Tensor-Based Preprocessing of Combined EEG/MEG Data

5 pagesInternational audienceDue to their good temporal resolution, electroencephalography (EEG) and magnetoencephalography (MEG) are two often used techniques for brain source analysis. In order to improve the results of source localization algorithms applied to EEG or MEG data, tensor-based preprocessing techniques can be used to separate the sources and reduce the noise. These methods are based on the Canonical Polyadic (CP) decomposition (also called Parafac) of space-time-frequency (STF) or space-time-wave-vector (STWV) data. In this paper, we analyze the combination of EEG and MEG data to enhance the performance of the tensor-based preprocessing. To this end, we consider the joint CP decomposition of two (or more) third order tensors with one or two identical loading matrices. We present the necessary modifications for several classical CP decomposition algorithms and examine the gain on performance in the EEG/MEG context by means of simulations

ICAR, a tool for Blind Source Separation using Fourth Order Statistics only

International audienceThe problem of blind separation of overdetermined mixtures of sources, that is, with fewer sources than (or as many sources as) sensors, is addressed in this paper. A new method, named ICAR (Independent Component Analysis using Redundancies in the quadricovariance), is proposed in order to process complex data. This method, without any whitening operation, only exploits some redundancies of a particular quadricovariance matrix of the data. Computer simulations demonstrate that ICAR offers in general good results and even outperforms classical methods in several situations: ICAR ~(i) succeeds in separating sources with low signal to noise ratios, ~(ii) does not require sources with different SO or/and FO spectral densities, ~(iii) is asymptotically not affected by the presence of a Gaussian noise with unknown spatial correlation, (iv) is not sensitive to an over estimation of the number of sources

Localization of extended brain sources from EEG/MEG: The ExSo-MUSIC approach.

International audienceWe propose a new MUSIC-like method, called 2q-ExSo-MUSIC (q≥1). This method is an extension of the 2q-MUSIC (q≥1) approach for solving the EEG/MEG inverse problem, when spatially-extended neocortical sources ("ExSo") are considered. It introduces a novel ExSo-MUSIC principle. The novelty is two-fold: i) the parameterization of the spatial source distribution that leads to an appropriate metric in the context of distributed brain sources and ii) the introduction of an original, efficient and low-cost way of optimizing this metric. In 2q-ExSo-MUSIC, the possible use of higher order statistics (q≥2) offers a better robustness with respect to Gaussian noise of unknown spatial coherence and modeling errors. As a result we reduced the penalizing effects of both the background cerebral activity that can be seen as a Gaussian and spatially correlated noise, and the modeling errors induced by the non-exact resolution of the forward problem. Computer results on simulated EEG signals obtained with physiologically-relevant models of both the sources and the volume conductor show a highly increased performance of our 2q-ExSo-MUSIC method as compared to the classical 2q-MUSIC algorithms

An Alternating Direction Method of Multipliers for Constrained Joint Diagonalization by Congruence (Invited Paper)

International audienceIn this paper, we address the problem of joint diagonalization by congruence (i.e. the canonical polyadic decomposition of semi-symmetric 3rd order tensors) subject to arbitrary convex constraints. Sufficient conditions for the existence of a solution are given. An efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM) is then designed. ADMM provides an elegant approach for handling the additional constraint terms, while taking advantage of the structure of the objective function. Numerical tests on simulated matrices show the benefits of the proposed method for low signal to noise ratios. Simulations in the context of nuclear magnetic resonance spectroscopy are also provided

Medical image registration using Edgeworth-based approximation of Mutual Information

International audienceWe propose a new similarity measure for iconic medical image registration, an Edgeworth-based third order approximation of Mutual Information (MI) and named 3-EMI. Contrary to classical Edgeworth-based MI approximations, such as those proposed for inde- pendent component analysis, the 3-EMI measure is able to deal with potentially correlated variables. The performance of 3-EMI is then evaluated and compared with the Gaussian and B-Spline kernel-based estimates of MI, and the validation is leaded in three steps. First, we compare the intrinsic behavior of the measures as a function of the number of samples and the variance of an additive Gaussian noise. Then, they are evaluated in the context of multimodal rigid registration, using the RIRE data. We finally validate the use of our measure in the context of thoracic monomodal non-rigid registration, using the database proposed during the MICCAI EMPIRE10 challenge. The results show the wide range of clinical applications for which our measure can perform, including non-rigid registration which remains a challenging problem. They also demonstrate that 3-EMI outperforms classical estimates of MI for a low number of samples or a strong additive Gaussian noise. More generally, our measure gives competitive registration results, with a much lower numerical complexity compared to classical estimators such as the reference B-Spline kernel estimator, which makes 3-EMI a good candidate for fast and accurate registration tasks

A Performance Study of various Brain Source Imaging Approaches

International audienceThe objective of brain source imaging consists in reconstructing the cerebral activity everywhere within the brain based on EEG or MEG measurements recorded on the scalp. This requires solving an ill-posed linear inverse problem. In order to restore identifiability, additional hypotheses need to be imposed on the source distribution, giving rise to an impressive number of brain source imaging algorithms. However, a thorough comparison of different methodologies is still missing in the literature. In this paper, we provide an overview of priors that have been used for brain source imaging and conduct a comparative simulation study with seven representative algorithms corresponding to the classes of minimum norm, sparse, tensor-based, subspace-based, and Bayesian approaches. This permits us to identify new benchmark algorithms and promising directions for future research

Turning Tangent Empirical Mode Decomposition: A Framework for Mono- and Multivariate Signals.

International audienceA novel Empirical Mode Decomposition (EMD) algorithm, called 2T-EMD, for both mono- and multivariate signals is proposed in this paper. It differs from the other approaches by its computational lightness and its algorithmic simplicity. The method is essentially based on a redefinition of the signal mean envelope, computed thanks to new characteristic points, which offers the possibility to decompose multivariate signals without any projection. The scope of application of the novel algorithm is specified, and a comparison of the 2T-EMD technique with classical methods is performed on various simulated mono- and multivariate signals. The monovariate behaviour of the proposed method on noisy signals is then validated by decomposing a fractional Gaussian noise and an application to real life EEG data is finally presented

SAUD, un algorithme d'ICA par déflation semi-algébrique

Nous proposons dans cet article une nouvelle technique d'ICA baptisée SAUD (Semi-Algebraic Unitary Deflation) et basée sur une procédure de déflation unitaire semi-algébrique. SAUD permet ainsi d'identifier une à une les composantes indépendantes sans souffrir des inconvénients des approches adaptatives (lenteur de convergence, etc.). Des simulations numériques en contexte biomédical (EEG) illustrent le bon comportement de SAUD face à COM2, DEFA et FastICA

Blind Source Separation Methods Applied to Muscle Artefacts Removing from Epileptic Eeg Recording: A Comparative Study.

International audienceElectroencephalogram (EEG) recordings are often contaminated with muscle artifacts. These artifacts obscure the EEG and complicate its interpretation or even make the interpretation unfeasible. In this paper, realistic spike EEG signals are simulated from the activation of a 5 cm2 epileptic patch in the left superior temporal gyrus. Background activities and real muscle artifacts are then added to the simulated data. We compare the efficiency of Empirical Mode Decomposition (EMD), Independent Component Analysis (ICA) and Blind Source Separation based on Canonical Correlation Analysis (BSS-CCA) to remove muscle artifacts from the EEG signals. The quantitative comparison indicates that the EMD approach exhibits a better performance than ICA and BSS-CCA, especially in the case of very low Signal to Noise Ratio (SNR)

ICAR, un algorithme d'ICA à convergence rapide, robuste au bruit

- Une nouvelle méthode de séparation aveugle de sources, baptisée ICAR et n'exploitant que les statistiques d'ordre 4 des observations, est proposée. Cette dernière est comparée par simulations aux méthodes usuelles, COM1, COM2, JADE et FastICA. Sa vitesse de convergence et sa robustesse à la cohérence spatiale (inconnue du récepteur) d'un bruit gaussien font d'ICAR l'un des algorithmes les plus performants à l'heure actuelle