In Vivo Conductivity Estimation Using Somatosensory Evoked Potentials and Cortical Constraint on the Source

Vallaghé, Sylvain; Clerc, Maureen; Badier, Jean‐Michel

doi:10.1109/isbi.2007.357032

Cited by 8 publications

(17 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach has been suggested in Lew et al (2007), Vallaghé et al (2007). In section 2 we use Cramer-Rao bounds to demonstrate the impracticality of this approach by showing that the uncertainty in the skull conductivity is inherent to the model.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic forward model for electroencephalography source analysis

Plis¹,

George²,

Jun³

et al. 2007

Phys. Med. Biol.

View full text Add to dashboard Cite

Source localization by electroencephalography (EEG) requires an accurate model of head geometry and tissue conductivity. The estimation of source time courses from EEG or from EEG in conjunction with magnetoencephalography (MEG) requires a forward model consistent with true activity for the best outcome. Although MRI provides an excellent description of soft tissue anatomy, a high resolution model of the skull (the dominant resistive component of the head) requires CT, which is not justified for routine physiological studies. Although a number of techniques have been employed to estimate tissue conductivity, no present techniques provide the noninvasive 3D tomographic mapping of conductivity that would be desirable. We introduce a formalism for probabilistic forward modeling that allows the propagation of uncertainties in model parameters into possible errors in source localization. We consider uncertainties in the conductivity profile of the skull, but the approach is general and can be extended to other kinds of uncertainties in the forward model. We and others have previously suggested the possibility of extracting conductivity of the skull from measured electroencephalography data by simultaneously optimizing over dipole parameters and the conductivity values required by the forward model. Using Cramer-Rao bounds, we demonstrate that this approach does not improve localization results nor does it produce reliable conductivity estimates. We conclude that the conductivity of the skull has to be either accurately measured by an independent technique, or that the uncertainties in the conductivity values should be reflected in uncertainty in the source location estimates.

show abstract

Section: Introductionmentioning

confidence: 99%

Probabilistic forward model for electroencephalography source analysis

Plis¹,

George²,

Jun³

et al. 2007

Phys. Med. Biol.

View full text Add to dashboard Cite

show abstract

“…This was extensively demonstrated in Plis et al (2007). This approach of extracting the conductivity together with the dipole location was also suggested in Lew et al (2007) and Vallaghé et al (2007).…”

Section: Traditional Eeg Inverse Problemmentioning

confidence: 83%

Subspace electrode selection methodology for the reduction of the effect of uncertain conductivity values in the EEG dipole localization: a simulation study using a patient-specific head model

2012

View full text Add to dashboard Cite

The simulation of the electroencephalogram (EEG) using a realistic head model needs the correct conductivity values of several tissues. However, these values are not precisely known and have an influence on the accuracy of the EEG source analysis problem. This paper presents a novel numerical methodology for the increase of accuracy of the EEG dipole source localization problem. The presented subspace electrode selection (SES) methodology is able to limit the effect of uncertain conductivity values on the solution of the EEG inverse problem, yielding improved source localization accuracy. We redefine the traditional cost function associated with the EEG inverse problem and introduce a selection procedure of EEG potentials. In each iteration of the presented EEG cost function minimization procedure, potentials are selected that are affected as little as possible by the uncertain conductivity value. Using simulation data, the proposed SES methodology is able to enhance the neural source localization accuracy dependent on the dipole location, the assumed versus actual conductivity and the possible noise in measurements.

show abstract

“…The obtained results showed that the estimation of the skull conductivity improves the estimation of the underlying brain activity, especially in the case of multiple active dipoles. In addition, the proposed method was compared with the optimization techniques introduced by Vallaghé et al [12] (that requires having only one dipole active) and Gutiérrez et al [10] (that requires knowing the active dipole positions in advance). The proposed method showed very competitive results for these examples.…”

Section: Discussionmentioning

confidence: 99%

“…The operator was calculated for ρ gt = 3.6 mS/m, and white noise was added to obtain SNR = 10 dB. The proposed method is compared with two other approaches: 1) our previous model [18] with a default value ρ fix = ρ m a x +ρ m i n 2 = 18.15 mS/m; and 2) the optimization method studied by Vallaghé et al [12] that is able to estimate ρ and the brain activity jointly since there is only one active dipole for this example. The estimated dipole locations are shown in Fig.…”

Section: A Synthetic Datamentioning

confidence: 99%

“…However, most of them require very restrictive conditions to yield good results. Assumptions required by the existing methods to estimate the skull conductivity include a very good a priori knowledge Manuscript received October 16, 2016 about the active dipole positions [10], [11], the presence of only one active dipole [12], or the existence of few possible discrete values for the skull conductivity [13], [14]. In previous works [18], [19], we introduced a hierarchical Bayesian model to promote structured sparsity for recovering point-like brain activity.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Skull Conductivity Estimation for EEG Source Localization

Costa

Batatia

Oberlin

et al. 2017

IEEE Signal Process. Lett.

View full text Add to dashboard Cite

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.

show abstract

In Vivo Conductivity Estimation Using Somatosensory Evoked Potentials and Cortical Constraint on the Source

Cited by 8 publications

References 11 publications

Probabilistic forward model for electroencephalography source analysis

Probabilistic forward model for electroencephalography source analysis

Subspace electrode selection methodology for the reduction of the effect of uncertain conductivity values in the EEG dipole localization: a simulation study using a patient-specific head model

Skull Conductivity Estimation for EEG Source Localization

Contact Info

Product

Resources

About