PDF(Pubmed)
Abstract：
UNASSIGNED: Source analysis of Electroencephalography (EEG) data requires the computation of the scalp potential induced by current sources in the brain. This so-called EEG forward problem is based on an accurate estimation of the volume conduction effects in the human head, represented by a partial differential equation which can be solved using the finite element method (FEM). FEM offers flexibility when modeling anisotropic tissue conductivities but requires a volumetric discretization, a mesh, of the head domain. Structured hexahedral meshes are easy to create in an automatic fashion, while tetrahedral meshes are better suited to model curved geometries. Tetrahedral meshes, thus, offer better accuracy but are more difficult to create.
UNASSIGNED: We introduce CutFEM for EEG forward simulations to integrate the strengths of hexahedra and tetrahedra. It belongs to the family of unfitted finite element methods, decoupling mesh and geometry representation. Following a description of the method, we will employ CutFEM in both controlled spherical scenarios and the reconstruction of somatosensory-evoked potentials.
UNASSIGNED: CutFEM outperforms competing FEM approaches with regard to numerical accuracy, memory consumption, and computational speed while being able to mesh arbitrarily touching compartments.
UNASSIGNED: CutFEM balances numerical accuracy, computational efficiency, and a smooth approximation of complex geometries that has previously not been available in FEM-based EEG forward modeling.
UNASSIGNED: We introduce CutFEM for EEG forward simulations to integrate the strengths of hexahedra and tetrahedra. It belongs to the family of unfitted finite element methods, decoupling mesh and geometry representation. Following a description of the method, we will employ CutFEM in both controlled spherical scenarios and the reconstruction of somatosensory-evoked potentials.
UNASSIGNED: CutFEM outperforms competing FEM approaches with regard to numerical accuracy, memory consumption, and computational speed while being able to mesh arbitrarily touching compartments.
UNASSIGNED: CutFEM balances numerical accuracy, computational efficiency, and a smooth approximation of complex geometries that has previously not been available in FEM-based EEG forward modeling.
摘要：
■脑电图（EEG）数据的源分析需要计算大脑中电流源感应的头皮电位。这个所谓的EEG前向问题是基于对人体头部体积传导效应的准确估计，由偏微分方程表示，可以使用有限元方法（FEM）求解。FEM在建模各向异性组织电导率时提供了灵活性，但需要体积离散化，一个网格，头域。结构化的六面体网格很容易以自动方式创建，而四面体网格更适合于模型弯曲的几何形状。四面体网格，因此，提供更好的准确性，但更难创建。
■我们引入CutFEM进行EEG正向模拟，以整合六面体和四面体的优势。它属于非拟合有限元方法家族，解耦网格和几何表示。根据该方法的描述，我们将在受控球形场景和体感诱发电位重建中使用CutFEM。
■CutFEM在数值精度方面优于竞争的FEM方法，内存消耗，和计算速度，同时能够任意啮合触摸隔间。
■CutFEM平衡数值精度，计算效率，以及复杂几何形状的平滑近似，这在基于FEM的EEG正向建模中以前是不可用的。
■我们引入CutFEM进行EEG正向模拟，以整合六面体和四面体的优势。它属于非拟合有限元方法家族，解耦网格和几何表示。根据该方法的描述，我们将在受控球形场景和体感诱发电位重建中使用CutFEM。
■CutFEM在数值精度方面优于竞争的FEM方法，内存消耗，和计算速度，同时能够任意啮合触摸隔间。
■CutFEM平衡数值精度，计算效率，以及复杂几何形状的平滑近似，这在基于FEM的EEG正向建模中以前是不可用的。
