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Chunk #115 — 3 Inverse solutions — 3.1 Non parametric optimization methods — 3.1.1 The Bayesian framework — Low resolution electrical tomography (LORETA)

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Review on solving the inverse problem in EEG source analysis.
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LORETA [5,27] combines the lead-field normalization with the Laplacian operator, thus, gives the depth-compensated inverse solution under the constraint of smoothly distributed sources. It is based on the maximum smoothness of the solution. It normalizes the columns of G to give all sources (close to the surface and deeper ones) the same opportunity of being reconstructed. This is better than minimum-norm methods in which deeper sources cannot be recovered because dipoles located at the surface of the source space with smaller magnitudes are priveleged. In LORETA, sources are distributed in the whole inner head volume. In this case, L(D) = ||ΔB.D||2 and B = Ω ⊗ I3 is a diagonal matrix for the column normalization of G.