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A Finite Element Solution of the Forward Problem in EEG for Multipolar Sources.
IEEE Trans Neural Syst Rehabil Eng ; 27(3): 368-377, 2019 03.
Article em En | MEDLINE | ID: mdl-30561347
ABSTRACT
Multipolar source models have been presented in the context of electro/magnetoencephalography (E/MEG) to compensate for the limitations of the classical equivalent current dipole to represent realistic generators of brain activity. Although there exist several reports accounting for the advantages of multipolar components over single dipoles, there is still no available numerical implementation in fully personalized scenarios. In this paper, we present, for the first time, a finite element framework for simulating EEG signals generated by multipolar current sources in individualized, heterogeneous, and anisotropic head models. This formulation is based on the subtraction approach, guaranteeing the existence and uniqueness of the solution. In particular, we analyze the cases of monopolar, dipolar, and quadrupolar source components, for which we study their performance in idealized and realistic head models. Numerical solutions are compared with analytical formulas in multi-layered spherical models. Such formulas are available in the case of monopolar and dipolar sources, and here derived for the quadrupolar components. We finally illustrate their advantages in the description of extended current generators using a realistic head model. The framework presented here enables further analysis towards the estimation of biophysically principled source parameters from standard E/MEG experiments.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Análise de Elementos Finitos / Eletroencefalografia Limite: Humans Idioma: En Revista: IEEE Trans Neural Syst Rehabil Eng Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Análise de Elementos Finitos / Eletroencefalografia Limite: Humans Idioma: En Revista: IEEE Trans Neural Syst Rehabil Eng Ano de publicação: 2019 Tipo de documento: Article