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Review on solving the inverse problem in EEG source analysis

Overview of attention for article published in Journal of NeuroEngineering and Rehabilitation, November 2008
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

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2 X users
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1 patent
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2 Wikipedia pages

Citations

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1212 Mendeley
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Title
Review on solving the inverse problem in EEG source analysis
Published in
Journal of NeuroEngineering and Rehabilitation, November 2008
DOI 10.1186/1743-0003-5-25
Pubmed ID
Authors

Roberta Grech, Tracey Cassar, Joseph Muscat, Kenneth P Camilleri, Simon G Fabri, Michalis Zervakis, Petros Xanthopoulos, Vangelis Sakkalis, Bart Vanrumste

Abstract

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 the state-of-the-art techniques used to find approximate solutions of the brain sources giving rise to a scalp potential recording. Furthermore, a review of the performance results of the different techniques is provided to compare these different inverse solutions. The authors also include the results of a Monte-Carlo analysis which they performed to compare four non parametric algorithms and hence contribute to what is presently recorded in the literature. An extensive list of references to the work of other researchers is also provided. This paper starts off with a mathematical description of the inverse problem and proceeds to discuss the two main categories of methods which were developed to solve the EEG inverse problem, mainly the non parametric and parametric methods. The main difference between the two is to whether a fixed number of dipoles is assumed a priori or not. Various techniques falling within these categories are described including minimum norm estimates and their generalizations, LORETA, sLORETA, VARETA, S-MAP, ST-MAP, Backus-Gilbert, LAURA, Shrinking LORETA FOCUSS (SLF), SSLOFO and ALF for non parametric methods and beamforming techniques, BESA, subspace techniques such as MUSIC and methods derived from it, FINES, simulated annealing and computational intelligence algorithms for parametric methods. From a review of the performance of these techniques as documented in the literature, one could conclude that in most cases the LORETA solution gives satisfactory results. In situations involving clusters of dipoles, higher resolution algorithms such as MUSIC or FINES are however preferred. Imposing reliable biophysical and psychological constraints, as done by LAURA has given superior results. The Monte-Carlo analysis performed, comparing WMN, LORETA, sLORETA and SLF, for different noise levels and different simulated source depths has shown that for single source localization, regularized sLORETA gives the best solution in terms of both localization error and ghost sources. Furthermore the computationally intensive solution given by SLF was not found to give any additional benefits under such simulated conditions.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1,212 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 18 1%
Germany 8 <1%
United Kingdom 8 <1%
Japan 6 <1%
Canada 6 <1%
Australia 4 <1%
Spain 4 <1%
Turkey 2 <1%
Belgium 2 <1%
Other 19 2%
Unknown 1135 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 317 26%
Researcher 202 17%
Student > Master 178 15%
Student > Bachelor 87 7%
Student > Doctoral Student 74 6%
Other 182 15%
Unknown 172 14%
Readers by discipline Count As %
Engineering 253 21%
Neuroscience 198 16%
Psychology 152 13%
Computer Science 87 7%
Agricultural and Biological Sciences 81 7%
Other 194 16%
Unknown 247 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 January 2023.
All research outputs
#4,189,952
of 23,556,846 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#237
of 1,313 outputs
Outputs of similar age
#15,329
of 93,222 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#2
of 8 outputs
Altmetric has tracked 23,556,846 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,313 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one has done well, scoring higher than 81% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 93,222 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.