EEG reconstruction and skull conductivity estimation using a Bayesian model promoting structured sparsity

Costa, Facundo; Batatia, Hadj; Oberlin, Thomas; Tourneret, Jean-Yves

doi:10.48550/arxiv.1609.06874

Cited by 2 publications

(7 citation statements)

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“…We also note that in this work we estimated a single conductivity value for the whole skull. As a next step, we plan to use the proposed approach in conjugation with other optimization techniques [15,16] to produce more detailed skull conductivity maps.…”

Section: Results Discussion and Future Workmentioning

confidence: 99%

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Simultaneous Skull Conductivity and Focal Source Imaging from EEG Recordings with the help of Bayesian Uncertainty Modelling

Koulouri¹,

Rimpiläinen²

2020

Preprint

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The electroencephalography (EEG) source imaging problem is very sensitive to the electrical modelling of the skull of the patient under examination. Unfortunately, the currently available EEG devices and their embedded software do not take this into account; instead, it is common to use a literature-based skull conductivity parameter. In this paper, we propose a statistical method based on the Bayesian approximation error approach to compensate for source imaging errors due to the unknown skull conductivity and, simultaneously, to compute a low-order estimate for the actual skull conductivity value. By using simulated EEG data that corresponds to focal source activity, we demonstrate the potential of the method to reconstruct the underlying focal sources and loworder errors induced by the unknown skull conductivity. Subsequently, the estimated errors are used to approximate the skull conductivity. The results indicate clear improvements in the source localization accuracy and feasible skull conductivity estimates.

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Section: Results Discussion and Future Workmentioning

confidence: 99%

“…, the joint distribution of σ and α l can be approximated as Gaussian, and therefore the MAP estimate (16) gives…”

Section: Simultaneous Skull Conductivity and Source Estimationmentioning

confidence: 99%

Simultaneous Skull Conductivity and Focal Source Imaging from EEG Recordings with the help of Bayesian Uncertainty Modelling

Koulouri¹,

Rimpiläinen²

2020

Preprint

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“…A different approach of lead field matrix approximation is presented in [10], [15], where a polynomial matrix of degree n is used to approximate the exact lead fields. This method has significant differences compared to ours.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, the approach in [10], [15] has several advantages. It allows to use forward solution computation as a black box without any knowledge of its structure (i. e. matrices S, H σ and D σ ) or of their dependence on conductivity.…”

Section: Discussionmentioning

confidence: 99%

“…Our method is inspired by the reduced basis method, but adapted to the particular structure of the EEG forward problem and the nature of its conductivity parameter space. The previous presented method [10], [15] is limited to one unknown conductivity: elements of the lead field matrix are approximated using polynomial interpolation based on a set of precomputed values. With this approach, the complexity of the approximation would increase fast with the number of unknown conductivities.…”

Section: Introductionmentioning

confidence: 99%

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Fast Approximation of EEG Forward Problem and Application to Tissue Conductivity Estimation

Maksymenko¹,

Clerc²,

Papadopoulo³

2020

IEEE Trans. Med. Imaging

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Bioelectric source analysis in the human brain from scalp electroencephalography (EEG) signals is sensitive to the conductivities of different head tissues. The conductivity of tissues is subject dependent, so non-invasive methods for conductivity estimation are necessary to fine tune EEG models. To do so, the EEG forward problem solution (so-called lead field matrix) must be computed for a large number of conductivity configurations.Computing a lead field requires a matrix inversion which is computationally intensive for realistic head models. Thus, the required time for computing a large number of lead fields can become impractical. In this work, we propose to approximate the lead field matrix for a set of conductivity configurations, using the exact solution only for a small set of support points in the conductivity space. Our approach accelerates the computation time, while controlling the approximation error.Our method is tested on simulated and measured EEG data for brain and skull conductivity estimation. This test demonstrates that the approximation does not introduce any bias and runs significantly faster than if exact lead field were to be computed.Index Terms-EEG forward problem, EEG inverse problem, conductivity estimation, lead field matrix approximation.K. Maksymenko is with Athena,

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EEG reconstruction and skull conductivity estimation using a Bayesian model promoting structured sparsity

Cited by 2 publications

References 0 publications

Simultaneous Skull Conductivity and Focal Source Imaging from EEG Recordings with the help of Bayesian Uncertainty Modelling

Simultaneous Skull Conductivity and Focal Source Imaging from EEG Recordings with the help of Bayesian Uncertainty Modelling

Fast Approximation of EEG Forward Problem and Application to Tissue Conductivity Estimation

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