Abstract Precisely localizing the sources of brain activity as recorded by EEG is a fundamental procedure and major challenge for both research clinical practice. Even though many methods algorithms have been proposed, their relative advantages limitations are still not well established. Moreover, these involve tuning multiple parameters, which no principled way selection exists yet. These uncertainties emphasized due to lack ground-truth validation testing. Here we present Localize-MI...

Electrophysiological source imaging (ESI) aims at reconstructing the precise origin of brain activity from measurements electric field on scalp. Across laboratories/research centers/hospitals, ESI is performed with different methods, partly due to ill-posedness underlying mathematical problem. However, it difficult find systematic comparisons involving a wide variety methods. Further, existing rarely take into account variability results respect input parameters. Finally, are typically using...

Abstract We present a Bayesian filtering approach for automatic estimation of dynamical source models from magnetoencephalographic data. apply multi‐target and the theory Random Finite Sets in an algorithm that recovers life times, locations strengths set dipolar sources. The reconstructed dipoles are clustered time space to associate them with applied this new method synthetic data sets show here it is able automatically estimate structure most cases more accurately than either traditional...

Electroencephalography is a non-invasive imaging modality in which primary current density generated by the neural activity brain to be reconstructed based on external electric potential measurements. This paper focuses finite element method (FEM) from both forward and inverse aspects. The goal establish clear correspondence between lowest order Raviart–Thomas basis functions dipole sources as well show that adopted FEM approach computationally effective. Each function associated with moment...

In this paper, we develop a novel Bayesian approach to the problem of estimating neural currents in brain from fixed distribution magnetic field (called topography), measured by magnetoencephalography. Differently recent studies that describe inversion techniques, such as spatio-temporal regularization/filtering, which dynamics always plays role, face here purely static inverse problem. Neural are modelled an unknown number current dipoles, whose state space is described terms...

Abstract. We consider the problem of reconstructing number size distribution (or particle distribution) in atmosphere from lidar measurements extinction and backscattering coefficients. assume that can be modeled as a superposition log-normal distributions, each one defined by three parameters: mode, width height. use Bayesian model Monte Carlo algorithm to estimate these parameters. test developed method on synthetic data generated distributions containing or two modes perturbed Gaussian...

A Rao-Blackwellized particle filter for the tracking of neural sources from biomagnetic data is described. comparison with a sampling importance resampling performed in case both simulated and real shows that use Rao-Blackwellization highly recommended since it produces more accurate reconstructions within lower computational effort.

We discuss the use of a recent class sequential Monte Carlo methods for solving inverse problems characterized by semi-linear structure, i.e. where data depend linearly on subset variables and nonlinearly remaining ones. In this type problems, under proper Gaussian assumptions one can marginalize linear variables. This means that procedure needs only to be applied nonlinear variables, while ones treated analytically; as result, variance and/or computational cost decrease. approach solve...

Automatic estimation of current dipoles from biomagnetic data is still a problematic task. This due not only to the ill-posedness inverse problem but also two intrinsic difficulties introduced by dipolar model: unknown number sources and nonlinear relationship between source locations data. Recently, we have developed new Bayesian approach, particle filtering, based on dynamical tracking dipole constellation. Contrary many dipole-based methods, filtering does assume stationarity...

The accurate characterization of cortical functional connectivity from Magnetoencephalography (MEG) data remains a challenging problem due to the subjective nature analysis, which requires several decisions at each step analysis pipeline, such as choice source estimation algorithm, metric and parcellation, name but few. Recent studies have emphasized importance selecting regularization parameter in minimum norm estimates with caution, variations its value can result significant differences...

We consider the problem of estimating neural activity from measurements magnetic fields recorded by magnetoencephalography. exploit temporal structure and model current as a collection evolving dipoles, which appear disappear, but whose locations are constant throughout their lifetime. This fully reflects physiological interpretation model. In order to conduct inference under this proposed model, it was necessary develop an algorithm based around state-of-the-art sequential Monte Carlo...

<p style='text-indent:20px;'>We present a very simple yet powerful generalization of previously described model and algorithm for estimation multiple dipoles from magneto/electro-encephalographic data. Specifically, the consists in introduction log-uniform hyperprior on standard deviation set conditionally linear/Gaussian variables. We use numerical simulations an experimental dataset to show that approximation posterior distribution remains extremely stable under wide range values...

We consider the problem of retrieving aerosol extinction coefficient from Raman lidar measurements. This is an ill--posed inverse that needs regularization, and we propose to use Expectation--Maximization (EM) algorithm provide stable solutions. Indeed, EM iterative imposes a positivity constraint on solution, provides regularization if iterations are stopped early enough. describe stopping criterion inspired by statistical principle. then discuss its properties concerning spatial...

We present a comparison of three methods for the solution magnetoencephalography inverse problem. The are: linearly constrained minimum variance beamformer, an algorithm implementing multiple signal classification with recursively applied projection and particle filter Bayesian tracking. Synthetic data neurophysiological significance are analyzed by to recover position, orientation amplitude active sources. Finally, real set evoked simple auditory stimulus is considered.

Abstract The present work aims at validating a Bayesian multi-dipole modeling algorithm (SESAME) in the clinical scenario consisting of localizing generators single interictal epileptiform discharges from resting state magnetoencephalographic recordings. We use results Equivalent Current Dipole fitting, performed by an expert user, as benchmark, and compare SESAME with those two widely used source localization methods, RAP-MUSIC wMNE. In addition, we investigate relation between...

We describe a novel method for dynamic estimation of multi-dipole states from Magneto/Electro-encephalography (M/EEG) time series. The new approach builds on the recent development particle filters M/EEG; these algorithms approximate, with samples and weights, posterior distribution neural sources at t given data up to t. However, off-line inference purposes it is preferable work smoothing distribution, i.e. conditioned whole In this study, we use Monte Carlo algorithm approximate...

The study of functional connectivity from magnetoecenphalographic (MEG) data consists quantifying the statistical dependencies among time series describing activity different neural sources magnetic field recorded outside scalp. This problem can be addressed by utilizing measures whose computation in frequency domain often relies on evaluation cross-power spectrum estimated solving MEG inverse problem. Recent studies have focused optimal determination framework regularization theory for...

We consider the problem of reconstructing cross-power spectrum an unobservable multivariate stochastic process from indirect measurements a second process, related to first one through linear operator. In two-step approach, would compute regularized reconstruction signal, and then estimate its solution. investigate whether optimal regularization parameter for signal also gives best spectrum. show that answer depends on method, specifically we prove that, under white Gaussian assumption: (i)...

The hard X-ray emission in a solar flare is typically characterized by number of discrete sources, each with its own spectral, temporal, and spatial variability. Establishing the relationship among these sources critical to determining role energy release transport processes that occur within flare. In this paper we present novel method identify characterize source emission. permits quantitative determination most likely subsources present, relative probabilities given subregion represented...

We consider imaging of solar flares from NASA Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) data as a parametric problem, where are represented finite collection geometric shapes. set up Bayesian model in which the number objects forming image is priori unknown, well their use sequential Monte Carlo algorithm to explore corresponding posterior distribution. apply method synthetic and experimental data, largely known RHESSI community. The reconstructs improved images flares,...