AI 中文总结
该研究对比了两款有限元软件的脑电源模型，结合多种源估计方法，发现逆方法的性能取决于源聚焦假设与源模型的兼容性，宽分布源模型对噪声更敏感。
AI 中文摘要
本研究对比了Zeffiro Interface和DUNEuro中有限元法计算的正问题解，使用了两者提供的不同脑电源模型。我们对比了DUNEuro的两种源模型：Whitney基（Whitney basis）和局部减法（Local subtraction），以及Zeffiro Interface的散度适配模型（divergence-conforming model）。为进行源估计，我们应用了稀疏促进标准化分层自适应L1回归（SHAL1R）、标准化卡尔曼滤波（SKF）、经典标准化低分辨率脑电磁断层成像（sLORETA）和偶极子扫描（dipole scanning）。分析指标包括地球移动距离（Earth Mover's Distance）、深度偏差散点图，以及振幅分布和聚焦性的定性评估。对每种方法进行源插值的初步实验显示，局部减法模型在不同深度下与 lead field 局部行为的预期高度匹配。主要结果表明，逆方法的成功高度依赖于其对源聚焦性的假设与所选源模型的兼容性：点源模型与专为这类源设计的方法配对时表现最佳，即稀疏促进方法和单源扫描方法；此外，允许宽分布 patch 源的源模型对额外噪声更敏感。
英文摘要
In this study, we compare forward solutions computed with finite element methods in Zeffiro Interface and DUNEuro, using the different source models they provided. We compared two of the source models from DUNEuro, called Whitney basis and Local subtraction, and the divergence-conforming model of Zeffiro Interface. For source estimation, we applied sparsity-promoting standardized hierarchical adaptive L1 regression (SHAL1R), standardized Kalman filtering (SKF), classical sLORETA, and dipole scanning. Analyses include Earth Mover's Distance, depth bias scatter plots, and qualitative assessments of amplitude distribution and focality. Preliminary experiments with source interpolation for each method revealed that Local subtraction closely matches expectations for the local behavior of the lead field at various depths. The main results reveal that the success of an inverse method depends strongly on the compatibility between its assumptions about the focality of the source and the chosen source model, with point-source models performing best when paired with methods designed for such sources, i.e., sparsity-promoting methods and methods that scan for a single source. Moreover, source models that admit patch sources with a wide distribution are more sensitive to additional noise.
Comments18 pages, 14 figures, an extended version of the conference paper https://arxiv.org/abs/2604.20448, fixed typo in Section 4.5 title
