English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this primer, we give a review of the inverse problem for EEG source localization.This is intended for the researchers new in the field to get insight in thestate-of-the-art techniques used to find approximate solutions of the brain sourcesgiving rise to a scalp potential recording. Furthermore, a review of the performanceresults of the different techniques is provided to compare these different inversesolutions. The authors also include the results of a Monte-Carlo analysis which theyperformed to compare four non parametric algorithms and hence contribute to what ispresently recorded in the literature. An extensive list of references to the work ofother researchers is also provided.  This paper starts off with a mathematical description of the inverse problem andproceeds to discuss the two main categories of methods which were developed to solvethe EEG inverse problem, mainly the non parametric and parametric methods. The maindifference between the two is to whether a fixed number of dipoles is assumed apriori or not. Various techniques falling within these categories are describedincluding minimum norm estimates and their generalizations, LORETA, sLORETA, VARETA,S-MAP, ST-MAP, Backus-Gilbert, LAURA, Shrinking LORETA FOCUSS (SLF), SSLOFO and ALFfor non parametric methods and beamforming techniques, BESA, subspace techniques suchas MUSIC and methods derived from it, FINES, simulated annealing and computationalintelligence algorithms for parametric methods. From a review of the performance ofthese techniques as documented in the literature, one could conclude that in mostcases the LORETA solution gives satisfactory results. In situations involvingclusters of dipoles, higher resolution algorithms such as MUSIC or FINES are howeverpreferred. Imposing reliable biophysical and psychological constraints, as done byLAURA has given superior results. The Monte-Carlo analysis performed, comparing WMN,LORETA, sLORETA and SLF, for different noise levels and different simulated sourcedepths has shown that for single source localization, regularized sLORETA gives thebest solution in terms of both localization error and ghost sources. Furthermore thecomputationally intensive solution given by SLF was not found to give any additionalbenefits under such simulated conditions.

Review on solving the inverse problem in EEG source analysis