当大的提高，并且新方法也应用于临床脑电图数据。哼。大脑地图。 21:221–235，2004 年。© 2004 Wiley-Liss, Inc.
In the dynamical inverse problem of electroencephalogram (EEG) generation where a specific dynamics for the electrical current distribution is assumed, we can impose general spatiotemporal constraints onto the solution by casting the problem into a state space representation and assuming a specific class of parametric models for the dynamics. The Akaike Bayesian Information Criterion (ABIC), which is based on the Type II likelihood, was used to estimate the parameters and evaluate the model. In addition, dynamic low‐resolution brain electromagnetic tomography (LORETA), a new approach for estimating the current distribution is introduced. A recursive penalized least squares (RPLS) step forms the main element of our implementation. To obtain improved inverse solutions, dynamic LORETA exploits both spatial and temporal information, whereas LORETA uses only spatial information. A considerable improvement in performance compared to LORETA was found when dynamic LORETA was applied to simulated EEG data, and the new method was applied also to clinical EEG data. Hum. Brain Mapp. 21:221–235, 2004. © 2004 Wiley‐Liss, Inc.