Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Pirkanmaa, Finland.
PLoS One. 2023 Sep 20;18(9):e0290715. doi: 10.1371/journal.pone.0290715. eCollection 2023.
This paper introduces an automated approach for generating a finite element (FE) discretization of a multi-compartment human head model for electroencephalographic (EEG) source localization. We aim to provide an adaptable FE mesh generation tool for EEG studies. Our technique relies on recursive solid angle labeling of a surface segmentation coupled with smoothing, refinement, inflation, and optimization procedures to enhance the mesh quality. In this study, we performed numerical meshing experiments with the three-layer Ary sphere and a magnetic resonance imaging (MRI)-based multi-compartment head segmentation which incorporates a comprehensive set of subcortical brain structures. These experiments are motivated, on one hand, by the sensitivity of non-invasive subcortical source localization to modeling errors and, on the other hand, by the present lack of open EEG software pipelines to discretize all these structures. Our approach was found to successfully produce an unstructured and boundary-fitted tetrahedral mesh with a sub-one-millimeter fitting error, providing the desired accuracy for the three-dimensional anatomical details, EEG lead field matrix, and source localization. The mesh generator applied in this study has been implemented in the open MATLAB-based Zeffiro Interface toolbox for forward and inverse processing in EEG and it allows for graphics processing unit acceleration.
本文提出了一种自动生成用于脑电图（EEG）源定位的多腔人头模型有限元（FE）离散化的方法。我们旨在为 EEG 研究提供一种适应性强的 FE 网格生成工具。我们的技术依赖于表面分割的递归立体角标记，结合平滑、细化、膨胀和优化过程来提高网格质量。在这项研究中，我们使用三层 Ary 球体和基于磁共振成像（MRI）的多腔头部分割进行了数值网格实验，其中包含了一套全面的皮质下脑结构。这些实验一方面是为了满足非侵入性皮质下源定位对建模误差的敏感性，另一方面是因为目前缺乏用于离散化所有这些结构的开放 EEG 软件管道。我们的方法成功地生成了具有亚一毫米拟合误差的非结构化和边界拟合的四面体网格，为三维解剖细节、EEG 导联场矩阵和源定位提供了所需的准确性。本研究中应用的网格生成器已在基于 MATLAB 的开放 Zeffiro Interface 工具箱中实现，用于 EEG 的正向和逆向处理，并允许图形处理单元加速。
