Spatio Temporal EEG Source Imaging with the Hierarchical Bayesian Elastic Net and Elitist Lasso Models

Paz-Linares, Deirel; Vega‐Hernández, Mayrim; Rojas-López, Pedro A.; Valdés-Hernández, Pedro A.; Martı́nez-Montes, Eduardo; Valdés‐Sosa, Pedro A.

doi:10.3389/fnins.2017.00635

Cited by 29 publications

(33 citation statements)

References 62 publications

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“…See for example [129] in which the use of non-negativity, orthogonality and sparseness reduce leakage. In a similar vein it has also been shown that Structured Sparse Bayesian Learning (SSBL) [127] may also achieve similar gains with respect to the usual linear minimum norm inverse methods.…”

Section: E Source Leakage In Connectivity Estimatesmentioning

confidence: 85%

“…Alternatively, beamforming such as linearly constrained minimum variance or its variants [123], [124] or source scanning strategy such as MUSIC and its variants [125], [126], can be used to estimate source distributions. Recently, sparsity and other properties such as nonnegativity and orthogonality have been pursued to obtain enhanced source imaging and localization results [127]- [131]. See [111] for a recent review of EEG/MEG source imaging and localization methods.…”

Section: A Source Imaging and Localizationmentioning

confidence: 99%

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Electrophysiological Brain Connectivity: Theory and Implementation

Astolfi

Valdés‐Sosa

et al. 2019

IEEE Trans. Biomed. Eng.

188

144

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We review the theory and algorithms of electrophysiological brain connectivity analysis. This tutorial is aimed at providing an introduction to brain functional connectivity from electrophysiological signals, including electroencephalography, magnetoencephalography, electrocorticography, and stereoelectroencephalography. Various connectivity estimators are discussed, and algorithms introduced. Important issues for estimating and mapping brain functional connectivity with electrophysiology are discussed.Index Terms-Brain functional connectivity, electrophysiological connectivity, effective connectivity, EEG, MEG, intracranial EEG, electrophysiological connectome.

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Section: E Source Leakage In Connectivity Estimatesmentioning

confidence: 85%

Section: A Source Imaging and Localizationmentioning

confidence: 99%

Electrophysiological Brain Connectivity: Theory and Implementation

Astolfi

Valdés‐Sosa

et al. 2019

IEEE Trans. Biomed. Eng.

188

144

View full text Add to dashboard Cite

show abstract

“…More biophysical information may be built into the prior distributions to more effectively differentiate the EEG signal from the sensor noise. Particularly, covariance matrices corresponding to different types of structured sparsity source models should be examined (Paz-Linares et al, 2017 ).…”

Section: Discussionmentioning

confidence: 99%

Unified Bayesian Estimator of EEG Reference at Infinity: rREST (Regularized Reference Electrode Standardization Technique)

Yao

Valdés‐Sosa

2018

Front. Neurosci.

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The choice of reference for the electroencephalogram (EEG) is a long-lasting unsolved issue resulting in inconsistent usages and endless debates. Currently, both the average reference (AR) and the reference electrode standardization technique (REST) are two primary, apparently irreconcilable contenders. We propose a theoretical framework to resolve this reference issue by formulating both (a) estimation of potentials at infinity, and (b) determination of the reference, as a unified Bayesian linear inverse problem, which can be solved by maximum a posterior estimation. We find that AR and REST are very particular cases of this unified framework: AR results from biophysically non-informative prior; while REST utilizes the prior based on the EEG generative model. To allow for simultaneous denoising and reference estimation, we develop the regularized versions of AR and REST, named rAR and rREST, respectively. Both depend on a regularization parameter that is the noise to signal variance ratio. Traditional and new estimators are evaluated with this framework, by both simulations and analysis of real resting EEGs. Toward this end, we leverage the MRI and EEG data from 89 subjects which participated in the Cuban Human Brain Mapping Project. Generated artificial EEGs—with a known ground truth, show that relative error in estimating the EEG potentials at infinity is lowest for rREST. It also reveals that realistic volume conductor models improve the performances of REST and rREST. Importantly, for practical applications, it is shown that an average lead field gives the results comparable to the individual lead field. Finally, it is shown that the selection of the regularization parameter with Generalized Cross-Validation (GCV) is close to the “oracle” choice based on the ground truth. When evaluated with the real 89 resting state EEGs, rREST consistently yields the lowest GCV. This study provides a novel perspective to the EEG reference problem by means of a unified inverse solution framework. It may allow additional principled theoretical formulations and numerical evaluation of performance.

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“…This is another reason to provide accurate forward head models. Abundant inverse solutions using of these contraints have been developed and implemented, examples being weighted minimum-norm estimation (WMNE) (Hämäläinen et al 1994), beamforming (BF) (Grech et al 2008;Van Veen et al 1997), Blind Source Separation (BSS) , exact Low Resolution Electromagnetic Tomography (eLORETA) (Pascual-Marqui 2007), Sparse Structural Bayesian Learning (SSBL) (Paz-Linares et al 2017), and so on. This problem is even more difficult for source connectivity estimation, as an aspect that we will pay a close attention.…”

Section: Introductionmentioning

confidence: 99%

EECoG-Comp: An Open Source Platform for Concurrent EEG/ECoG Comparisons: applications to connectivity stuides

Wang

Valdés-Hernández

Paz-Linares

et al. 2018

Preprint

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Electrophysiological Source Imaging (ESI) methods are hampered by the lack of "gold standards" for model comparison. Concurrent electroencephalography (EEG) and electrocorticography (ECoG) recordings (namely EECoG) are considered gold standard to validating EEG generative models with primate models have the unique advantages of both flexibility and translational value in human research. However the severe EEG artifacts during such invasive experiments, the complexity of providing sufficiently detailed biophysical models, as well as lacking sound statistical connectivity comparison methods have hampered the availability and analysis of such datasets. In this paper, 1) we provide EECoG-Comp: an open source platform (https://github.com/Vincent-wq/EECoG-Comp) which encompasses the preprocessing, forward modeling, simulation and comparison module; 2) we take the simultaneous EECoG dataset from www.neurotycho.org as an example to illustrate the use of this platform and compare the source connectivity estimation performance of 4 popular ESI methods named MNE, LCMV, eLORETA and SSBL. The conclusion shows the limits of performance of these ESI connectivity estimators using both simulations and real data analysis. In fact, the use of this platform also suggests the need for both improved simultaneous EEG and ECoG experiments and ESI connectivity estimators.2 Noteworthily, more recent and realistic phantoms, even 3D printed, are now available for these tests (Collier et al., 2012; Li et. Al 2013; Zhang et al. 2013). source setting are: , rand ESI H,eeg,Θ J (human EEG with different ESI methods) and , ,, rand ESI rand ESI M,eeg,Θ M,ecog,Θ J J (monkey EEG and ECoG source activities estimated from different ESI methods), and the source estimators for the block source setting are , block ESI H,eeg,Θ J (human EEG with different ESI methods) and , ,, block ESI block ESI M,eeg,Θ M,ecog,Θ J J (monkey EEG and ECoG source activities estimated from different ESI methods). With this simulation, we have both the "ground truth" source activities , rand block Θ Θ J J with the "ground truth" source connecitivity , rand block   as connectivity, and their corresponding estimators from human EEG, monkey EEG and monkey ECoG simulations with different number of sources, different number of sensors and different ESI methods. Before further analysis, we bandpass all the estimated source activation to 8-12Hz and limit the connectivity estimations to alpha band. In order to compare connectivity from different ESI results, we use graphical LASSO (Hsieh et al. 2014) to reconstruct the connectivity estimators (ˆ, rand block   ) and compare them with the tools provided by EECoG_conComp. Notice that, for the source activities, we have the "ground truth" , rand block Θ Θ J J , and we can estimate the source connectivity from them without applying any ESI methods, which is taken as the basic performance to compared against. Finally, we summaize the simulation results with Performance Improvement Ratio ( PIR ESI ) of a specific ESI method, which is d...

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Spatio Temporal EEG Source Imaging with the Hierarchical Bayesian Elastic Net and Elitist Lasso Models

Cited by 29 publications

References 62 publications

Electrophysiological Brain Connectivity: Theory and Implementation

Electrophysiological Brain Connectivity: Theory and Implementation

Unified Bayesian Estimator of EEG Reference at Infinity: rREST (Regularized Reference Electrode Standardization Technique)

EECoG-Comp: An Open Source Platform for Concurrent EEG/ECoG Comparisons: applications to connectivity stuides

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