Neilson Lora A, Kovalyov Mikhail, Koles Zoltan J
Department of Electrical and Computer Engineering, University of Alberta, W2-106 ECERF, Edmonton, Alberta, Canada T6G 2V4.
Clin Neurophysiol. 2005 Oct;116(10):2302-14. doi: 10.1016/j.clinph.2005.07.010.
Solution of the forward problem using realistic head models is necessary for accurate EEG source analysis. Realistic models are usually derived from volumetric magnetic resonance images that provide a voxel resolution of about 1 mm3. Electrical models could, therefore contain, for a normal adult head, over 4 million elements. Solution of the forward problem using models of this magnitude has so far been impractical due to issues of computation time and memory.
A preconditioner is proposed for the conjugate-gradient method that enables the forward problem to be solved using head models of this magnitude. It is applied to the system matrix constructed from the head anatomy using finite differences. The preconditioner is not computed explicitly and so is very efficient in terms of memory utilization.
Using a spherical head model discretized into over 4 million volumes, we have been able to obtain accurate forward solutions in about 60 min on a 1 GHz Pentium III. L2 accuracy of the solutions was better than 2%.
Accurate solution of the forward problem in EEG in a finely discretized head model is practical in terms of computation time and memory.
The results represent an important step in head modeling for EEG source analysis.
使用逼真的头部模型求解正向问题对于准确的脑电图源分析是必要的。逼真的模型通常源自体积磁共振图像，其提供约1立方毫米的体素分辨率。因此，对于正常成人头部，电模型可能包含超过400万个单元。由于计算时间和内存问题，迄今为止，使用这种规模的模型求解正向问题是不切实际的。
提出一种用于共轭梯度法的预处理器，它能够使用这种规模的头部模型求解正向问题。它应用于使用有限差分从头部解剖结构构建的系统矩阵。预处理器不进行显式计算，因此在内存利用方面非常高效。
使用离散为超过400万个体积的球形头部模型，我们能够在1 GHz奔腾III处理器上约60分钟内获得准确的正向解。解的L2精度优于2%。
在精细离散的头部模型中，就计算时间和内存而言，准确求解脑电图正向问题是可行的。
这些结果代表了脑电图源分析头部建模中的重要一步。
