Yamashita Okito, Galka Andreas, Ozaki Tohru, Biscay Rolando, Valdes-Sosa Pedro
Graduate University for Advanced Studies, Tokyo, Japan.
Hum Brain Mapp. 2004 Apr;21(4):221-35. doi: 10.1002/hbm.20000.
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.
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力学，我们可以通过将问题转化为状态空间表示并假设动力学的特定参数模型类，对解施加一般的时空约束。基于II型似然性的赤池贝叶斯信息准则（ABIC）用于估计参数和评估模型。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利用了空间和时间信息，而LORETA仅使用空间信息。将动态LORETA应用于模拟EEG数据时，发现其性能与LORETA相比有显著提高，并且该新方法也应用于临床EEG数据。
