通过H(div)有限元源进行脑电图（EEG）前向建模
Electroencephalography (EEG) Forward Modeling via H(div) Finite Element Sources with Focal Interpolation
AI总结：
本文提出基于H(div)有限元方法的EEG前向建模方法，利用双极源和两种插值方法提高预测精度，对比传统单极方法展示其优越性。
摘要：
本研究旨在开发基于有限元方法的聚焦、准确且稳健的前向建模方法，以预测计算域表面的电势分布。在EEG评估中，将源电流放置到几何复杂的灰质 compartment 是一项具有挑战性但必要的任务，以避免因组织导电性跳跃导致的前向误差。本文采用数学严谨的公式，利用H(div)基函数建模电流场。线性和二次函数用于建模电势场，采用标准线性拉格朗日（节点）基函数。所得模型包含双极源，通过位置基于优化（PBO）和均值位置/取向（MPO）方法插值到随机位置和取向。这些结果表明，当前双极方法可以达到或在某些方面超越传统单极方法（部分积分PI和圣文纳SV方法）的精度。
英文摘要：
The goal of this study is to develop focal, accurate and robust finite element method (FEM) based approaches which can predict the electric potential on the surface of the computational domain given its structure and internal primary source current distribution. While conducting an EEG evaluation, the placement of source currents to the geometrically complex grey matter compartment is a challenging but necessary task to avoid forward errors attributable to tissue conductivity jumps. Here, this task is approached via a mathematically rigorous formulation, in which the current field is modeled via divergence conforming H(div) basis functions. Both linear and quadratic functions are used while the potential field is discretized via the standard linear Lagrangian (nodal) basis. The resulting model includes dipolar sources which are interpolated into a random set of positions and orientations utilizing two alternative approaches: the position based optimization (PBO) and the mean position/orientation (MPO) method. These results demonstrate that the present dipolar approach can reach or even surpass, at least in some respects, the accuracy of two classical reference methods, the partial integration (PI) and St. Venant (SV) approach which utilize monopolar loads instead of dipolar currents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
