paperKB
coga / coga-kb
Help
Sign in

Chunk #127 — 3 Inverse solutions — 3.1 Non parametric optimization methods — 3.1.1 The Bayesian framework — Standardized low resolution brain electromagnetic tomography

Source
Review on solving the inverse problem in EEG source analysis.
Embedded
yes

Text

where D^MNE,l ∈ ℝ3 × 1 is the current density estimate at the lth voxel given by the minimum norm estimate and [SD^] ll ∈ ℝ3 × 3 is the lth diagonal block of the resolution matrix SD^. It was found [32] that in all noise free simulations, although the image was blurred, sLORETA had exact, zero error localization when reconstructing single sources, that is, the maximum of the current density power estimate coincided with the exact dipole location. In all noisy simulations, it had the lowest localization errors when compared with the minimum norm solution and the Dale method [33]. The Dale method is similar to the sLORETA method in that the current density estimate given by the minimum norm solution is used and source localization is based on standardized values of the current density estimates. However, the variance of the current density estimate is based only on the measurement noise, in contrast to sLORETA, which takes into account the actual source variance as well.