背景:准确解决脑电图(EEG)前向问题对于精确的EEG源分析至关重要。先前的研究表明,将多隔室头部模型与有限元方法(FEM)结合使用可以在数值上和在人体头部的几何近似方面产生很高的精度。然而,生成多隔室头模型的工作量通常太高,并且对于FEM在研究中的更广泛应用而言,使用公开可用的FEM实现过于复杂。在本文中,我们提出了一个基于MATLAB的管道,旨在解决这种缺乏易于使用的集成软件解决方案。所提出的管道允许在FieldTrip工具箱中使用FEM轻松应用五室头模型,以进行EEG源分析。
方法:来自SimBio工具箱的FEM,更具体地说,圣维南的方法,集成到FieldTrip工具箱中。我们给出了实现及其应用的简短草图,我们使用该管道进行体感诱发电位(SEP)的源定位。然后,我们评估使用自动生成的五室六面体头部模型[皮肤,头骨,脑脊液(CSF),灰质,白质]与高度精确的四面体头模型相比,该模型是在半自动分割的基础上生成的,并进行了非常仔细且耗时的手动校正。
结果:SEP数据的源分析正确定位了P20组件,并实现了很高的拟合优度。随后与高度详细的四面体头部模型的比较表明,自动生成的五室头部模型的性能与高度详细的四室头部模型(皮肤,头骨,CSF,brain).与三室机头模型相比,这是一个重大改进,这在实践中经常使用,因为在各种研究中已经显示了建立CSF区室模型的重要性。
结论:所提出的管道有助于将五室头部模型与FEM一起用于EEG源分析。与常用的三隔室头部模型相比,可以解决EEG正向问题的准确性得到了提高,并且可以获得更可靠的EEG源重建结果。
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.