Smooth reconstruction of cortical sources from EEG or MEG recordings

Wagner, Michael; Fuchs, Manfred; Wischmann, H.‐A.; Drenckhahn, R.; Köhler, Thomas

doi:10.1016/s1053-8119(96)80170-4

Cited by 27 publications

(33 citation statements)

References 2 publications

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“…Additionally cLORETA (Wagner et al, 1996) and focal vector field reconstruction (FVR) (Haufe et al, 2008) were used to estimate solutions and the results from these inverse algorithms were compared with IRES (these results are presented in Figs. S4 and S5 of the supplementary materials).…”

Section: Methodsmentioning

confidence: 99%

Imaging brain source extent from EEG/MEG by means of an iteratively reweighted edge sparsity minimization (IRES) strategy

Sohrabpour

Worrell

et al. 2016

NeuroImage

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Estimating extended brain sources using EEG/MEG source imaging techniques is challenging. EEG and MEG have excellent temporal resolution at millisecond scale but their spatial resolution is limited due to the volume conduction effect. We have exploited sparse signal processing techniques in this study to impose sparsity on the underlying source and its transformation in other domains (mathematical domains, like spatial gradient). Using an iterative reweighting strategy to penalize locations that are less likely to contain any source, it is shown that the proposed iteratively reweighted edge sparsity minimization (IRES) strategy can provide reasonable information regarding the location and extent of the underlying sources. This approach is unique in the sense that it estimates extended sources without the need of subjectively thresholding the solution. The performance of IRES was evaluated in a series of computer simulations. Different parameters such as source location and signal-to-noise ratio were varied and the estimated results were compared to the targets using metrics such as localization error (LE), area under curve (AUC) and overlap between the estimated and simulated sources. It is shown that IRES provides extended solutions which not only localize the source but also provide estimation for the source extent. The performance of IRES was further tested in epileptic patients undergoing intracranial EEG (iEEG) recording for pre-surgical evaluation. IRES was applied to scalp EEGs during interictal spikes, and results were compared with iEEG and surgical resection outcome in the patients. The pilot clinical study results are promising and demonstrate a good concordance between noninvasive IRES source estimation with iEEG and surgical resection outcomes in the same patients. The proposed algorithm, i.e. IRES, estimates extended source solutions from scalp electromagnetic signals which provide relatively accurate information about the location and extent of the underlying source.

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Section: Methodsmentioning

confidence: 99%

Imaging brain source extent from EEG/MEG by means of an iteratively reweighted edge sparsity minimization (IRES) strategy

Sohrabpour

Worrell

et al. 2016

NeuroImage

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“…Similarly to sLORETA, it has originally been conceived for volumetric source localization, but can be modified to work on a surface grid (Wagner et al, 1996). In this case, it is also referred to as cLORETA.…”

Section: Cloretamentioning

confidence: 99%

“…where L corresponds to the surface Laplacian matrix whose elements are given by (Wagner et al, 1996) …”

Section: Cloretamentioning

confidence: 99%

EEG extended source localization: Tensor-based vs. conventional methods

et al. 2014

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“…To compare the performance of SISSY to state-of-the-art methods such as STWV-DA, 4-ExSo-MUSIC, and cortical LORETA (cLORETA) (Wagner et al, 1996), we conduct an extensive simulation study with realistic EEG data in the context of interictal epileptic extended source localization.…”

Section: Simulationsmentioning

confidence: 99%

SISSY: An efficient and automatic algorithm for the analysis of EEG sources based on structured sparsity

et al. 2017

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Over the past decades, a multitude of different brain source imaging algorithms have been developed to identify the neural generators underlying the surface electroencephalography measurements. While most of these techniques focus on determining the source positions, only a small number of recently developed algorithms provides an indication of the spatial extent of the distributed sources. In a recent comparison of brain source imaging approaches, the VB-SCCD algorithm has been shown to be one of the most promising algorithms among these methods. However, this technique suffers from several problems: it leads to amplitude-biased source estimates, it has difficulties in separating close sources, and it has a high computational complexity due to its implementation using second order cone programming.To overcome these problems, we propose to include an additional regulariza-

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Smooth reconstruction of cortical sources from EEG or MEG recordings

Cited by 27 publications

References 2 publications

Imaging brain source extent from EEG/MEG by means of an iteratively reweighted edge sparsity minimization (IRES) strategy

Imaging brain source extent from EEG/MEG by means of an iteratively reweighted edge sparsity minimization (IRES) strategy

EEG extended source localization: Tensor-based vs. conventional methods

SISSY: An efficient and automatic algorithm for the analysis of EEG sources based on structured sparsity

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