Location of Sources of Evoked Scalp Potentials: Corrections for Skull and Scalp Thicknesses

Ary, James P.; Klein, Stanley A.; Fender, Derek H.

doi:10.1109/tbme.1981.324817

Cited by 295 publications

(110 citation statements)

References 13 publications

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“…1. The orientations of each source were fixed and the lead-field L for all the sources calculated analytically (Ary et al, 1981). The density of grey matter was assumed to vary over the dipole location as shown in Fig.…”

Section: Simplified Model and Synthetic Datamentioning

confidence: 99%

An empirical Bayesian solution to the source reconstruction problem in EEG

et al. 2005

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Distributed linear solutions of the EEG source localisation problem are used routinely. In contrast to discrete dipole equivalent models, distributed linear solutions do not assume a fixed number of active sources and rest on a discretised fully 3D representation of the electrical activity of the brain. The ensuing inverse problem is underdetermined and constraints or priors are required to ensure the uniqueness of the solution. In a Bayesian framework, the conditional expectation of the source distribution, given the data, is attained by carefully balancing the minimisation of the residuals induced by noise and the improbability of the estimates as determined by their priors. This balance is specified by hyperparameters that control the relative importance of fitting and conforming to various constraints. Here we formulate the conventional bWeighted Minimum NormQ (WMN) solution in terms of hierarchical linear models. An bExpectation-MaximisationQ (EM) algorithm is used to obtain a bRestricted Maximum LikelihoodQ (ReML) estimate of the hyperparameters, before estimating the bMaximum a PosterioriQ solution itself. This procedure can be considered a generalisation of previous work that encompasses multiple constraints. Our approach was compared with the bclassicQ WMN and Maximum Smoothness solutions, using a simplified 2D source model with synthetic noisy data. The ReML solution was assessed with four types of source location priors: no priors, accurate priors, inaccurate priors, and both accurate and inaccurate priors. The ReML approach proved useful as: (1) The regularisation (or influence of the a priori source covariance) increased as the noise level increased. (2) The localisation error (LE) was negligible when accurate location priors were used. (3) When accurate and inaccurate location priors were used simultaneously, the solution was not influenced by the inaccurate priors. The ReML solution was then applied to real somatosensory-evoked responses to illustrate the application in an empirical setting. D

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Section: Simplified Model and Synthetic Datamentioning

confidence: 99%

An empirical Bayesian solution to the source reconstruction problem in EEG

et al. 2005

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“…The potential somewhere else in the sphere and the potential based on more complicated spherical models of the head can not be expressed in a closed-form but will contain an infinite series of summations. To perform a series summation is rather time consuming and therefore several groups use the expression for the potential at the surface of a homogeneous sphere and make corrections so that effectively one uses a model consisting of a sphere surrounded by two concentric shells representing the skull and the scalp (Ary et al 1981). …”

Section: Methods Of Harmonicsmentioning

confidence: 99%

The influence of the volume conductor on electric source estimation

Peters

Wieringa

1993

Brain Topogr

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“…In EEG source problem, neural sources are reconstructed inside the brain based on electric potential measurements around the scalp, and it is well known that the inverse solution depends strongly on the accuracy of discretized head geometry [2,4,7,8,16,39,49,54] and the accuracy of electric conductivity modelling of different tissues [3,47,25,48,34,50,51]. The head features can be extracted, to some extent, by using multi-modal imaging (computed tomography / diffusion magnetic resonance imaging), for example.…”

Section: Introductionmentioning

confidence: 99%