Journal ArticleDOI
A Bayesian approach to introducing anatomo-functional priors in the EEG/MEG inverse problem
Sylvain Baillet,Line Garnero +1 more
TLDR
A new approach to the recovering of dipole magnitudes in a distributed source model for magnetoencephalographic (MEG) and electroencephalography (EEG) imaging is presented, introducing spatial and temporal a priori information as a cure to this ill-posed inverse problem.Abstract:
We present a new approach to the recovering of dipole magnitudes in a distributed source model for magnetoencephalographic (MEG) and electroencephalographic (EEG) imaging. This method consists in introducing spatial and temporal a priori information as a cure to this ill-posed inverse problem. A nonlinear spatial regularization scheme allows the preservation of dipole moment discontinuities between some a priori noncorrelated sources, for instance, when considering dipoles located on both sides of a sulcus. Moreover, we introduce temporal smoothness constraints on dipole magnitude evolution at time scales smaller than those of cognitive processes. These priors are easily integrated into a Bayesian formalism, yielding a maximum a posteriori (MAP) estimator of brain electrical activity. Results from EEG simulations of our method are presented and compared with those of classical quadratic regularization and a now popular generalized minimum-norm technique called low-resolution electromagnetic tomography (LORETA).read more
Citations
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Journal ArticleDOI
Brainstorm: a user-friendly application for MEG/EEG analysis
TL;DR: Brainstorm as discussed by the authors is a collaborative open-source application dedicated to magnetoencephalography (MEG) and EEG data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data.
Journal ArticleDOI
Electromagnetic brain mapping
TL;DR: The underlying models currently used in MEG/EEG source estimation are described and the various signal processing steps required to compute these sources are described.
Journal ArticleDOI
EEG source imaging
Christoph M. Michel,Micah M. Murray,Göran Lantz,Sara L. Gonzalez,Laurent Spinelli,Rolando Grave de Peralta +5 more
TL;DR: It is shown that modern EEG source imaging simultaneously details the temporal and spatial dimensions of brain activity, making it an important and affordable tool to study the properties of cerebral, neural networks in cognitive and clinical neurosciences.
Journal ArticleDOI
Review on solving the inverse problem in EEG source analysis
Roberta Grech,Tracey A. Cassar,Joseph Muscat,Kenneth P. Camilleri,Simon G. Fabri,Michalis Zervakis,Petros Xanthopoulos,Vangelis Sakkalis,Bart Vanrumste +8 more
TL;DR: The Monte-Carlo analysis performed, comparing WMN, LORETA, sLorETA and SLF, for different noise levels and different simulated source depths has shown that for single source localization, regularized sLORETA gives the best solution in terms of both localization error and ghost sources.
Journal ArticleDOI
Multiple sparse priors for the M/EEG inverse problem
Karl J. Friston,Lee M. Harrison,Jean Daunizeau,Stefan J. Kiebel,Christophe Phillips,Nelson J. Trujillo-Barreto,Richard N. Henson,Guillaume Flandin,Jérémie Mattout +8 more
TL;DR: The key contribution is the automatic selection of multiple cortical sources with compact spatial support that are specified in terms of empirical priors that obviates the need to use priors with a specific form (e.g., smoothness or minimum norm).
References
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Journal ArticleDOI
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Journal ArticleDOI
Multiple emitter location and signal parameter estimation
TL;DR: In this article, a description of the multiple signal classification (MUSIC) algorithm, which provides asymptotically unbiased estimates of 1) number of incident wavefronts present; 2) directions of arrival (DOA) (or emitter locations); 3) strengths and cross correlations among the incident waveforms; 4) noise/interference strength.
Journal ArticleDOI
Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain
TL;DR: The mathematical theory of the method is explained in detail, followed by a thorough description of MEG instrumentation, data analysis, and practical construction of multi-SQUID devices.
Journal ArticleDOI
Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain.
TL;DR: A direct comparison of the tomography results with those obtained from fitting one and two dipoles illustrates that the new method provides physiologically meaningful results while dipolar solutions fail in many situations.
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Improved localizadon of cortical activity by combining eeg and meg with mri cortical surface reconstruction: A linear approach
Anders M. Dale,Martin I. Sereno +1 more