Linear inverse solutions: simulations from a realistic head model in MEG.
作者： Laurent Soufflet , Peter H. Boeijinga
DOI: 10.1007/S10548-005-0278-6
关键词: Inverse 、 Noise (electronics) 、 Inverse problem 、 Interference (wave propagation) 、 Dipole 、 Regularization (mathematics) 、 Mathematical optimization 、 Gaussian noise 、 Mathematical analysis 、 Mathematics 、 Boundary element method
摘要: Distributed linear solutions are widely used in source localization to solve the ill-posed EEG/MEG inverse problem. In classical approach based on dipole sources, these methods estimate current densities at a great number of brain sites, typically nodes 3-D grid which discretizes chosen solution space. The estimated density distributions displayed as electromagnetic tomography (BET) images. We have tested well known minimum norm (MN, WMN, LORETA) and other [WROP, sLORETA, interference uniform, gain weight vector normalized (WVN), new named SLF (Standardized Lead Field)], using MEG configuration (BTi Magnes 2500 WH with 148 axial magnetometers) realistic head model BEM (Boundary Element Method). were compared noise-free condition presence noise errors (DLE) together figure merit that we called max uniformity, measures capability an show spots activity similar amplitudes tomographies when multiple sources moments simultaneously active. Whereas some (sLORETA, uniform SLF) capable zero case, none them reached 100% correct localizations high level Gaussian noise. solution, has advantage be independent from any regularization parameter, presented best results lowest uniformities, almost localizatious 10% more than 90% 30% added data. Nevertheless, no was able combine same time single visualize comparable tomographies.
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