Institute for Biomagnetism and Biosignalanalysis, University of Münster, Malmedyweg 15, 48149, Münster, Germany.
Scientific Computing & Imaging (SCI) Institute, University of Utah, 72 Central Campus Dr., Salt Lake City, 84112, USA.
Biomed Eng Online. 2018 Mar 27;17(1):37. doi: 10.1186/s12938-018-0463-y.
Accurately solving the electroencephalography (EEG) forward problem is crucial for precise EEG source analysis. Previous studies have shown that the use of multicompartment head models in combination with the finite element method (FEM) can yield high accuracies both numerically and with regard to the geometrical approximation of the human head. However, the workload for the generation of multicompartment head models has often been too high and the use of publicly available FEM implementations too complicated for a wider application of FEM in research studies. In this paper, we present a MATLAB-based pipeline that aims to resolve this lack of easy-to-use integrated software solutions. The presented pipeline allows for the easy application of five-compartment head models with the FEM within the FieldTrip toolbox for EEG source analysis.
The FEM from the SimBio toolbox, more specifically the St. Venant approach, was integrated into the FieldTrip toolbox. We give a short sketch of the implementation and its application, and we perform a source localization of somatosensory evoked potentials (SEPs) using this pipeline. We then evaluate the accuracy that can be achieved using the automatically generated five-compartment hexahedral head model [skin, skull, cerebrospinal fluid (CSF), gray matter, white matter] in comparison to a highly accurate tetrahedral head model that was generated on the basis of a semiautomatic segmentation with very careful and time-consuming manual corrections.
The source analysis of the SEP data correctly localizes the P20 component and achieves a high goodness of fit. The subsequent comparison to the highly detailed tetrahedral head model shows that the automatically generated five-compartment head model performs about as well as a highly detailed four-compartment head model (skin, skull, CSF, brain). This is a significant improvement in comparison to a three-compartment head model, which is frequently used in praxis, since the importance of modeling the CSF compartment has been shown in a variety of studies.
The presented pipeline facilitates the use of five-compartment head models with the FEM for EEG source analysis. The accuracy with which the EEG forward problem can thereby be solved is increased compared to the commonly used three-compartment head models, and more reliable EEG source reconstruction results can be obtained.
准确求解脑电图（EEG）正问题对于精确的 EEG 源分析至关重要。先前的研究表明，使用多腔室头部模型结合有限元方法（FEM）可以在数值上和对人体头部的几何逼近方面达到高精度。然而，生成多腔室头部模型的工作量往往过高，并且使用公开可用的 FEM 实现对于更广泛地将 FEM 应用于研究也过于复杂。在本文中，我们提出了一个基于 MATLAB 的流水线，旨在解决缺乏易于使用的集成软件解决方案的问题。该流水线允许在 EEG 源分析的 FieldTrip 工具包中轻松应用具有 FEM 的五腔室头部模型。
来自 SimBio 工具包的 FEM，更具体地说是圣文森特方法，被集成到 FieldTrip 工具包中。我们简要介绍了实现方法及其应用，并使用该流水线进行了体感诱发电位（SEP）的源定位。然后，我们评估了使用自动生成的五腔室六面体头部模型（皮肤、颅骨、脑脊液（CSF）、灰质、白质）可以达到的精度，与基于半自动分割的高度准确的四面体头部模型进行比较，该模型具有非常仔细和耗时的手动校正。
SEP 数据的源分析正确定位了 P20 成分，并实现了高度拟合。随后与高度详细的四面体头部模型的比较表明，自动生成的五腔室头部模型的性能与高度详细的四腔室头部模型（皮肤、颅骨、CSF、大脑）相当。与在实践中经常使用的三腔室头部模型相比，这是一个重大改进，因为在各种研究中已经证明了建模 CSF 腔室的重要性。
提出的流水线促进了 FEM 用于 EEG 源分析的五腔室头部模型的使用。与常用的三腔室头部模型相比，可以提高 EEG 正问题的求解精度，并获得更可靠的 EEG 源重建结果。
