Bayesian EEG source localization using a structured sparsity prior

Costa, Facundo; Batatia, Hadj; Oberlin, Thomas; D’Giano, Carlos; Tourneret, Jean-Yves

doi:10.1016/j.neuroimage.2016.08.064

Cited by 33 publications

(41 citation statements)

References 40 publications

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“…As mentioned in [18], the sampler presented above may get stuck around local maxima of the target distribution. Multiple-dipole shift (MDS) and interchain (IC) proposals for the dipoles location were introduced in [18] to solve this issue.…”

Section: Proposalsmentioning

confidence: 99%

“…This section generalizes the hierarchical Bayesian model of [18] to include the skull conductivity. The priors used for each of the model parameters and hyperparameters (except for ρ) are the same as were used in [18] and are summarized in Table I, where B, G, and U stand for the Bernoulli, gamma, and uniform distributions, respectively, δ is the Dirac delta function, m i denotes the ith row of M , 1 R + (x) = 1 is the indicator function on R + , and α = β = 1.…”

Section: B Prior Distributionsmentioning

confidence: 99%

“…The priors used for each of the model parameters and hyperparameters (except for ρ) are the same as were used in [18] and are summarized in Table I, where B, G, and U stand for the Bernoulli, gamma, and uniform distributions, respectively, δ is the Dirac delta function, m i denotes the ith row of M , 1 R + (x) = 1 is the indicator function on R + , and α = β = 1. In this table, we can see that each row x i is either jointly zero or nonzero, which is indicated by the discrete variable z i .…”

Section: B Prior Distributionsmentioning

confidence: 99%

“…Finally, the noise variance σ 2 n is assigned a Jeffrey's prior reflecting the absence of knowledge about this parameter. The reader is invited to consult [18] for more motivations and details about the Bayesian model used for EEG source localization.…”

Section: B Prior Distributionsmentioning

confidence: 99%

“…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. In the present letter (and the associated technical report [20]), we generalize this model to estimate the skull conductivity jointly with the brain activity in a completely unsupervised framework.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Proposalsmentioning

confidence: 99%

Section: B Prior Distributionsmentioning

confidence: 99%

Section: B Prior Distributionsmentioning

confidence: 99%

Section: B Prior Distributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Skull Conductivity Estimation for EEG Source Localization

Costa

Batatia

Oberlin

et al. 2017

IEEE Signal Process. Lett.

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Scalable spatio‐temporal Bayesian analysis of high‐dimensional electroencephalography data

Mohammed

Dey

2021

Can J Statistics

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We present a scalable Bayesian modelling approach for identifying brain regions that respond to a certain stimulus and use them to classify subjects. More specifically, we deal with multi-subject electroencephalography (EEG) data with a binary response distinguishing between alcoholic and control groups. The covariates are matrix-variate with measurements taken from each subject at different locations across multiple time points. EEG data have a complex structure with both spatial and temporal attributes. We use a divide-and-conquer strategy and build separate local models, that is, one model at each time point. We employ Bayesian variable selection approaches using a structured continuous spike-and-slab prior to identify the locations that respond to a certain stimulus. We incorporate the spatio-temporal structure through a Kronecker product of the spatial and temporal correlation matrices. We develop a highly scalable estimation algorithm, using likelihood approximation, to deal with large number of parameters in the model. Variable selection is done via clustering of the locations based on their duration of activation. We use scoring rules to evaluate the prediction performance. Simulation studies demonstrate the efficiency of our scalable algorithm in terms of estimation and fast computation. We present results using our scalable approach on a case study of multi-subject EEG data.

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