Archive ouverte UNIGE | last documents for author 'Teodor Alecu'https://archive-ouverte.unige.ch/Latest objects deposited in the Archive ouverte UNIGE for author 'Teodor Alecu'engBrain-computer interaction research at the Computer Vision and Multimedia Laboratory, University of Genevahttps://archive-ouverte.unige.ch/unige:48044https://archive-ouverte.unige.ch/unige:48044abstract not availableMon, 09 Mar 2015 11:34:29 +0100Research in Brain-computer interaction, Multimodal Interaction Group, Computer Vision and Multimedia Laboratory, University of Genevahttps://archive-ouverte.unige.ch/unige:48045https://archive-ouverte.unige.ch/unige:48045abstract not availableMon, 09 Mar 2015 11:34:29 +0100Local estimation with global priorshttps://archive-ouverte.unige.ch/unige:47991https://archive-ouverte.unige.ch/unige:47991abstract not availableMon, 09 Mar 2015 11:34:26 +0100The inverse problem solutions and resolutionshttps://archive-ouverte.unige.ch/unige:47992https://archive-ouverte.unige.ch/unige:47992The purpose of this document is to investigate what can and what cannot be done in terms of accuracy of the reconstruction of a still image (this term is used here with its most general meaning) from distorted measurements. This reconstruction is generally known as the inverse problem...Mon, 09 Mar 2015 11:34:26 +0100Research in Brain-computer interaction, Multimodal Interaction Group, Computer Vision and Multimedia Laboratory, University of Genevahttps://archive-ouverte.unige.ch/unige:47889https://archive-ouverte.unige.ch/unige:47889abstract not availableFri, 06 Mar 2015 17:12:21 +0100Information theoretic bit-rate optimization for average trial protocol Brain-Computer Interfaceshttps://archive-ouverte.unige.ch/unige:47809https://archive-ouverte.unige.ch/unige:47809abstract not availableFri, 06 Mar 2015 17:12:16 +0100Analyse des mesures de débit pour interfaces cerveau-ordinateurhttps://archive-ouverte.unige.ch/unige:47808https://archive-ouverte.unige.ch/unige:47808Cet article propose une comparaison entre différentes définitions de débit binaire utilisées dans la communauté des interfaces cerveau-ordinateur. Les hypothèses relatives à ces définitions et leurs limitations sont discutées. Une simulation montre que la définition du débit binaire de Nykopp est proche de la capacité discrète pour symboles équiprobables. Inversement, la définition de Wolpaw s'éloigne de cette capacité lorsque le nombre de tâches à classifier augmente, ce qui pourrait conduire à sous-estimer le débit binaire réel et à tirer des conclusions erronées au sujet du nombre de classes optimal; elle ne devrait donc pas être utilisée.Fri, 06 Mar 2015 17:12:15 +0100Localization properties of an EEG sensor system: lower bounds and optimalityhttps://archive-ouverte.unige.ch/unige:47708https://archive-ouverte.unige.ch/unige:47708Most studies concerning the EEG inverse problem focus on the properties of one or another specific inverse solution. Few studies approach the problem of the bounds imposed by the system itself, indifferently of the inversion method used. We are interested in the localization properties of an EEG sensor system using a generic reconstruction procedure in the context of a Brain Computer Interface project. We investigate various perturbations: additive noise, electrode misplacement errors and external sources contributions. The estimation of errors uses the notions of normalized measurements and sensitivity functions in a deterministic framework, but our results closely link to the stochastic CramerRao minimum bound. We propose to modify the system, and more specifically the electrodes configuration, such as to minimize the forecasted errors, thus enhancing the robustness of the system. The configurations obtained through a hybrid Simulated Annealing - Gradient Descent approach show significant improvement when compared to normal setups.Fri, 06 Mar 2015 17:12:09 +0100Regularized two-step brain activity reconstruction from spatio-temporal EEG datahttps://archive-ouverte.unige.ch/unige:47710https://archive-ouverte.unige.ch/unige:47710We are aiming at using EEG source localization in the framework of a Brain Computer Interface project. We propose here a new reconstruction procedure, targeting source (or equivalently mental task) differentiation. EEG data can be thought of as a collection of time continuous streams from sparse locations. The measured electric potential on one electrode is the result of the superposition of synchronized synaptic activity from sources in all the brain volume. Consequently, the EEG inverse problem is a highly underdetermined (and ill-posed) problem. Moreover, each source contribution is linear with respect to its amplitude but non-linear with respect to its localization and orientation. In order to overcome these drawbacks we propose a novel two-step inversion procedure. The solution is based on a double scale division of the solution space. The first step uses a coarse discretization and has the sole purpose of globally identifying the active regions, via a sparse approximation algorithm. The second step is applied only on the retained regions and makes use of a fine discretization of the space, aiming at detailing the brain activity. The local configuration of sources is recovered using an iterative stochastic estimator with adaptive joint minimum energy and directional consistency constraints.Fri, 06 Mar 2015 17:12:09 +0100The Gaussian transformhttps://archive-ouverte.unige.ch/unige:47707https://archive-ouverte.unige.ch/unige:47707This paper introduces the general purpose Gaussian Transform, which aims at representing a generic symmetric distribution as an infinite mixture of Gaussian distributions. We start by the mathematical formulation of the problem and continue with the investigation of the conditions of existence of such a transform. Our analysis leads to the derivation of analytical and numerical tools for the computation of the Gaussian Transform, mainly based on the Laplace and Fourier transforms, as well as of the afferent properties set (e.g. the transform of sums of independent variables). Finally, the Gaussian Transform is exemplified in analytical form for typical distributions (e.g. Gaussian, Laplacian), and in numerical form for the Generalized Gaussian and Generalized Cauchy distributions families.Fri, 06 Mar 2015 17:12:09 +0100EEG Cortical Imaging: A Vector Field Approach For Laplacian Denoising And Missing Data Estimationhttps://archive-ouverte.unige.ch/unige:47709https://archive-ouverte.unige.ch/unige:47709The surface Laplacian is known to be a theoretical reliable approximation of the cortical activity. Unfortunately, because of its high pass character and the relative low density of the EEG caps, the estimation of the Laplacian itself tends to be very sensitive to noise. We introduce a method based on vector field regularization through diffusion for denoising the Laplacian data and thus obtain robust estimation. We use a forward-backward diffusion aiming for source energy minimization while preserving contrasts between active and nonactive regions. This technique uses headcap geometry specific differential operators to counter the low sensor density. The comparison with classical denoising schemes clearly demonstrates the advantages of our method. We also propose an algorithm based on the Gauss-Ostrogradsky theorem for estimation of the Laplacian on missing (bad) electrodes, which can be combined with the regularization technique in order to provide a joint validation framework.Fri, 06 Mar 2015 17:12:09 +0100Soft/hard focalization in the EEG inverse problemhttps://archive-ouverte.unige.ch/unige:47712https://archive-ouverte.unige.ch/unige:47712We present in this paper a novel statistical based focalized reconstruction method for the underdetermined EEG (electroencephalogram) inverse problem. The algorithm is based on the representation of non-Gaussian distributions as an infinite mixture of Gaussians (IMG) and relies on an iterative procedure consisting out of alternated variance estimation/linear inversion operations. By taking into account noise statistics, it performs implicit spurious data rejection and produces robust focalized solutions allowing for straightforward discrimination of active/non-active brain regions. We apply the proposed reconstruction procedure to average evoked potentials EEG data and compare the reconstruction results with the corresponding known physiological responses.Fri, 06 Mar 2015 17:12:09 +0100Localization properties of an EEG sensor system : electrode misplacement sensitivityhttps://archive-ouverte.unige.ch/unige:47695https://archive-ouverte.unige.ch/unige:47695abstract not availableFri, 06 Mar 2015 17:12:08 +0100Denoising with infinite mixture of Gaussianshttps://archive-ouverte.unige.ch/unige:47706https://archive-ouverte.unige.ch/unige:47706We show in this paper how an Infinite Mixture of Gaussians model can be used to estimate/denoise non-Gaussian data with local linear estimators based on the Wiener filter. The decomposition of the data in Gaussian components is straightforwardly computed with the Gaussian Transform, previously derived in [2]. The estimation is based on a two-step procedure, the first step consisting in variance estimation, and the second step in data estimation through Wiener filtering. We propose new generic variance estimators based on the Infinite Gaussian Mixture prior such as the cumulative estimator or the local-global estimator, as well as more classical Bayesian estimators. Results are presented in terms of distortion for the case of Generalized Gaussian data.Fri, 06 Mar 2015 17:12:08 +0100The Gaussian transform of distributions : definition, computation and applicationhttps://archive-ouverte.unige.ch/unige:47435https://archive-ouverte.unige.ch/unige:47435This paper introduces the general-purpose Gaussian transform of distributions, which aims at representing a generic symmetric distribution as an infinite mixture of Gaussian distributions. We start by the mathematical formulation of the problem and continue with the investigation of the conditions of existence of such a transform. Our analysis leads to the derivation of analytical and numerical tools for the computation of the Gaussian transform, mainly based on the Laplace and Fourier transforms, as well as of the afferent properties set (e.g., the transform of sums of independent variables). The Gaussian transform of distributions is then analytically derived for the Gaussian and Laplacian distributions, and obtained numerically for the generalized Gaussian and the generalized Cauchy distribution families. In order to illustrate the usage of the proposed transform we further show how an infinite mixture of Gaussians model can be used to estimate/denoise non-Gaussian data with linear estimators based on the Wiener filter. The decomposition of the data into Gaussian components is straightforwardly computed with the Gaussian transform, previously derived. The estimation is then based on a two-step procedure: the first step consists of variance estimation, and the second step consists of data estimation through Wiener filtering. To this purpose, we propose new generic variance estimators based on the infinite mixture of Gaussians prior. It is shown that the proposed estimators compare favorably in terms of distortion with the shrinkage denoising technique and that the distortion lower bound under this framework is lower than the classical minimum mean-square error bound.Tue, 03 Mar 2015 16:36:12 +0100Brain-computer interaction research at the Computer Vision and Multimedia Laboratory, University of Genevahttps://archive-ouverte.unige.ch/unige:47430https://archive-ouverte.unige.ch/unige:47430This paper describes the work being conducted in the domain of brain-computer interaction (BCI) at the Multimodal Interaction Group, Computer Vision and Multimedia Laboratory, University of Geneva, Geneva, Switzerland. The application focus of this work is on multimodal interaction rather than on rehabilitation, that is how to augment classical interaction by means of physiological measurements. Three main research topics are addressed. The first one concerns the more general problem of brain source activity recognition from EEGs. In contrast with classical deterministic approaches, we studied iterative robust stochastic based reconstruction procedures modeling source and noise statistics, to overcome known limitations of current techniques. We also developed procedures for optimal electroencephalogram (EEG) sensor system design in terms of placement and number of electrodes. The second topic is the study of BCI protocols and performance from an information-theoretic point of view. Various information rate measurements have been compared for assessing BCI abilities. The third research topic concerns the use of EEG and other physiological signals for assessing a user's emotional status.Tue, 03 Mar 2015 16:36:11 +0100Robust focalized brain activity reconstruction using electroencephalogramshttps://archive-ouverte.unige.ch/unige:46163https://archive-ouverte.unige.ch/unige:46163This thesis is developed around the problem of brain activity reconstruction/identification using non-invasive ElectroEncephaloGrams. Multiple research lines branch from the central research line, some of them constituting distinct reconstruction methods, and others serving as aiding tools to the reconstructions. Noticeably, the application area of some of the proposed tools is broader than the original considered problem. The presented algorithms share the common preoccupation for statistical robustness and focalization, aiming to provide the users of the methods with solutions allowing as much as possible straightforward identification/discrimination of active areas from inactive areas. The work tackles first the problem of 2D surfacic cortical reconstruction, proposing an anisotropic edge-preserving vector diffusion algorithm, inspired by the relatively recent emergence of diffusion algorithms in the image processing community. Subsequently, the 3D volumetric reconstruction problem is attacked under the perspective of statistical estimation. A thorough analysis of the reconstruction bounds, based on the introduction of the sensitivity functions and on the Cramér-Rao lower bound, results in principles for optimal system design. Then an alternate statistical modeling is proposed for non-Gaussian distributions, under the form of Infinite Mixture of Gaussians, using the newly introduced Gaussian Transform of distributions. Relying on the obtained results, new generic estimation algorithms are proposed for non-Gaussian data and tested in a denoising application. Their extension to the EEG inverse problem produces a new class of EEG inversion methods, yielding robust focalized solutions under non-Gaussian priors.Mon, 02 Feb 2015 13:29:59 +0100