Review on solving the inverse problem in EEG source analysis

By Roberta Grech, T. Cassar, J. Muscat, K. Camilleri, S. Fabri, M. Zervakis, P. Xanthopoulos, Vangelis Sakkalis, Bart Vanrumste. Journal of NeuroEngineering and Rehabilitation, 2008

1,197 citations recorded by the source corpus.

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.

Subfields: Applied Mathematics, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1ae05e8b5d2441f34fdab596f1c32e8d0c278bae

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Citations in the corpus

Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG (2011, background): “Although, this idea dates back to the mid-1980s [7], during the past decade, studies have developed novel approaches for determining the brain sources that underlie the spatial and temporal patterns of EEG and MEG signals [8,9].”
The steady-state visual evoked potential in vision research: A review. (2015, background): “, 2010), by cancellation due to opposing directions of current flow within the cortical laminae (Mitzdorf & Singer, 1979), and finally by the ill-posed nature of inverse modeling procedures (Grech et al., 2008).”
Decomposing Spatiotemporal Brain Patterns into Topographic Latent Sources (2014, neutral): “1 Our use of theword “source” is different from its use in and EEG (Grech et al., 2008).”
EEG artifact removal—state-of-the-art and guidelines (2015, background): “blem [40] and is not solvable with the solution to the BSS”
Distributed theta networks support the control of working memory: Evidence from scalp and intracranial EEG (2025, unknown): “Scalp EEG source localization faces inherent limitations, as the inverse problem does not yield a unique solution without additional assumptions or constraints (Grech et al., 2008; Michel & Brunet, 2019; Lai et al., 2018).”