L’EEG de surface permet l'étude spatio-temporelle de l’activité cérébrale avec une résolution temporelle élevée, cependant elle souffre de la forte atténuation du champ électrique propagée par l'os du crâne et de la présence de sources de bruits externes. De ce fait, nous souhaitons exploiter les mesures issues de la Stéréo-EEG (SEEG). Cette modalité consiste en l'introduction d'électrodes d'enregistrement au plus près des générateurs, bénéficiant ainsi d'un rapport signal à bruit bien supérieur à celui observé en EEG. Nous proposons ainsi dans cette thèse une étude de faisabilité de l'imagerie de sources à partir de ces mesures, basée sur une méthode d'inversion de type dipôle équivalent associée à un modèle de propagation à une sphère, capable d'amener à une précision de localisation de l'ordre de quelques millimètres. A partir d'une implantation clinique usuelle de la SEEG, nous évaluons les performances de localisation lorsque différents sous-ensembles de capteurs sont considérés. En présence de bruit réaliste, nous constatons que l'ajout de capteurs lointains peut amener à une dégradation de la précision de localisation. Ces conclusions nous amènent à proposer une approche de sélection des capteurs locaux dans le but d'optimiser la fiabilité des résultats. Les atouts et faiblesses de cette approche sont analysés dans un cadre de simulation réaliste afin d'explorer de façon pertinente les différents paramètres pouvant influer sur la qualité de résolution du problème inverse. Les approches sont appliquées sur des enregistrements SEEG récoltés au CHRU de Nancy afin de confronter les méthodes de localisation proposées à des mesures réelles / The surface EEG makes it possible to study the brain activity with a high temporal resolution, however it suffers from the severe attenuation of the electrical propagation through the skull bone as well as the addition of external artifacts. As an alternative, we would like to exploit the Stereo-EEG (SEEG) recordings, consisting in shaft electrodes implanted in the brain volume in the direct vicinity of the brain generators. These data benefit from a high signal to noise ratio compared to this observed in surface EEG. We propose in this thesis a feasibility study of source imaging from the SEEG, based on an equivalent current dipole inversion method associated with an analytical One-Sphere propagation model, able to bring localization precision of the order of a few millimeters. Using a typical clinical electrode implantation, we evaluate the localization performance when different subsets of sensors are considered. In the presence of realistic noise, we observe that the addition of distant sensors with respect to the source can lead to a degradation of the localization accuracy. These conclusions lead us to propose a local sensor selection approach in order to optimize the reliability of the results. The strengths and weaknesses of this approach are analyzed on a realistic simulation framework, for a relevant exploration of the different parameters impacting on the quality of the SEEG source imaging. The approaches are applied on SEEG recordings collected at the CHRU of Nancy to evaluate their performance when facing real measurements

Passive source localization is the estimation of the positions of the sources or emitters given the sensor data. In this thesis, some of the well known methods for passive source localization are investigated and compared in a stationary emitter sensor framework. These algorithms are discussed in detail in two and three dimensions for both single and multiple target cases. Passive source localization methods can be divided into two groups as two-step algorithms and single-step algorithms. Angle-of-Arrival (AOA) based Maximum Likelihood (ML) and Least Squares (LS) source localization algorithms, Time- Difference-of-Arrival (TDOA) based ML and LS methods, AOA-TDOA based hybrid ML methods are presented as conventional two step techniques. Direct Position Determination (DPD) method is a well known technique within the single step approaches. In thesis, a number of variants of DPD technique with better computational complexity (the proposed methods do not need eigen-decomposition in the grid search) are presented. These are the Direct Localization (DL) with Multiple Signal Classification (MUSIC), DL with Deterministic ML (DML) and DL with Stochastic ML (SML) methods. The evaluation of these algorithms is done by considering the Cramer Rao Lower Bound (CRLB). Some of the CRLB expressions given in two dimensions in the literature are presented for threedimensions. Extensive simulations are done and the effects of different parameters on the performances of the methods are investigated. It is shown that the performance of the single step algorithms is good even at low SNR. DL with MUSIC algorithm performs as good as the DPD while it has significant savings in computational complexity. AOA, TDOA and hybrid algorithms are compared in different scenarios. It is shown that the improvement achieved by single-step techniques may be acceptable when the system cost and complexity are ignored. The localization algorithms are compared for the multiple target case as well. The effect of sensor deployments on the location performance is investigated.

The angular location of an acoustic source can be estimated by measuring an acoustic direction of incidence based solely on the noise produced by the source. Methods for determining the direction of incidence based on sound intensity, the phase of cross-spectral functions, and cross-correlation functions are available. In this current work, we implement Dominant Frequency SElection (DFSE) algorithm. Direction of arrival (DOA) estimation usingmicrophone arrays is to use the phase information present in signals from microphones that are spatially separated. DFSE uses the phase difference between the Fourier transformedsignals to estimate the direction ofarrival (DOA)and is implemented using a three-element ’L’ shaped microphone array, linear microphone array, and planar 16-microphone array. This method is based on simply locating the maximum amplitude from each of the Fourier transformed signals and thereby deriving the source location by solving the set of non-linear least squares equations. For any pair of microphones, the surface on whichthe time difference ofarrival (TDOA) is constant is a hyperboloidoftwo sheets. Acoustic source localization algorithms typically exploit this fact by grouping all microphones into pairs, estimating the TDOA of each pair, then finding the point where all associated hyperboloids most nearly intersect. We make use of both closed-form solutions and iterative techniques to solve for the source location.Acoustic source positioned in 2-dimensional plane and 3-dimensional space have been successfully located.

Steered Response Power based algorithms are widely used for finding sound source location using microphone array systems. SRCP-PHAT is one such algorithm that has a robust performance under noisy and reverberant conditions. The algorithm creates a likelihood function over the field of view. This thesis employs image processing methods on SRCP-PHAT images, to exploit the difference in power levels and pixel patterns to discriminate between sound source and background pixels. Hough Transform based ellipse detection is used to identify the sound source locations by finding the centers of elliptical edge pixel regions typical of source patterns. Monte Carlo simulations of an eight microphone perimeter array with single and multiple sound sources are used to simulate the test environment and area under receiver operating characteristic (ROCA) curve is used to analyze the algorithm performance. Performance was compared to a simpler algorithm involving Canny edge detection and image averaging and an algorithms based simply on the magnitude of local maxima in the SRCP image. Analysis shows that Canny edge detection based method performed better in the presence of coherent noise sources.

Central to human intelligence, visual categorization is a skill that is both remarkably fast and accurate. Although there have been numerous studies in primates regarding how information flows in inferiortemporal (ITC) and prefrontal (PFC) cortices during online discrimination of visual categories, there has been little comparable research on the human cortex. To bridge this gap, this thesis explores how visual categories emerge in prefrontal cortex and the ventral stream, which is the human homologue of ITC. In particular, cortical spatiotemporal plasticity in visual category learning was investigated using behavioral experiments, magnetoencephalographic (MEG) imaging, and statistical machine learning methods. From a theoretical perspective, scientists from work on non-human primates have posited that PFC plays a primary role in the encoding of visual categories. Much of the extant research in the cognitive neuroscience literature, however, emphasizes the role of the ventral stream. Despite their apparent incompatibility, no study has evaluated these theories in the human cortex by examining the roles of the ventral stream and PFC in online discrimination and acquisition of visual categories. To address this question, I conducted two learning experiments using visually-similar categories as stimuli and recorded cortical response using MEG—a neuroimaging technique that offers a millisecond temporal resolution. Across both experiments, categorical information was found to be available during the period of cortical activity. Moreover, late in the learning process, this information is supplied increasingly in the ventral stream but less so in prefrontal cortex. These findings extend previous theories by suggesting that the ventral stream is crucial to long-term encoding of visual categories when categorical perception is proficient, but that PFC jointly encodes visual categories early on during learning. From a methodological perspective, MEG is limited as a technique because it can lead to false discoveries in a large number of spatiotemporal regions of interest (ROIs) and, typically, can only coarsely reconstruct the spatial locations of cortical responses. To address the first problem, I developed an excursion algorithm that identified ROIs contiguous in time and space. I then used a permutation test to measure the global statistical significance of the ROIs. To address the second problem, I developed a method that incorporates domainspecific and experimental knowledge in the modeling process. Utilizing faces as a model category, I used a predefined “face” network to constrain the estimation of cortical activities by applying differential shrinkages to regions within and outside this network. I proposed and implemented a trial-partitioning approach which uses trials in the midst of learning for model estimation. Importantly, this renders localizing trials more precise in both the initial and final phases of learning. In summary, this thesis makes two significant contributions. First, it methodologically improves the way we can characterize the spatiotemporal properties of the human cortex using MEG. Second, it provides a combined theory of visual category learning by incorporating the large time scales that encompass the course of the learning.

Acoustic Sound Source localization using signal processing is required in order to estimate the direction from where a particular acoustic source signal is coming and it is also important in order to find a soluation for hands free communication. Video conferencing, hand free communications are different applications requiring acoustic sound source localization. This applications need a robust algorithm which can reliably localize and position the acoustic sound sources. The Steered Response Power Phase Transform (SRP-PHAT) is an important and roubst algorithm to localilze acoustic sound sources. However, the algorithm has a high computational complexity thus making the algorithm unsuitable for real time applications. This thesis focuses on describe the implementation of the SRP-PHAT algorithm as a function of source type, reverberation levels and ambient noise. The main objective of this thesis is to present different approaches of the SRP-PHAT to verify the algorithm in terms of acoustic enviroment, microphone array configuration, acoustic source position and levels of reverberation and noise.

A Brain-Computer Interface (BCI) is a system to communicate with external world through the brain activity. The brain activity is measured by Electro-Encephalography (EEG) and then processed by a BCI system. EEG source reconstruction could be a way to improve the accuracy of EEG classification in EEGbased brain–computer interface (BCI). In this thesis BCI methods were applied on derived sources which by their EEG enhancement it became possible to obtain a more accurate EEG detection and brought a new application to BCI technology that are recognition of writing letters imagery from brain waves. The BCI system enables people to write and type letters by their brain activity (EEG). To this end, first part of the thesis is dedicated to EEG source reconstruction techniques to select the most optimal EEG channels for task classification purposes. Due to this reason the changes in EEG signal power from rest state to motor imagery task was used, to find the location of an active single equivalent dipole. Implementing an inverse problem solution on the power changes by Multiple Sparse Priors (MSP) method generated a scalp map where its fitting showed the localization of EEG electrodes. Having the optimized locations the secondary objective was to choose the most optimal EEG features and rhythm for an efficient classification. This became possible by feature ranking, 1- Nearest Neighbor leave-one-out. The feature vectors were computed by applying the combined methods of multitaper method, Pwelch. The features were classified by several methods of Normal densities based quadratic classifier (qdc), k-nearest neighbor classifier (knn), Mixture of Gaussians classification and Train neural network classifier using back-propagation. Results show that the selected features and classifiers are able to recognize the imagination of writing alphabet with the high accuracy. / BCI controls external devices and interacts with the environment by brain signals. Measured EEG signals over the motor cortex exhibit changes in power related to the movements or imaginations which are executed in motor tasks [1]. These changes declare increase or decrease of power in the alpha (8Hz-13Hz), and beta (13Hz-28Hz) frequency bands from resting state to motor imagery task that known as event related synchronization (in case of power increasing) and desynchronization (in case of power decreasing) [2]. The necessity to communicate with the external world for locked-in state (LIS) patients (a paralyzed patient who only communicates with eyes), made doctors and engineers motivated to develop a BCI technology for typing letters through brain commands. Many researches have been done around this area to ascertain the dream of typing for handicapped. In the brain some regions of the cerebral cortex (motor cortex) are involved in the planning, control, and execution of voluntary movements. Electroencephalography (EEG) signals are electrical potential generated by the nerve cells in the cerebral cortex. In order to execute motoric tasks, the EEG signals are appeared over the motor cortex [1]. The measured brain response to a stimulus is called eventrelated potential (ERP). P300-event related potential (ERP) is an evoked neuron response to an external auditory or visual stimulus that is detectable in scalp-recorded EEG (The P300 is evoked potential which occurs across the parieto-central on the skull 300 ms after applying the stimulus). Farwell and Donchin have proven in a P300-based BCI speller [3] that P300 response is a reliable signal for controlling a BCI system. They described the P300 speller, in which alphanumeric characters are represented in a matrix grid of six-by-six matrix. The user should focus on one of the 36 character cells while each row and column of the grid is intensified randomly and sequentially. The P300, observed in EEG signals, is created by the intersection of the target row and column which causes detection of the target stimuli with a probability of 1/6 (in case of high accuracy of flashing operation). Also when the target stimulus is rarely presented in the random sequence of stimuli causes a neural reaction to unpredictable but recognizable event and a P300 response is evoked [3]. Generally when the subject is involved with the task to recognize the targets, the P300 wave happens and the signal amplitude varies with the unlikelihood of the targets. Its dormancy changes with the difficulty of recognizing the target stimulus from the standard stimuli [3].The attended character of the matrix can be extracted by proper feature extraction and classification of P300. A plenty of procedures for feature extraction and classification have been applied to improve the performance of originally reported speller [3], such as stepwise linear discriminate analysis (SWLDA) [4, 5], wavelets [1], support vector machines [6, 7, 8] and matched filtering [9]. Till now, BCI-related P300 research has mostly considered on signals from standard P300 scalp locations. While in [10, 11, 12, 13, 14, 15, 16] it has been proven that the use of additional locations, especially posterior sites, may improve classification accuracy, but it has not been addressed to particular offline and online studies. Recently, auditory version improvement of the visual P300 speller allows locked in patients who have problem in the visual system to use the P300 speller system by relating two numbers to each letter which indicate the row and column of letter position [17]. Now a new technology is needed which can substitute a keyboard with no alphabet menu. The technology will be handy for blind people and useful for healthy persons who need to work hands free with their computer or mobile. The aim of this thesis is to improve EEG detection through source localization for a new BCI application to type with EEG signals without using alphabet menu. / +98-9359576229

L'EMG utérin appelé Electrohystérogramme (EHG) a été exploité depuis longtemps par ses caractéristiques temporelles, fréquentielles, et temps-fréquence, pour la prédiction de l'accouchement prématuré, tandis que l'étude de sa propagation est rare. Tous les résultats des études antérieures n'ont pas montré un potentiel satisfaisant pour une application clinique. L'objectif de cette thèse est l'analyse de la propagation ainsi que de la non-linéarité des signaux EHG pendant la grossesse et le travail en vue d'une application clinique. Une analyse monovariée a été faite pour étudier la non-linéarité et la sensibilité des méthodes aux différentes caractéristiques des signaux. Une analyse bivariée a ensuite été menée pour l‟étude de la propagation de l‟EHG, en mesurant le couplage entre les voies ainsi que la direction de couplage, ce qui est une nouveauté de notre thèse. Dans cette analyse, nous proposons une approche de filtrage-fenêtrage pour améliorer les méthodes d'estimation du couplage et de sa direction. Une autre nouveauté de cette thèse est l'implantation d'un outil de localisation de source d'EHG pour étudier la dynamique de l'utérus au niveau de la source, et non pas au niveau des électrodes comme fait dans les études précédentes. Les résultats montrent que les méthodes non linéaires sont plus capables que les méthodes linéaires, de classifier les contractions de grossesse et de travail. La méthode de réversibilité de temps est la moins sensible à la fréquence d'échantillonnage et au contenu fréquentiel du signal. Les résultats indiquent également une augmentation de couplage et une concentration des directions vers le col de l‟utérus, en allant de la grossesse vers le travail. En respectant la non-stationnarité des signaux EHG et en se libérant de l'effet de filtrage de la graisse, très variable durant la grossesse et entre les différentes femmes, notre méthode de filtrage-fenêtrage (segmentation et filtrage du signal EHG pour ne garder que la composant FWL), améliore les performances des méthodes de connectivité. L'intensité des sources localisées et leur nombre sont plus élevés durant le travail que durant la grossesse. Les sources localisées sont actives et propagées durant le travail alors que durant la grossesse elles restent faibles et localisées. Une amélioration de la matrice d'électrodes du protocole expérimental de rat a été effectuée par le développement d'une électrode à succion. Ce protocole pourra ensuite être utilisé pour la validation de nos méthodes et celle du modèle électrophysiologique. / The uterine EMG -called Electrohysterogramme (EHG)- temporal, frequency, and time-frequency characteristics have been used for a long time for the prediction of preterm labor. However, the investigation of its propagation is rare. All the results of the previous studies did not show a satisfactory potential for clinical application. The objective of this thesis is the analysis of the propagation as well as of the nonlinear characteristics of EHG signals during pregnancy and labor for clinical application. A monovariate analysis was done to investigate the nonlinearity and the sensibility of methods to different characteristics of the signals. A bivariate analysis was then done for the investigation of the propagation of EHG by measuring the coupling between channels, as well as the direction of coupling, which is an innovative part of our thesis. In this analysis we propose a new approach to improve the coupling and direction estimation methods. Another innovation of this thesis is the implementation of a tool for EHG source localization to investigate the dynamic of the uterus at the source level, not at electrodes level as previously done. Results show that nonlinear methods are more able to classify pregnancy and labor contractions than linear ones, and that time reversibility method is the least sensitive to sampling frequency and frequency content of the signal. Results also indicate an increase in coupling and a concentration of coupling direction toward the cervix when going from pregnancy to labor. We also proposed to respect the nonstationarity of EHG signal and to recover the effect of variable fat filtering along pregnancy, by segmenting and filtering the EHG in its FWL component. This filtering-windowing approach permits to improve the performances of connectivity methods. Finally, the intensity of localized sources and their number is higher in labor than in pregnancy contractions. The identified sources are more active and more propagated in labor whereas in pregnancy they remain weak and local. An improvement in the electrode matrix of the rat experimental protocol has also been done by developing a suction electrode. This protocol can then be used for the validation of our methods and of the electrophysiological model.

No description available.

No description available.