Disertační práce se zabývá lokalizací zdrojů zvuku a akustickým zoomem. Hlavním cílem této práce je navrhnout systém s akustickým zoomem, který přiblíží zvuk jednoho mluvčího mezi skupinou mluvčích, a to i když mluví současně. Tento systém je kompatibilní s technikou prostorového zvuku. Hlavní přínosy disertační práce jsou následující: 1. Návrh metody pro odhad více směrů přicházejícího zvuku. 2. Návrh metody pro akustické zoomování pomocí DirAC. 3. Návrh kombinovaného systému pomocí předchozích kroků, který může být použit v telekonferencích.

L’imagerie de source corticale joue un rôle important pour la compréhension fonctionnelle ou pathologique du cerveau. Elle permet d'estimer l'activation de certaines zones corticales en réponse à un stimulus cognitif donné et elle est également utile pour identifier la localisation des activités pathologiques, qui sont les premières étapes de l'étude des activations de réseaux neuronaux sous-jacents. Diverses méthodes d'investigation clinique peuvent être utilisées, des modalités d'imagerie (TEP, IRM) et magnéto-électroencéphalographie (EEG, SEEG, MEG). Nous souhaitions résoudre le problème à partir de données non invasives : les mesures de l'EEG de scalp, elle procure une résolution temporelle à la hauteur des processus étudiés Cependant, la localisation des sources activées à partir d'enregistrements EEG reste une tâche extrêmement difficile en raison de la faible résolution spatiale. Pour ces raisons, nous avons restreint les objectifs de cette thèse à la reconstruction de cartes d’activation des sources corticales de surface. Différentes approches ont été explorées. Les méthodes les plus simples d'imagerie corticales sont basées uniquement sur les caractéristiques géométriques de la tête. La charge de calcul est considérablement réduite et les modèles utilisés sont faciles à mettre en œuvre. Toutefois, ces approches ne fournissent pas d'informations précises sur les générateurs neuronaux et sur leurs propriétés spatiotemporelles. Pour surmonter ces limitations, des techniques plus sophistiquées peuvent être utilisées pour construire un modèle de propagation réaliste, et donc d'atteindre une meilleure reconstruction de sources. Cependant, le problème inverse est sévèrement mal posé, et les contraintes doivent être imposées pour réduire l'espace des solutions. En l'absence de modèle bioanatomique, les méthodes développées sont fondées sur des considérations géométriques de la tête ainsi que la propagation physiologique des sources. Les opérateurs matriciels de rang plein sont appliqués sur les données, de manière similaire à celle effectuée par les méthodes de surface laplacien, et sont basés sur l'hypothèse que les données de surface peuvent être expliquées par un mélange de fonctions de bases radiales linéaires produites par les sources sous-jacentes. Dans la deuxième partie de ces travaux, nous détendons la contrainte-de rang plein en adoptant un modèle de dipôles distribués sur la surface corticale. L'inversion est alors contrainte par une hypothèse de parcimonie, basée sur l'hypothèse physiologique que seuls quelques sources corticales sont simultanément actives ce qui est particulièrement valable dans le contexte des sources d'épilepsie ou dans le cas de tâches cognitives. Pour appliquer cette régularisation, nous considérons simultanément les deux domaines spatiaux et temporels. Nous proposons deux dictionnaires combinés d’atomes spatio-temporels, le premier basé sur une analyse en composantes principales des données, la seconde à l'aide d'une décomposition en ondelettes, plus robuste vis-à-vis du bruit et bien adaptée à la nature non-stationnaire de ces données électrophysiologiques. Toutes les méthodes proposées ont été testées sur des données simulées et comparées aux approches classiques de la littérature. Les performances obtenues sont satisfaisantes et montrent une bonne robustesse vis-à-vis du bruit. Nous avons également validé notre approche sur des données réelles telles que des pointes intercritiques de patients épileptiques expertisées par les neurologues de l'hôpital universitaire de Nancy affiliées au projet. Les localisations estimées sont validées par l'identification de la zone épileptogène obtenue par l'exploration intracérébrale à partir de mesures stéréo EEG. / Cortical Source Imaging plays an important role for understanding the functional and pathological brain mechanisms. It links the activation of certain cortical areas in response to a given cognitive stimulus, and allows one to study the co-activation of the underlying functional networks. Among the available acquisition modality, electroencephalographic measurements (EEG) have the great advantage of providing a time resolution of the order of the millisecond, at the scale of the dynamic of the studied process, while being a non-invasive technique often used in clinical routine. However the identification of the activated sources from EEG recordings remains an extremely difficult task because of the low spatial resolution this modality provides, of the strong filtering effect of the cranial bones and errors inherent to the used propagation model. In this work different approaches for the estimation of cortical activity from surface EEG have been explored. The simplest cortical imaging methods are based only on the geometrical characteristics of the head. The computational load is greatly reduced and the used models are easy to implement. However, such approaches do not provide accurate information about the neural generators and on their spatiotemporal properties. To overcome such limitations, more sophisticated techniques can be used to build a realistic propagation model, and thus to reach better source reconstruction by its inversion. However, such inversion problem is severely ill-posed, and constraints have to be imposed to reduce the solution space. We began by reconsidering the cortical source imaging problem by relying mostly on the observations provided by the EEG measurements, when no anatomical modeling is available. The developed methods are based on simple but universal considerations about the head geometry as well as the physiological propagation of the sources. Full-rank matrix operators are applied on the data, similarly as done by Surface Laplacian methods, and are based on the assumption that the surface can be explained by a mixture of linear radial basis functions produced by the underlying sources. In the second part of the thesis, we relax the full-rank constraint by adopting a distributed dipole model constellating the cortical surface. The inversion is constrained by an hypothesis of sparsity, based on the physiological assumption that only a few cortical sources are active simultaneously Such hypothesis is particularly valid in the context of epileptic sources or in the case of cognitive tasks. To apply this regularization, we consider simultaneously both spatial and temporal domains. We propose two combined dictionaries of spatio-temporal atoms, the first based on a principal components analysis of the data, the second using a wavelet decomposition, more robust to noise and well suited to the non-stationary nature of these electrophysiological data. All of the proposed methods have been tested on simulated data and compared to conventional approaches of the literature. The obtained performances are satisfactory and show good robustness to the addition of noise. We have also validated our approach on real epileptic data provided by neurologists of the University Hospital of Nancy affiliated to the project. The estimated locations are consistent with the epileptogenic zone identification obtained by intracerebral exploration based on Stereo-EEG measurements.

Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2018-08-13T14:49:42Z No. of bitstreams: 2 Dissertação - Lucas Fiorini Cruz - 2018.pdf: 23542908 bytes, checksum: 75e04989d0719a9b5d0d22225c38ce7a (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2018-08-14T11:35:12Z (GMT) No. of bitstreams: 2 Dissertação - Lucas Fiorini Cruz - 2018.pdf: 23542908 bytes, checksum: 75e04989d0719a9b5d0d22225c38ce7a (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-08-14T11:35:12Z (GMT). No. of bitstreams: 2 Dissertação - Lucas Fiorini Cruz - 2018.pdf: 23542908 bytes, checksum: 75e04989d0719a9b5d0d22225c38ce7a (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-05-18 / Electroencephalography (EEG) measurements are widely used in clinical assessments for research due to its noninvasive nature and for providing several informations on the neural activity associated to both neural functions and disorders, including epilepsy syndromes. In cases related with partial epilepsy, surgical interventions are recommended and the accurate location of the seizure becomes a sine qua non prerequisite for those procedures. Brain source position estimation can help in the selection and classification of brain spots. In this sense, for control purposes, this work begins with the development of a mathematical model that is more electromagnetically representative than the usual model, presenting different aggregate characteristics such as refraction and, especially, frequency dependent attenuation of the wave. We also propose a new method that estimates the source positions from spectral peaks produced by the eigenvalues of the sum of the spatial covariance matrix of the EEG signals and a spatial covariance matrix related to a simulated source that is numerically swept throughout every point on different horizontal layers of the brain. The key approach was to select the eigenvalues that were less affected by the noise and use them to produce the search spectrum. In order to assess the accuracy and robustness of the proposed method, we compared its RMSE (Root Mean Square Error) performance at different SNRs (Signal-to-Noise Ratio) to those of MUSIC (Multiple Signal Classification), a method based on orthogonal subspaces, and NSF (Noise Subspace Fitting), a method based on subspace fitting. The results were produced for both the usual and proposed signal model in order to evaluate their accuracy. Subsequently, the signal models were compared after spatial filtering, aiming the determination of the waveform of a particular source. The proposed approach presents the lowest threshold SNR and the highest accuracy under noisy conditions for all analyzed cases and for both models. The new approach for the signal model made the estimation more accurate in all the studied cases, besides providing greater accuracy on spatial filtering, when compared to the usual model. / Sinais de eletroencefalografia (EEG) são amplamente utilizados em análises clínicas e para fins de pesquisas devido ao seu caráter não invasivo e por possuírem informações sobre as atividades neurais ligadas às funções e anomalias cerebrais, incluindo a epilepsia. Nos casos relacionados à epilepsia parcial, intervenções cirúrgicas são recomendadas e a precisa localização da região epileptogênica é uma condição sine qua non para tal procedimento. Nesse sentido, para fins de controle, este trabalho se inicia com a proposta de um novo modelo matemático das atividades elétricas do cérebro que, quando comparado ao modelo Dipolo elétrico - encontrado na literatura - aproxima-se mais do real, apresentando diferentes características agregadas como refração e, especialmente, atenuação dependente da frequência da onda. Em outro plano, também é proposto um novo método que estima as posições das fontes elétricas cerebrais a partir de picos espectrais produzidos por autovalores da soma da matriz de covariância espacial do sinal EEG com uma matriz de covariância espacial relacionada a uma fonte simulada que, numericamente, é posicionada em pontos de diferentes camadas horizontais do cérebro. A chave para essa abordagem é selecionar os autovalores menos afetados pelo ruído e utilizá-los para construir o espectro de busca. Para analisar a robustez e acurácia do método, avalia-se o seu desempenho em relação à raiz do erro médio quadrático (REMQ) para diferentes valores de relação sinal-ruído (SNR), comparando-o ao algoritmo MUSIC (Multiple Signal Classification), baseado em subespaços ortogonais, e ao NSF (Noise Subspace Fitting), método baseado em “distância” entre subespaços (subspace fitting). Todos os resultados são gerados para o modelo de sinal usual e o proposto, com fim de avaliar a acurácia conferida a cada um destes. Posteriormente, os modelos de sinal são comparados quanto à filtragem espacial, buscando a determinação da forma de onda de uma fonte em particular. A abordagem proposta apresenta uma menor SNR de limiar de desempenho e uma maior acurácia sob condições ruidosas em todos os casos analisados e nos dois modelos. O novo modelo do sinal tornou a estimação DOA mais precisa em todos os casos estudados, além de ter conferido maior precisão à filtragem espacial, quando comparado ao modelo usual.