Thèses sur le sujet « Event Related Optical Signal »

The P300 waveform of the auditory event-related potential (ERP) was evaluated in studies which examined measurement methodology and which analysed the separate sub-components of the waveform in a group of patients with schizophrenia and manic depressive illness. Two simulation studies were carried out. One was designed to evaluate a new objective method for measuring P300 latency, the other applied the technique of latency corrected averaging to P300 measurement. A model for the P300 is described which allows the time domain behaviour of the waveform to be predicted when it is subjected to the different high-pass filters used in clinical studies. The first clinical study was a detailed investigation on the measurement methodology in clinical studies of the auditory P300. The second clinical study used novel auditory stimuli as a means of separating the sub-components of the P300 complex. Three clinical tests were used based on the standard auditory "oddball", a standard auditory oddball with additional "distracting" novel stimuli, and a passive paradigm using novel stimuli. The use of Slow Wave as an index of task difficulty was examined. Twenty-eight controls, 29 schizophrenics and 28 subjects with bipolar depression were studied. The results from these experiments showed that abnormalities of P300 sub-components in the schizophrenic group are enhanced when using distracting stimuli and that these differences are present even in passive orienting. The bipolar group also showed abnormal P300 sub-components in responses to the distraction task. These results could imply a more widespread disruption in underlying brain structures in schizophrenia than bipolar depression. Used in conjunction with brain perfusion images derived from single photon emission tomography (SPET), the paradigms described have shown great potential for locating the underlying brain structures involved in the genesis of P300, and how they are affected in abnormal pathological states.

Neurological degenerative diseases like stroke, Alzheimer, Amyothrophic Lateral Sclerosis (ALS), Parkinson and many others are constantly increasing their incidence in the world health statistics as far as the mean age of the global population is getting higher and higher. This leads to a general need for effective, at-home and low-cost rehabilitative and health-daily-care tools. The latter should consist either of technological devices implemented for operating in a remote way, i.e. tele-medicine is quickly spreading around the world, or very-advanced computer-based and robotic systems to realize intense and repetitive trainings. This is the challenge in which Information and Communications Technology (ICT) is asked to play a major role in order to bring medicine to reach further advancements. Indeed, no way to cope with these issues is possible outside a strong and vivid cooperation among multi-disciplinary teams of clinicians, physicians, biologists, neuropsychologists and engineers and without a resolute pushing towards a widespread interoperability between Institutes, Hospitals and Universities all over the world, as recently highlighted during the main International conferences on ICT in healthcare. The establishment of well-defined standards for gathering and sharing data will then represent a key element to enhance the efficacy of the aforementioned collaborations. Among the others, stroke is one of the most common neurological pathologies being the second or third cause of mortality in the world; moreover, it causes more than sixty percent survivors remain with severe cognitive and motor impairments that impede them in living normal lives and require a twenty-four-hours daily care. As a consequent, on one side stroke survivors experience a frustrating condition of being completely dependent on other people even to perform simple daily actions like reach and grasp an object,hold a glass of water to drink it and so on. States, by their side, have to take into account additional costs to provide stroke patients and their families with appropriate cares and supports to cope with their needs. For this reason, more and more fundings are recently made available by means of grants, European and International projects, programs to exchange different expertise among various countries with the aim to study how to accelerate and make more effective the recovery process of chronic stroke patients. The global research about this topic is conducted on several parallel aspects: as regard as the basic knowledge of brain processes, neurophysiologists, biologists and engineers are particularly interested in an in-depth understanding of the so-called neuroplastic changes that brain daily operates in order to adapt individuals to life changes, experiences and to realize more extensively their own potentialities. Neuroplasticity is indeed the corner stone for most of the trainings nowadays adopted by the standard as well as the more innovative methods in the rehabilitative programs for post-stroke recovery. Specifically speaking, motor rehabilitation usually includes longterm, repetitive and intense goal-directed exercises that promote neuroplastic mechanisms such as neural sprouting, synapto-genesis and dendritic branching. These processes are strictly related with motor improvements and their study could - one day - serve as prognostic measures of the recovery. Another aspect of this field of neuroscience research is the number of applications that it makes feasible. One of the most exciting is to connect an injured brain to a computer or a robotic device in a Brain-Computer or Brain-Machine Interface (BCI or BMI) scheme aiming at bypassing the impairments of the patient and make him/her autonomously move again or train his/her motor abilities in a more effective way. This kind of research can already count an amount of literature that provides several proofs of concept that these heterogeneous systems constituted by humans and robots can work at the purpose. A particular application of BCI for restoring or enhancing, at least, the reaching abilities of chronic stroke survivors was implemented and is still currently being improved at I.R.C.C.S. San Camillo Hospital Foundation, an Institute for the rehabilitation from neurological diseases located in Lido of Venice and partially technically supported by the Department of Information Engineering of Padua in range of an agreement signed in 2009. This specific BCI platform allows patients to train and improve their reaching movements by means of a robotic arm that provides a force that helps patients in completing the training exercise, i.e. to hit a predetermined target. This force feedback is however subject to a strict condition: during the movement, the person has to produce the expected pattern of cerebral activity. Whenever this is accomplished, a force is delivered proportionally to the entity of the latter activity, otherwise the patient is obliged to operate without any help. In this way, this platform implements the so-called operant-learning, that is one of the most effective conditioning techniques to make a subject learn or relearn a task. If, on one hand, the primary and explicit task is to improve a movement, on the other side the secondary but most important task is to deploy the perilesional part of the brain - still healthy - in becoming responsible for the control of the movement. It is a popular and widely-accepted opinion within the neuroscience community, indeed, that a healthy region of the sensorimotor area nearby the damaged one - which was previously in charge of performing the (reaching) movement - can optimally accomplish the impaired motor function substituting the original control area. Technically speaking, the main crucial feature that can ensure the effectiveness of the whole system is the precise and in real-time identification and quantification of the cerebral pattern associated with the movement, the worldwide named movement-related desynchronization (MRD). Starting from its original definition, passing through the most used techniques for its recognition, the thesis work presents a series of criticisms of the current signal processing method to detect the MRD and a complete analysis of the possible features that can better represent the movement condition and that can be more easily extracted during the on-line operations. Brain - it is well-known - learns by trials and errors and it needs a slightly-delayed (in the range of fraction of seconds) feedback of its performance to learn a task in the best way. This BCI application was born with the purpose to provide the above-mentioned feedback: however, this is only feasible if a computationally easy and contingent signal processing technique is available. This thesis work would like to cope with the lack of a well-planned real-time signal analysis in the current experimental protocol.

This dissertation focuses on two specific problems related to the design of parametric signal classifiers: dimensionality reduction to overcome the curse of dimensionality and information fusion to improve classification by exploiting complementary information from multiple sensors or multiple classifiers. Dimensionality reduction is achieved by introducing a strategy to rank and select a subset of principal component transform (PCT) coefficients that carry the most useful discriminatory information. The criteria considered for ranking transform coefficients include magnitude, variance, inter-class separation, and classification accuracies of individual transform coefficients. The ranking strategy not only facilitates overcoming the dimensionality curse for multivariate classifier implementation but also provides a means to further select, out of a rank-ordered set, a smaller set of features that give the best classification accuracies. Because the class-conditional densities of transform feature vectors are often assumed to be multivariate Gaussian, the dimensionality reduction strategy focuses on overcoming the specific problems encountered in the design of practical multivariate Gaussian classifiers using transform feature vectors. Through experiments with event related potentials (ERPs) and ear pressure signals, it is shown that the dimension of the feature space can be decreased quite significantly by means of the feature ranking and selection strategy. Furthermore, the resulting Gaussian classifiers yield higher classification accuracies than those reported in previous classification studies on the same signal sets. Amongst the four feature selection criteria, Gaussian classifiers using the maximum magnitude and maximum variance selection criteria gave the best classification accuracies across the two sets of classification experiments. For the multisensor case, dimensionality reduction is achieved by introducing a spatio-temporal array model to observe the signals across channels and time, simultaneously. A two-step process which uses the Kolmogrov-Smirnov test and the Lilliefors test is formulated to select the array elements which have different Gaussian densities across all signal categories. Selecting spatio-temporal elements that fit the assumed model and also statistically differ across the signal categories not only decreases the dimensionality significantly but also ensures high classification accuracies. The selection is dynamic in the sense that selecting spatio-temporal array elements corresponds to selecting samples of different sensors at different time-instants. Each selected array element is classified using a univariate Gaussian classifier and the resulting decisions are fused into a decision fusion vector which is classified using a discrete Bayes classifier. The application of the resulting dynamic channel selection-based classification strategy is demonstrated by designing and testing classifiers for multi-channel ERPs and it is shown that strategy yields high classification accuracies. Most noteworthy of the two dimensionality reduction strategies is the fact that the multivariate Gaussian signal classifiers developed can be implemented without having to collect a prohibitively large number of training signals simply to satisfy the dimensionality conditions. Consequently, the classification strategies can be beneficial for designing personalized human-machine-interface (HMI) signal classifiers for individuals from whom only a limited number of training signals can reliably be collected due to severe disabilities. The information fusion strategy introduced is aimed at improving the performance of signal classifiers by combining signals from multiple sensors or by combining decisions of multiple classifiers. Fusion classifiers with diverse components (classifiers or data sets) outperform those with less diverse components. Determining component diversity, therefore, is of the utmost importance in the design of fusion classifiers which are often employed in clinical diagnostic and numerous other pattern recognition problems. A new pairwise diversity-based ranking strategy is introduced to select a subset of ensemble components, which when combined, will be more diverse than any other component subset of the same size. The strategy is unified in the sense that the components can be either polychotomous classifiers or polychotomous data sets. Classifier fusion and data fusion systems are formulated based on the diversity selection strategy and the application of the two fusion strategies are demonstrated through the classification of multi-channel ERPs. From the results it is concluded that data fusion outperforms classifier fusion. It is also shown that the diversity-based data fusion system outperforms the system using randomly selected data components. Furthermore, it is demonstrated that the combination of data components that yield the best performance, in a relative sense, can be determined through the diversity selection strategy.

The research described here provides the technology and theory to quantify surface quality for a variety of wood and wood-based products. This technology provides a means of monitoring trends in surface quality which can be used to discriminate between Agood@ products and Abad@ products (the methods described in this research are not intended to provide ?grading? of individual workpieces) as well as provide information to the machine operator as to the source of poor quality machined surfaces. The analysis can be done either on-line at industrial speeds or off-line as a periodic quality control tool. Although the surface quality can be quantifiably measured, the determination of the best feature from the surface profile (root mean square, peak amplitude, average wavelength, frequency content, Joint Time and Frequency Analysis (JTFA) and Wavelet Analysis results, etc.) for the quantification of surface Adefects@ is highly dependent on the application. This research consisted of three broad areas: (1) determination of an optimal hardware configuration for both laboratory and industrial surface scans of wood products, (2) determination of the optimal set of surface descriptors as well as the development of advanced signal processing techniques such as the wavelet transform to accurately describe the quality of a surface as well as provide information to the machine operator on the cause of the loss of surface quality, and (3) development of a software interface to distill the advanced signal processing techniques into a readily obtainable and readable format for the machine operator as well as provide assistance for process decisions.

Event related potentials (ERPs) are obtained from noninvasive electroencephalograms (EEG) which measure neuronal activity of brain on the scalp. However, conventional ERPs derived by averaging of single EEG trials have strong latency variability and are smeared, resulting in blurred scalp topography, especially in late components of ERP. The smearing problem had been addressed by reconstructing ERPs after latency correction with a new EEG analysis method Residue Iteration Decompo¬sition (RIDE), which was demonstrated in a face priming paradigm to improve distinctness in scalp topography (Ouyang et al., 2011). This thesis aims to (1) extend the benefits of RIDE to neural source space by localizing the neural generators of ERPs, thereby developing an integrated RIDE framework for improvement in source localization and causal modeling of effective source networks, and (2) apply the framework to the face priming paradigm for famous faces, to explore the dynamics of face processing and priming. We localized sources through brain electrical source analysis for both conventional ERP and RIDE derived ERPs (RERPs). RERPs allowed localization of an additional motor execution source (Premotor Cortex, PMC), apart from 5 other common sources, of which 2 (Occipital Lobe, OL; Fusiform Gyrus, FG) were obtained from early activity (< 250 ms) and 3 (Mediotemporal lobe, MTL; Prefrontal Cortex, PFC; Anterior Temporal Lobe, ATL) from late activities (> 250 ms) of RERPs respectively. Priming effects, i.e., the difference between primed famous (PF) and unprimed famous (UF) face conditions in source waveforms (SWFs), were extended and enhanced in RERPs, especially for late sources. The priming effects revealed (1) the role of sources in each hemisphere that play in perception, memory and execution, (2) parallel processing of information in sources, (3) early processing in the right hemisphere, and (4) predominance of the right hemisphere in face recognition. Results confirmed SWFs of RERPs as better choice for the dynamic causal model (DCM). Two candidate DCM models, forward (F) and forward-backward (FB) were outlined on each hemisphere with SWFs from PF and UF conditions of RERP data. Priming has tendency to facilitate the FB model in the left hemisphere. On the other hand, independent of model preference, priming strengthened a bidirectional connection between FG and PFC in both hemispheres; this indicates a strong role of FG in structural representation and of PFCs in controlling decisions about face familiarity. Priming modulates the pathway FGMTLPFC differently in the two hemispheres, strengthening the involvement of MTL in the left hemisphere and weakening in the right hemisphere. This indicates proficiency of the left and right MTL in processing different aspects of facial information. Further, a backward connection ATLPFC in the left hemisphere was found to be functionally relevant for both conditions in speeding up response time in individual subjects, reinforcing the role of PFC in executive functioning and ATL in naming of famous faces. Thus, an integrated framework of source localization and DCM with RERPs allows a novel, comprehensive understanding of time resolved dynamics in face recognition and priming, thereby piloting prospects of its application to other experimental paradigms.

L’imagerie de l’activité neuronale (AN) permet d’étudier le fonctionnement normal et pathologique du cerveau humain, en plus d’aider au diagnostic et à la planification d’interventions neurochirurgicales. L’électroencéphalographie (EEG) et l’imagerie par résonance magnétique fonctionnelle (IRMf) comptent parmi les modalités d’imagerie fonctionnelle les plus utilisées en recherche et en clinique. Plusieurs éléments de la structure cérébrale peuvent toutefois influencer les signaux mesurés, de sorte qu’ils ne reflètent pas uniquement l’AN. Il importe donc d’en tenir compte pour bien interpréter les résultats, surtout lorsqu’on compare des sujets à l’anatomie cérébrale très différente. En outre, la maturation, le vieillissement et certaines pathologies s’accompagnent de changements structurels du cerveau. Ceci complique l’analyse de données longitudinales et la comparaison d’un groupe cible avec un groupe contrôle. Or, notre compréhension des interactions structure-signal demeure incomplète et très peu d’études en tiennent compte. Mon projet de doctorat a consisté à étudier les impacts de la structure cérébrale sur les signaux d’EEG et d’IRMf ainsi qu’à explorer des pistes de solution pour s’en affranchir. J’ai d’abord étudié l’effet de l’amincissement cortical dû au vieillissement sur la désynchronisation liée à l’événement (« event-related desynchronization » - ERD) en EEG. Les résultats ont mis en lumière une relation linéaire négative entre l’ERD et l’épaisseur corticale, ce qui a permis de corriger les signaux par régression. J’ai ensuite étudié l’impact de la présence de veines sur la réponse BOLD (blood-oxygen-level dependent) mesurée en IRMf suite à une stimulation visuelle. Ces travaux ont démontré que la densité veineuse locale, qui varie fortement d’une région et d’un sujet à l’autre, corrèle positivement avec l’amplitude et le délai de la réponse BOLD. Finalement, j’ai adapté une technique de classification de données visant à améliorer la détection des régions du cortex activées en IRMf. Cette méthode permet d’éviter plusieurs problèmes de l’analyse classique en IRMf, de réduire l’impact de la structure cérébrale sur les résultats obtenus et d’établir des cartes d’activité cérébrale contenant plus d’information. Globalement, ces travaux contribuent à l’amélioration de notre compréhension des interactions structure-signal en EEG et en IRMf, ainsi qu’au développement de méthodes d’analyse réduisant leur impact sur l’interprétation des données en termes d’AN.
Abstract : Imaging neural activity allows studying normal and pathological function of the human brain, while also being a useful tool for diagnosis and neurosurgery planning. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are some of the most commonly used functional imaging modalities, both in research and clinic. Many aspects of cerebral structure can however influence the measured signals, so that they do not only reflect neural activity. Taking them into account is therefore of import to correctly interpret results, especially when comparing subjects displaying large differences in brain anatomy. In addition, maturation, aging as well as some pathologies are associated with changes in brain structure. This acts as a confounding factor when analysing longitudinal data or comparing target and control groups. Yet, our understanding of structure-signal relationships remains incomplete and very few studies take them into account. My Ph.D. project consisted in studying the impacts of cerebral structure on EEG and fMRI signals as well as exploring potential solutions to mitigate them. In that regard, I first studied the effect of age-related cortical thinning on event-related desynchronization (ERD) in EEG. Results allowed identifying a negative linear relationship between ERD and cortical thickness, enabling signal correction using regression. I then investigated how the presence of veins in a region impacts the blood-oxygen-level dependent (BOLD) response measured in fMRI following visual stimulation. This work showed that local venous density, which strongly varies across regions and subjects, correlates positively with the BOLD response amplitude and delay. Finally, I adapted a data clustering technique to improve the detection of activated cortical regions in fMRI. This method allows eschewing many problematic assumptions used in classical fMRI analyses, reducing the impacts of cerebral structure on results and establishing richer brain activity maps. Globally, this work contributes to further our understanding of structure-signal interactions in EEG and fMRI as well as to develop analysis methods that reduce their impact on data interpretation in terms of neural activity.

Cette thèse explore l'analyse de l'activité électrique du cerveau. Un défi important de ces signaux est leur grande variabilité à travers différents essais et/ou différents sujets. Nous proposons une nouvelle méthode appelée "adaptive waveform learning" (AWL). Cette méthode est suffisamment générale pour permettre la prise en compte de la variabilité empiriquement rencontrée dans les signaux neuroélectriques, mais peut être spécialisée afin de prévenir l'overfitting du bruit. La première partie de ce travail donne une introduction sur l'électrophysiologie du cerveau, présente les modalités d'enregistrement fréquemment utilisées et décrit l'état de l'art du traitement de signal neuroélectrique. La principale contribution de cette thèse consiste en 3 chapitres introduisant et évaluant la méthode AWL. Nous proposons d'abord un modèle de décomposition de signal général qui inclut explicitement différentes formes de variabilité entre les composantes de signal. Ce modèle est ensuite spécialisé pour deux applications concrètes: le traitement d'une série d'essais expérimentaux segmentés et l'apprentissage de structures répétées dans un seul signal. Deux algorithmes sont développés pour résoudre ces problèmes de décomposition. Leur implémentation efficace basée sur des techniques de minimisation alternée et de codage parcimonieux permet le traitement de grands jeux de données.Les algorithmes proposés sont évalués sur des données synthétiques et réelles contenant des pointes épileptiformes. Leurs performances sont comparées à celles de la PCA, l'ICA, et du template-matching pour la détection de pointe
This thesis investigates the analysis of brain electrical activity. An important challenge is the presence of large variability in neuroelectrical recordings, both across different subjects and within a single subject, for example, across experimental trials. We propose a new method called adaptive waveform learning (AWL). It is general enough to include all types of relevant variability empirically found in neuroelectric recordings, but can be specialized for different concrete settings to prevent from overfitting irrelevant structures in the data. The first part of this work gives an introduction into the electrophysiology of the brain, presents frequently used recording modalities, and describes state-of-the-art methods for neuroelectrical signal processing. The main contribution of this thesis consists in three chapters introducing and evaluating the AWL method. We first provide a general signal decomposition model that explicitly includes different forms of variability across signal components. This model is then specialized for two concrete applications: processing a set of segmented experimental trials and learning repeating structures across a single recorded signal. Two algorithms are developed to solve these models. Their efficient implementation based on alternate minimization and sparse coding techniques allows the processing of large datasets. The proposed algorithms are evaluated on both synthetic data and real data containing epileptiform spikes. Their performances are compared to those of PCA, ICA, and template matching for spike detection

This study focuses on realization of a Brain Computer Interface (BCI)for the paralyzed to control assistive environmental devices. For this purpose, different motor imagery tasks are classified using different signal processing methods. Specifically, band-pass filtering, Laplacian filtering, and common average reference (CAR) filtering areused to enhance the EEG signal. For feature extraction
Common Spatial Pattern (CSP), Power Spectral Density (PSD), and Principal Component Analysis (PCA) are tested. Linear Feature Normalization (LFN), Gaussian Feature Normalization (GFN), and Unit-norm Feature Vector Normalization (UFVN) are studied in Support Vector Machine (SVM) and Artificial Neural Network (ANN) classification. In order to evaluate and compare the performance of the methodologies, classification accuracy, Cohen&rsquo
s kappa coefficient, and Nykopp&rsquo
s information transfer are utilized. The first experiments on classifying motor imagery tasks are realized on the 3-class dataset (V) provided for BCI Competition III. Also, a 4-class problem is studied using the dataset (IIa) provided for BCI Competition IV. Then, 5 different tasks are studied in the METU Brain Research Laboratory to find the optimum number and type of tasks to control a motor imagery based BCI. Thereafter, an interface is designed for the paralyzed to control assistive environmental devices. Finally, a test application is implemented and online performance of the design is evaluated.

Ce travail de thèse a été centré sur l’analyse de l’impact d’un système d’alerte anticollision sur le traitement de l’information et le comportement du conducteur en conduite automobile simulée. Les objectifs de ce travail étaient 1) de déterminer l’impact d’un signal avertisseur associé à un système d’alerte anticollision sur le traitement de l’information à partir de potentiels évoqués ; 2) d’analyser l’efficacité d’un tel système en fonction de sa fiabilité ; 3) et en fonction de l’état attentionnel des conducteurs ; et 4) d’examiner l’adaptation comportementale au système au cours du temps. Grâce à une double approche comportementale et électrophysiologique, nous avons montré que le signal avertisseur agit au niveau de l’anticipation et de la préparation à la réponse ainsi qu’au niveau cognitif du traitement de l’information. Par ailleurs, nous avons confirmé que les systèmes anticollision ne nécessitent pas d’être complètement fiables pour être efficaces. Nous avons également observé que l’efficacité du signal avertisseur associé au système anticollision était moindre chez les sujets distraits, notamment lorsque la charge cognitive associée à la distraction était élevée. Ceci suggère que le signal avertisseur nécessite des ressources attentionnelles pour être traité et, donc, pour être efficace. Concernant l’adaptation comportementale au système, les principaux résultats ont montré, d’une part, que l’introduction immédiate du système a eu un effet positif sur le comportement de conduite et, d’autre part, que le processus d’adaptation au système à plus long terme peut être affecté si les conducteurs sont distraits par des tâches secondaires coûteuses en ressources attentionnelles
This thesis was focused on the analysis of the impact of a collision warning system on information processing and driver behaviour in simulated driving. The objectives of this work were 1) to determine the impact of a warning signal associated with a collision warning system on the processing of information using evoked potentials, 2) to analyse the effectiveness of such a system according to its reliability, 3) and according to the drivers’ attentional state, 4) to examine the behavioural adaptation to the system over time. Using an electrophysiological and behavioural dual approach, we showed that the warning signal acts at the level of anticipation and response preparation and at the level of cognitive processing. Moreover, we confirmed that collision warning systems do not need to be completely reliable to be effective. We also observed that the effectiveness of the warning signal associated with the collision warning system was lower in distracted subjects, especially when the cognitive load associated with the distraction was high. This suggests that the warning signal requires attentionnal resources in order to be processed and, therefore, to be effective. Concerning behavioural adaptation to the system, the main results showed firstly that the immediate introduction of the system had a positive effect on the driving behaviour and secondly that the process of adaptation of the system at the longer term may be affected if drivers are distracted by high demanding secondary tasks

Une interface cerveau-ordinateur permet d'interagir avec un système, comme un système d'écriture, uniquement par l'activité cérébrale. Un des phénomènes neurophysiologiques permettant cette interaction est le potentiel évoqué cognitif P300, lequel correspond à une modification du signal 300 ms après la présentation d'une information attendue. Cette petite réaction cérébrale est difficile à observer par électroencéphalographie car le signal est bruité. Dans cette thèse, de nouvelles techniques basées sur la théorie des ondelettes sont développées pour améliorer la détection des P300 en utilisant des mesures de similarité entre les canaux électroencéphalographiques. Une technique présentée dans cette thèse débruite les signaux en considérant simultanément la phase des signaux. Nous avons également étendu cette approche pour étudier la localisation du P300 dans le but de sélectionner automatiquement la fenêtre temporelle à étudier et faciliter la détection
Brain-Computer Interfaces (BCI) are control and communication systems which were initially developed for people with disabilities. The idea behind BCI is to translate the brain activity into commands for a computer application or other devices, such as a spelling system. The most popular technique to record brain signals is the electroencephalography (EEG), from which Event-Related Potentials (ERPs) can be detected and used in BCI systems. Despite the BCI popularity, it is generally difficult to work with brain signals, because the recordings contains also noise and artifacts, and because the brain components amplitudes are very small compared to the whole ongoing EEG activity. This thesis presents new techniques based on wavelet theory to improve BCI systems using signals' similarity. The first one denoises the signals in the wavelet domain simultaneously. The second one combines the information provided by the signals to localize the ERP in time by removing useless information

Eine wichtige Folge des Status epilepticus ist die Entwicklung einer chronischen Epilepsie. Die genauen Mechanismen und die Kinetik der Epileptogenese sind weitestgehend unklar. Ziel der vorliegenden Arbeit war ein besseres Verständnis des Prozesses durch die In-vitro-Analyse von Lokalisation und Kinetik funktioneller Folgen des Status epilepticus in vivo. In kombinierten Hippokampus-entorhinaler Kortex Hirnschnittpräparaten von Wistar-Ratten nach elektrisch induziertem selbsterhaltendem Status epilepticus (self-sustaining status epilepticus, SSSE) wurden im Niedrig-Magnesium-Modell anfallsartige Ereignisse (AE) ausgelöst und untersucht. Die In-vitro-Analyse der AE wurde eine, vier und acht Wochen nach SSSE durchgeführt. Um das räumliche Verhalten der epileptischen Aktivität beurteilen zu können, wurde die Messung des extrazellulären Feldpotenzials mit der Analyse intrinsischer optischer Signale kombiniert. Im Verlauf nach SSSE kam es zu einer Latenzverkürzung bis zum Auftreten epileptischer Aktivität und zu einer Zunahme der AE-Frequenz. Vier und acht Wochen nach SSSE stieg der Anteil der AE mit großflächigem Ursprung signifikant an. Im Verlauf nach SSSE wurden außerdem zunehmend diskontinuierliche Ausbreitungsmuster der Anfallsaktivität beobachtet. Acht Wochen nach SSSE zeigten 50% der Präparate zudem eine zeitlich und räumlich von den AE unabhängige, hochfrequente Aktivität im Gyrus dentatus. Zusammenfassend wurden eine Latenzverkürzung und eine Zunahme der AE-Frequenz als Hinweise für eine gesteigerte Exzitabilität des Hirngewebes nach SSSE gesehen. Neben dem großflächigen Ursprung deutet auch die Zunahme diskontinuierlicher Ausbreitungsmuster auf eine gesteigerte Synchronizität des neuronalen Netzwerkes nach SSSE hin. Die autonome Aktivität im Gyrus dentatus spricht dafür, dass die in vorangegangenen Studien beschriebenen strukturellen Änderungen in dieser Region mit einer veränderten Funktionalität einhergehen.
The development of chronic epilepsy is a serious consequence of Status epilepticus. Little is known about the mechanisms and kinetic of the epileptogenic process. The aim of this md-thesis was the analysis of localisation and kinetic of functional deficits in vitro after Status epilepticus in vivo. Using the Low-Magnesium-Model, seizure-like events (SLE) were induced in combined hippocampal-entorhinal cortex slices of wistar rats after electrically induced self-sustaining Status epilepticus (SSSE). One, four and eight weeks after SSSE the in-vitro-analysis of SLE was performed. In order to determine onset and spread-pattern of epileptic activity, the measurement of the extracellular field-potential was combined with the imaging of intrinsic optical signals (IOS). In the time course after SSSE there was a reduction of the latency to onset of seizure activity and an increase of the SLE-frequency. Four and eight weeks after SSSE a significant increase of SLE with regional onset was found. In Addition, there was an increase of non-contiguous propagation of seizure activity. Eight weeks after SSSE 50% of the brain-slices showed autonomous high-frequent activity in the dentate gyrus. In conclusion a reduction of the latency to onset of seizure activity and an increase of the SLE-frequency were found. These changes are indicators of increased excitability after SSSE. Other than the regional onset, the non-contiguous spread-pattern also indicates increased synchronicity of the neuronal network after SSSE. The autonomous activity in the dentate Gyrus shows, that the previously described structural changes in this region lead to functional deficits.

This master´s thesis deals with processing and analysis of data, acquired from experimental examination performed with functional magnetic resonance imaging technique. It is an oddball type experimental task and its goal is an examination of cognitive functions of the subject. The principles of functional magnetic resonance imaging, possibilities of experimental design, processing of acquired data, modeling of a response of organism and statistical analysis are described in this work. Furthermore, particular parts of preprocessing and analysis are carried out using real data set from experiment. The main goal of this work is suggestion and realization of model, which enables advanced categorization of stimuli, considering the type of previous rare stimulus and the number of frequent stimuli within them. With its in-depth categorization, this model enables detail exploration of cerebral processes, associated mainly with attention, memory, expectancy or cognitive closure. The second point of that work is an evaluation of models of hemodynamic response, which are applied in statistical analysis of data from fMRI experiment. Comparison of basis functions, the models of hemodynamic response to experimental stimulation used for general linear model, is performed in this work. The result of this comparison is an evaluation of detection efficiency of activated voxels, false positivity rate and computational and user difficulty.

L'étude par neuro-imagerie de l'activité de plusieurs cerveaux en interaction (hyperscanning) permet d'étendre notre compréhension des neurosciences sociales. Nous proposons un cadre pour l'hyperscanning utilisant les interfaces cerveau-ordinateur multi-utilisateur qui inclut différents paradigmes sociaux tels que la coopération ou la compétition. Les travaux de cette thèse comportent trois contributions interdépendantes. Notre première contribution est le développement d'une plateforme expérimentale sous la forme d'un jeu vidéo multijoueur, nommé Brain Invaders 2, contrôlé par la classification de potentiels évoqués visuels enregistrés par électroencéphalographie (EEG). Cette plateforme est validée par deux protocoles expérimentaux comprenant dix-neuf et vingt-deux paires de sujets et utilise différentes approches de classification adaptative par géométrie riemannienne. Ces approches sont théoriquement et expérimentalement comparées et nous montrons la supériorité de la fusion des classifieurs indépendants sur la classification d'un hypercerveau durant la seconde contribution. L'analyse de coïncidence des signaux entre les individus est une approche classique pour l'hyperscanning, elle est pourtant difficile quand les signaux EEG concernés sont transitoires avec une grande variabilité (intra- et inter-sujet) spatio-temporelle et avec un faible rapport signal-à-bruit. En troisième contribution, nous proposons un nouveau modèle composite de séparation aveugle de sources physiologiquement plausibles permettant de compenser cette variabilité. Une solution par diagonalisation conjointe approchée est proposée avec une implémentation d'un algorithme de type Jacobi. A partir des données de Brain Invaders 2, nous montrons que cette solution permet d'extraire simultanément des sources d'artéfacts, des sources d'EEG évoquées et des sources d'EEG continues avec plus de robustesse et de précision que les modèles existants
The study of several brains interacting (hyperscanning) with neuroimagery allows to extend our understanding of social neurosciences. We propose a framework for hyperscanning using multi-user Brain-Computer Interfaces (BCI) that includes several social paradigms such as cooperation or competition. This dissertation includes three interdependent contribution. The first contribution is the development of an experimental platform consisting of a multi-player video game, namely Brain Invaders 2, controlled by classification of visual event related potentials (ERP) recorded by electroencephalography (EEG). The plateform is validated through two experimental protocols including nineteen and twenty two pairs of subjects while using different adaptive classification approaches using Riemannian geometry. Those approaches are theoretically and experimentally compared during the second contribution ; we demonstrates the superiority in term of accuracy of merging independent classifications over the classification of the hyperbrain during the second contribution. Analysis of inter-brain synchronizations is a common approach for hyperscanning, however it is challenging for transient EEG waves with an great spatio-temporal variability (intra- and inter-subject) and with low signal-to-noise ratio such as ERP. Therefore, as third contribution, we propose a new blind source separation model, namely composite model, to extract simultaneously evoked EEG sources and ongoing EEG sources that allows to compensate this variability. A solution using approximate joint diagonalization is given and implemented with a fast Jacobi-like algorithm. We demonstrate on Brain Invaders 2 data that our solution extracts simultaneously evoked and ongoing EEG sources and performs better in term of accuracy and robustness compared to the existing models

Introdução: A atresia congênita de orelha constitui uma deformidade presente ao nascimento, de prevalência unilateral, decorrente da alteração no desenvolvimento das estruturas das orelhas externa e média. Geralmente, provoca perda auditiva condutiva, e pode ser acompanhada por componente sensorioneural. Dentre as formas de tratamento disponíveis, encontra-se o implante de orelha média Vibrant Soundbridge (VSB), que tem se mostrado eficaz no tratamento deste tipo de alteração. A literatura mostra melhora nos limiares tonais e nos resultados dos testes de percepção auditiva da fala, realizados com o uso do processador de fala após a cirurgia. Considerando que os indivíduos com este tipo de malformação podem passar por um período de privação sensorial auditiva anterior à reabilitação, torna-se interessante avaliar o estágio maturacional das estruturas auditivas corticais e o processamento das informações auditivas em nível central, bem como, verificar o benefício da indicação do VSB unilateral em situação de escuta difícil. Não foram encontrados estudos que abordam este aspecto e o emprego dos potenciais evocados auditivos corticais (PEAC) e do P300 em usuários de VSB. Objetivo: Analisar o impacto da perda auditiva condutiva e mista nos PEAC e P300 em usuários de VSB unilateral, com atresia de orelha bilateral, e verificar as habilidades auditivas, em situação de escuta difícil, considerando a indicação do VSB unilateral. Casuística e método: Vinte indivíduos, divididos em dois grupos, pareados em idade, sexo e grau de escolaridade. G1: dez indivíduos com perda auditiva condutiva ou mista bilateral, usuários de VSB unilateral, atendidos na Instituição de realização da pesquisa. Todos fizeram uso de aparelhos auditivos convencionais antes do VSB. G2: Dez indivíduos normo-ouvintes. Realização de audiometria em campo livre com o uso do VSB (apenas o G1), avaliação das habilidades auditivas pelo Hearing in Noise Test, pesquisa dos componentes P1, N1, P2, N2 e P300, em campo calibrado. Resultados: A média dos limiares tonais nas frequências de 500 a 3000 Hz, de 20 a 36 dB NA, mostrou que o VSB possibilitou o acesso aos sons da fala. Não foi observada diferença estatisticamente significante entre os valores de latência dos PEAC e P300 entre os grupos. Foi observada diferença estatisticamente significante entre o limiar de reconhecimento de sentenças e a relação sinal/ruído entre os grupos, sendo os melhores resultados apresentados pelo G2. Conclusão: Indivíduos com atresia de orelha e perda auditiva condutiva ou mista bilateral, quando adequadamente reabilitados, podem atingir a maturação das vias auditivas centrais e o processamento da informação auditiva em nível cortical. As habilidades de reconhecimento auditivo, sem e com ruído competitivo, mostraram-se defasadas quanto à normalidade, apontando para a indicação do VSB bilateral
Introduction: Congenital aural atresia is a congenital deformity. It is unilaterally prevalent due to alterations in the development of the external and middle ear structures. Congenital aural atresia causes conductive hearing loss and can be accompanied by sensorineural component. Among the available forms of treatment is the middle ear implant, Vibrant Soundbridge (VSB), which has been shown to be effective in treating this type of alteration. The literature shows improvement in tonal thresholds and in the results of tests of auditory perception of speech that were performed using the speech processor after surgery. Individuals with this type of malformation often experience a period of auditory sensory deprivation prior to rehabilitation. Hence, it is important to evaluate the maturation stage of the cortical auditory structures, the processing of auditory information at the central level, and to verify the benefit of unilateral VSB in difficult listening situations. There are no previous data on this aspect and with the use of cortical auditory evoked potentials (CAEP) and event-related potential (P300) in users of VSB. Aim: To analyze the impact of conductive and mixed hearing loss on CAEP and P300 in unilateral VSB users with bilateral ear atresia. To verify the auditory abilities in a difficult listening situation considering the indication for unilateral VSB. Materials and methods: Twenty individuals were divided into two groups matched for age, sex, and educational level. G1 comprised ten individuals with bilateral conductive or mixed hearing loss and users of unilateral VSB, who visited the research institution. All subjects used conventional hearing aids prior to VSB. G2 comprised ten normal hearing individuals. Audiometry in the free field was performed with the use of VSB (G1 only) and evaluation of hearing skills by the Hearing in Noise Test was conducted; components P1, N1, P2, N2, and P300 in a calibrated field were recorded. Results: Evaluation of the mean tonal thresholds in the frequencies between 500 and 3000 Hz, from 20 to 36 dB HL, demonstrated that VSB allowed access to speech sounds. There was no statistically significant difference in the CAEP and P300 latency values between the two groups. A statistically significant difference was observed in the sentence recognition threshold and the signal-to-noise ratio between the groups, with best results presented by G2. Conclusion: Individuals with congenital aural atresia and bilateral conductive or mixed hearing loss may reach maturation of the central auditory pathway and achieve adequate processing of auditory information at the cortical level, when rehabilitated. The auditory recognition skills, with and without competitive noise, were shown to be out of phase with normality, indicating the need for a bilateral VSB

Thesis (Ph. D.)--University of Wisconsin--Madison, 1992.
Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 65-71).

Thesis (M.S.)--University of Wisconsin--Madison, 1989.
Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 67-72).

L’attention visuo-spatiale peut être déployée à différentes localisations dans l’espace indépendamment de la direction du regard, et des études ont montré que les composantes des potentiels reliés aux évènements (PRE) peuvent être un index fiable pour déterminer si celle-ci est déployée dans le champ visuel droit ou gauche. Cependant, la littérature ne permet pas d’affirmer qu’il soit possible d’obtenir une localisation spatiale plus précise du faisceau attentionnel en se basant sur le signal EEG lors d’une fixation centrale. Dans cette étude, nous avons utilisé une tâche d’indiçage de Posner modifiée pour déterminer la précision avec laquelle l’information contenue dans le signal EEG peut nous permettre de suivre l’attention visuelle spatiale endogène lors de séquences de stimulation d’une durée de 200 ms. Nous avons utilisé une machine à vecteur de support (MVS) et une validation croisée pour évaluer la précision du décodage, soit le pourcentage de prédictions correctes sur la localisation spatiale connue de l’attention. Nous verrons que les attributs basés sur les PREs montrent une précision de décodage de la localisation du focus attentionnel significative (57%, p<0.001, niveau de chance à 25%). Les réponses PREs ont également prédit avec succès si l’attention était présente ou non à une localisation particulière, avec une précision de décodage de 79% (p<0.001). Ces résultats seront discutés en termes de leurs implications pour le décodage de l’attention visuelle spatiale, et des directions futures pour la recherche seront proposées.
Visuospatial attention can be deployed to different locations in space independently of ocular fixation, and studies have shown that event-related potential (ERP) components can effectively index whether such covert visuospatial attention is deployed to the left or right visual field. However, it is not clear whether we may obtain a more precise spatial localization of the focus of attention based on the EEG signals during central fixation. In this study, we used a modified Posner cueing task with an endogenous cue to determine the degree to which information in the EEG signal can be used to track visual spatial attention in presentation sequences lasting 200 ms. We used a machine learning classification method to evaluate how well EEG signals discriminate between four different locations of the focus of attention. We then used a multi-class support vector machine (SVM) and a leave-one-out cross-validation framework to evaluate the decoding accuracy (DA). We found that ERP-based features from occipital and parietal regions showed a statistically significant valid prediction of the location of the focus of visuospatial attention (DA = 57%, p < .001, chance-level 25%). The mean distance between the predicted and the true focus of attention was 0.62 letter positions, which represented a mean error of 0.55 degrees of visual angle. In addition, ERP responses also successfully predicted whether spatial attention was allocated or not to a given location with an accuracy of 79% (p < .001). These findings are discussed in terms of their implications for visuospatial attention decoding and future paths for research are proposed.