EEG/MEG-neuroimaging algorithm: eLORETA

Pascual-Marqui has posted a preprint and would like comments. Read on for details.

A technical report with some results in the field of
EEG/MEG-neuroimaging (including eLORETA) can be downloaded from:
Title and abstract are included below.
I hope by mid-November-2007 to have the software available (free,
academic, public domain, as usual).
Feedback would be greatly appreciated!

R.D. Pascual-Marqui
The KEY Institute for Brain-Mind Research
University Hospital of Psychiatry
pascualm at

Discrete, 3D distributed, linear imaging methods of electric neuronal
activity. Part 1: exact, zero error localization

Abstract: This paper deals with the EEG/MEG neuroimaging problem:
given measurements of scalp electric potential differences (EEG:
electroencephalogram) and extracranial magnetic fields (MEG:
magnetoencephalogram), find the 3D distribution of the generating
electric neuronal activity. This problem has no unique solution. Only
particular solutions with “good” localization properties are of
interest, since neuroimaging is concerned with the localization of
brain function. In this paper, a general family of linear imaging
methods with exact, zero error localization to point-test sources is
presented. One particular member of this family is sLORETA. It is
shown here that sLORETA has no localization bias in the presence of
measurement and biological noise. Another member of this family,
denoted as eLORETA (exact low resolution brain electromagnetic
tomography), is a genuine inverse solution (not merely a linear
imaging method) with exact, zero error localization in the presence of
measurement and structured biological noise. The general family of
imaging methods is further extended to include data-dependent
(adaptive) quasi-linear imaging methods, also with the exact, zero
error localization property.


One thought on “EEG/MEG-neuroimaging algorithm: eLORETA

  1. If someone posts here a comment of the type: “…Do you know that zero dipole localization error is just a trivial (and useless) property?…”, then here’s the proof that the statement is incorrect.

    Zero dipole localization error might be trivial and useless for methods that are not solutions to the inverse problem, such as methods that report at each voxel a “goodness-of-fit” (or any related) measure for the single, one-at-a-time, best fitting dipole. A solution to the EEG-inverse problem is based on a system of equations, where the distribution of current density (and not one dipole at a time) must explain the measurements.

    Imagine that the first tomography, the x-ray CAT scan, had been done with such an approach, one voxel at a time trying to explaining all line integrals. There would have been no tomography. This is the method advocated those who have published one-dipole-at-a-time tomographies.

    Linear systems can always be characterized by testing their response to Dirac-deltas. When a system is not linear because it is not even a solution to the inverse problem, then dipole localization might be non-informative.

    Roberto D. Pascual-Marqui
    The KEY Institute for Brain-Mind Research
    University Hospital of Psychiatry
    pascualm at


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