Review on solving the forward problem in EEG source analysis

Hallez, Hans; Vanrumste, Bart; Grech, Reuben; Muscat, Joseph; Clercq, Wim De; Vergult, Anneleen; D’Asseler, Yves; Camilleri, Kenneth P.; Fabri, Simon G.; Huffel, Sabine Van; Lemahieu, Ignace

doi:10.1186/1743-0003-4-46

Cited by 423 publications

(359 citation statements)

References 107 publications

(121 reference statements)

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“…Our forward model was relatively simple, consisting of 4 nested isotropic spheres. More sophisticated forward models incorporate complex geometry and/or anisotropy (29). While each of the source modeling techniques used here revealed similar results, future refinements in forward or inverse modeling should further improve the localization of current sources through hd-EEG.…”

Section: Source Modeling Hd-eeg Is Uniquely Suited To Studying Indivimentioning

confidence: 91%

“…The data for the TMS-evoked slow waves were collected from a single male subject (age 21) as part of a previous study (16). In this prior study, we used a 60-channel TMS-compatible EEG net (Nexstim) to record the response to TMS in 15 sleeping males (ages [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. Stimulation of the sensorimotor cortex at 65% of maximal stimulator output reliably elicited high-amplitude (Ͼ80 V) slow wave responses (negative zero crossing to subsequent positive zero crossing duration of 0.25-1 s) that began Ϸ125 ms after delivery of a pulse.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Source modeling sleep slow waves

Murphy

Riedner

Huber

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

394

385

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Slow waves are the most prominent electroencephalographic (EEG) feature of sleep. These waves arise from the synchronization of slow oscillations in the membrane potentials of millions of neurons. Scalplevel studies have indicated that slow waves are not instantaneous events, but rather they travel across the brain. Previous studies of EEG slow waves were limited by the poor spatial resolution of EEGs and by the difficulty of relating scalp potentials to the activity of the underlying cortex. Here we use high-density EEG (hd-EEG) source modeling to show that individual spontaneous slow waves have distinct cortical origins, propagate uniquely across the cortex, and involve unique subsets of cortical structures. However, when the waves are examined en masse, we find that there are diffuse hot spots of slow wave origins centered on the lateral sulci. Furthermore, slow wave propagation along the anterior؊posterior axis of the brain is largely mediated by a cingulate highway. As a group, slow waves are associated with large currents in the medial frontal gyrus, the middle frontal gyrus, the inferior frontal gyrus, the anterior cingulate, the precuneus, and the posterior cingulate. These areas overlap with the major connectional backbone of the cortex and with many parts of the default network.brain mapping ͉ default network ͉ electroencephalography ͉ slow oscillation ͉ traveling wave

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Section: Source Modeling Hd-eeg Is Uniquely Suited To Studying Indivimentioning

confidence: 91%

Section: Methodsmentioning

confidence: 99%

Source modeling sleep slow waves

Murphy

Riedner

Huber

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

394

385

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“…In fact [13], [16] states that a smearing effect on the forward potential computation is introduced by the skull anisotropy. The deeper a source is located the more it is surrounded by anisotopic tissues.…”

mentioning

confidence: 99%

An evaluation of EEG scanner's dependence on the imaging technique, forward model computation method, and array dimensionality

Stahlhut

Attias²,

Stopczynski

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-EEG source reconstruction involves solving an inverse problem that is highly ill-posed and dependent on a generally fixed forward propagation model. In this contribution we compare a low and high density EEG setup's dependence on correct forward modeling. Specifically, we examine how different forward models affect the source estimates obtained using four inverse solvers Minimum-Norm, LORETA, MinimumVariance Adaptive Beamformer, and Sparse Bayesian Learning.

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“…Most of the tissue conductivities have values that are well accepted in the literature [3]. However, there has been some controversy regarding the conductivity of the human skull [2] with authors reporting values that differ in almost an order of magnitude [4], [5]. Most of the source localization techniques use a default value for this parameter.…”

Section: Introductionmentioning

confidence: 99%

“…One of the most common models used to solve the associated ill-posed problem is the distributed source model [1], [2], which expresses the relationship between the brain activity and the M/EEG measurements by the leadfield matrix.…”

Section: Introductionmentioning

confidence: 99%

Skull Conductivity Estimation for EEG Source Localization

Costa

Batatia

Oberlin

et al. 2017

IEEE Signal Process. Lett.

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. Abstract-A reliable leadfield matrix is needed to solve the magnetoencephalography/electroencephalography (M/EEG) source localization problem. The computation of this matrix requires several physical parameters, including the conductivity of the tissues that compose the subject's head. Since it is not precisely known, we modify a recent Bayesian algorithm to estimate the skull conductivity jointly with the brain activity directly from the M/EEG measurements. Synthetic and real data are used to compare our technique with two optimization algorithms, showing that the proposed method is able to provide results of similar or better quality with the advantage of being applicable in a more general case.

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Review on solving the forward problem in EEG source analysis

Cited by 423 publications

References 107 publications

Source modeling sleep slow waves

Source modeling sleep slow waves

An evaluation of EEG scanner's dependence on the imaging technique, forward model computation method, and array dimensionality

Skull Conductivity Estimation for EEG Source Localization

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