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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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627 Dimensions

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1040 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

The data shown below were compiled from readership statistics for 1,040 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 18 2%
United Kingdom 8 <1%
Germany 8 <1%
Japan 6 <1%
Canada 6 <1%
Australia 4 <1%
Spain 4 <1%
China 2 <1%
France 2 <1%
Other 20 2%
Unknown 962 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 289 28%
Researcher 181 17%
Student > Master 160 15%
Student > Bachelor 77 7%
Student > Doctoral Student 67 6%
Other 161 15%
Unknown 105 10%
Readers by discipline Count As %
Engineering 236 23%
Neuroscience 161 15%
Psychology 144 14%
Agricultural and Biological Sciences 81 8%
Computer Science 73 7%
Other 167 16%
Unknown 178 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 21 January 2019.
All research outputs
#7,960,689
of 14,167,291 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#420
of 838 outputs
Outputs of similar age
#58,797
of 109,707 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#5
of 8 outputs
Altmetric has tracked 14,167,291 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 838 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one is in the 47th percentile – i.e., 47% of its peers scored the same or lower than it.
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 109,707 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
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 3 of them.