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Sellergren, Albin, Tobias Andersson i Jonathan Toft. "Signal processing through electroencephalography : Independent project in electrical engineering". Thesis, Uppsala universitet, Elektricitetslära, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-298771.
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This report is about a project where electroencephalography (EEG) wasused to control a two player game. The signals from the EEG-electrodeswere amplified, filtered and processed. Then the signals from the playerswere compared and an algorithm decided what would happen in the gamedepending on which signal was largest. The controls and the gaming mechanismworked as intended, however it was not possible to gather a signal fromthe brain with the method used in this project. So ultimately the goal wasnot reached.electroencephalography, EEG
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Birch, Gary Edward. "Single trial EEG signal analysis using outlier information". Thesis, University of British Columbia, 1988. http://hdl.handle.net/2429/28626.
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The goal of this thesis work was to study the characteristics of the EEG signal and then, based on the insights gained from these studies, pursue an initial investigation into a processing method that would extract useful event related information from single trial EEG. The fundamental tool used to study the EEG signal characteristics was autoregressive modeling. Early investigations pointed to the need to employ robust techniques in both model parameter estimation and signal estimation applications. Pursuing robust techniques ultimately led to the development of a single trial processing method which was based on a simple neurological model that assumed an additive outlier nature of event related potentials to the ongoing EEG process. When event related potentials, such as motor related potentials, are generated by a unique additional process they are "added" into the ongoing process and hence, will appear as additive outlier content when considered from the point of view of the ongoing process. By modeling the EEG with AR models with robustly estimated (GM-estimates) parameters and by using those models in a robust signal estimator, a "cleaned" EEG signal is obtained. The outlier content, data that is extracted from the EEG during cleaning, is then processed to yield event related information.
The EEG from four subjects formed the basis of the initial investigation into the viability of this single trial processing scheme. The EEG was collected under two conditions: an active task in which subjects performed a skilled thumb movement and an idle task in which subjects remained alert but did not carry out any motor activity. The outlier content was processed which provided single trial outlier waveforms. In the active case these waveforms possessed consistent features which were found to be related to events in the individual thumb movements. In the idle case the waveforms did not contain consistent features. Bayesian classification of active trials versus idle trials was carried out using a cost statistic resulting from the application of dynamic time warping to the outlier waveforms. Across the four subjects, when the decision boundary was set with the cost of misclassification equal, 93% of the active trials were classified correctly and 18% of the idle trials were incorrectly classified as active. When the cost of misclassifying an idle trial was set to be five times greater, 80% of the active trials were classified correctly and only 1.7% of the idle trials were incorrectly classified as active.Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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Li, Kun. "Advanced Signal Processing Techniques for Single Trial Electroencephalography Signal Classification for Brain Computer Interface Applications". Scholar Commons, 2010. http://scholarcommons.usf.edu/etd/3484.
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Brain Computer Interface (BCI) is a direct communication channel between brain and computer. It allows the users to control the environment without the need to control muscle activity [1-2]. P300-Speller is a well known and widely used BCI system that was developed by Farwell and Donchin in 1988 [3]. The accuracy level of the P300-BCI Speller as measured by the percent of communicated characters correctly identified by the system depends on the ability to detect the P300 event related potential (ERP) component among the ongoing electroencephalography (EEG) signal. Different techniques have been tested to reduce the number of trials needed to be averaged together to allow the reliable detection of the P300 response. Some of them have achieved high accuracies in multiple-trial P300 response detection. However the accuracy of single trial P300 response detection still needs to be improved. In this research, two single trial P300 response classification methods were designed. One is based on independent component analysis (ICA) with blind tracking and the other is based on variance analysis. The purpose of both methods is to detect a chosen character in real-time in the P300-BCI speller. The experimental results demonstrate that the proposed methods dramatically reduce the signal processing time, improve the data communication rate, and achieve overall accuracy of 79.1% for ICA based method and 84.8% for variance analysis based method in single trial P300 response classification task. Both methods showed better performance than that of the single trial stepwise linear discriminant analysis (SWLDA), which has been considered as the most accurate and practical technique working with P300-BCI Speller.
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Liu, Hui. "Online automatic epileptic seizure detection from electroencephalogram (EEG)". [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0012941.
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Shahbaz, Askari. "Dual mode brain near infrared spectroscopy and electroencephalography hardware design and signal processing". Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58418.
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Katyal, Bhavana. "Multiple current dipole estimation in a realistic head model using signal subspace methods". Online access for everyone, 2004. http://www.dissertations.wsu.edu/Thesis/Summer2004/b%5Fkatyal%5F072904.pdf.
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Mulyana, Ridwan S. "A Low Voltage, Low Power 4th Order Continuous-time Butterworth Filter for Electroencephalography Signal Recognition". The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1281981810.
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Hasan, Md Mahmudul. "Biomedical signal based drowsiness detection using machine learning: Singular and hybrid signal approaches". Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/211388/1/Md%20Mahmudul_Hasan_Thesis.pdf.
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Drowsiness is one of the main contributors to road crashes. This research program examines the utility of drowsiness detection based on singular and hybrid approaches using physiological signals of EEG, EOG, and ECG. Four supervised machine learning models were developed to detect drowsiness levels, using physiological features known to be associated with drowsiness and performance impairment. The ground truth was subjective sleepiness responses while performing a repetitive reaction time task. The outcome of the study indicates that the selected features provided higher performance in the hybrid approaches than the singular approaches, which could be useful for future research implications.
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Salma, Nabila. "EEG Signal Analysis in Decision Making". Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984237/.
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Decision making can be a complicated process involving perception of the present situation, past experience and knowledge necessary to foresee a better future. This cognitive process is one of the essential human ability that is required from everyday walk of life to making major life choices. Although it may seem ambiguous to translate such a primitive process into quantifiable science, the goal of this thesis is to break it down to signal processing and quantifying the thought process with prominence of EEG signal power variance. This paper will discuss the cognitive science, the signal processing of brain signals and how brain activity can be quantifiable through data analysis. An experiment is analyzed in this thesis to provide evidence that theta frequency band activity is associated with stress and stress is negatively correlated with concentration and problem solving, therefore hindering decision making skill. From the results of the experiment, it is seen that theta is negatively correlated to delta and beta frequency band activity, thus establishing the fact that stress affects internal focus while carrying out a task.
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Shahriari, Sheyda. "Electroencephalography (EEG) profile and sense of body ownership : a study of signal processing, proprioception and tactile illusion". Thesis, Brunel University, 2018. http://bura.brunel.ac.uk/handle/2438/16299.
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With the ability to feel through artificial limbs, users regain more function and increasingly see the prosthetics as parts of their own bodies. So, main focus of this project was dedicated to recuperating sensation by deception both in sighted and unsighted patients, started with illusionary experiments on healthy volunteers, brain signals were captured with medical EEG headsets during these tests to have a better understanding of how the brain works during body ownership illusions. EEG results suggest that gender difference exists in the perception of body transfer illusion. Visual input can be induced to trick the brain. Using the results, a new device has been designed (sound generator system-SGS) with the principal goal to find ways to include rich sensory feedback in prosthetic devices that would aid their incorporation of the user's body representation or schema. Studying the brain is fascinating; SGS tested and was found to have an adequate level of dexterity over course of one-month multiple times. After each try, the results were more tolerable than before that proved the idea that brain can learn and understand anything and can be manipulated temporary or lasting due to influences. Different methods used to validate the results, EEG acquisition, mapping subject brain function with EEG and finally interviewing participant after each attempt. Although the results of the illusion shows that when heat applies on rubber hand, subjects behave in similar manner as if their real hand was effected, but main question is still remains. How can the conditioning apply to daily life of amputees so that illusion become permanent? This is a rapidly developing field with advancements in technology and greater interdisciplinary integration of medicine, mechatronics and control engineering with the future looking to have permanent, low power consumption, highly functional devices with a greater intuitive almost natural feel using a variety of body signals including EMG, ultrasound, and Electrocorticography.
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Kwong, Siu-shing. "Detection of determinism of nonlinear time series with application to epileptic electroencephalogram analysis". View the Table of Contents & Abstract, 2005. http://sunzi.lib.hku.hk/hkuto/record/B35512222.
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Esteller, Rosana. "Detection of seizure onset in epileptic patients from intracranial EEG signals". Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/15620.
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Castillo, Ober, Simy Sotomayor, Guillermo Kemper i Vincent Clement. "Correspondence Between TOVA Test Results and Characteristics of EEG Signals Acquired Through the Muse Sensor in Positions AF7–AF8". Smart Innovation, Systems and Technologies, 2021. http://hdl.handle.net/10757/653803.
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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.This paper seeks to study the correspondence between the results of the test of variable of attention (TOVA) and the signals acquired by the Muse electroencephalogram (EEG) in the positions AF7 and AF8 of the cerebral cortex. There are a variety of research papers that estimates an index of attention in which the different characteristics in discrete signals of the brain activity were used. However, many of these results were obtained without contrasting them with standardized tests. Due to this fact, in the present work, the results will be compared with the score of the TOVA, which aims to identify an attention disorder in a person. The indicators obtained from the test are the response time variability, the average response time, and the d′ prime score. During the test, the characteristics of the EEG signals in the alpha, beta, theta, and gamma subbands such as the energy, average power, and standard deviation were extracted. For this purpose, the acquired signals are filtered to reduce the effect of the movement of the muscles near the cerebral cortex and then went through a subband decomposition process by applying transformed wavelet packets. The results show a well-marked correspondence between the parameters of the EEG signal of the indicated subbands and the visual attention indicators provided by TOVA. This correspondence was measured through Pearson’s correlation coefficient which had an average result of 0.8.
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Renfrew, Mark E. "A Comparison of Signal Processing and Classification Methods for Brain-Computer Interface". Case Western Reserve University School of Graduate Studies / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1246474708.
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Dalhoumi, Sami. "On pattern classification in motor imagery-based brain-computer interfaces". Thesis, Montpellier, 2015. http://www.theses.fr/2015MONTS240.
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Une interface cerveau-machine (ICM) est un système qui permet d'établir une communication directe entre le cerveau et un dispositif externe, en contournant les voies de sortie normales du système nerveux périphérique. Différents types d'ICMs existent dans la littérature. Parmi eux, les ICMs basées sur l'imagerie motrice sont les plus prometteuses. Elles sont basées sur l'autorégulation des rythmes sensorimoteurs par l'imagination de mouvement des membres différents (par exemple, imagination du mouvement de la main gauche et la main droite). Les ICMs basées sur l'imagerie motrice sont les meilleurs candidats pour les applications dédiées à des patients sévèrement paralysés mais elles sont difficiles à mettre en place parce que l'autorégulation des rythmes du cerveau n'est pas une tâche simple.Dans les premiers stades de la recherche en ICMs basées sur l'imagerie motrice, l'utilisateur devait effectuer des semaines, voire des mois, d'entrainement afin de générer des motifs d'activité cérébrale stables qui peuvent être décodés de manière fiable par le système. Le développement des techniques d'apprentissage automatique supervisé spécifiques à chaque utilisateur a permis de réduire considérablement la durée d'entrainement en ICMs. Cependant, ces techniques sont toujours confrontées aux problèmes de longue durée de calibrage et non-stationnarité des signaux cérébraux qui limitent l'utilisation de cette technologie dans la vie quotidienne. Bien que beaucoup de techniques d'apprentissage automatique avancées ont été essayées, ça reste toujours pas un problème non résolu.Dans cette thèse, j'étudie de manière approfondie les techniques d'apprentissage automatique supervisé qui ont été tentées afin de surmonter les problèmes de longue durée de calibrage et la non-stationnarité des signaux cérébraux en ICMs basées sur l'imagerie motrice. Ces techniques peuvent être classées en deux catégories: les techniques qui sont invariantes à la non-stationnarité et les techniques qui s'adaptent au changement. Dans la première catégorie, les techniques d'apprentissage par transfert entre différentes sessions et/ou différents individus ont attiré beaucoup d'attention au cours des dernières années. Dans la deuxième catégorie, différentes techniques d'adaptation en ligne des modèles d'apprentissage ont été tentées. Parmi elles, les techniques basées sur les potentiels d'erreurs sont les plus prometteuses. Les deux principales contributions de cette thèse sont basés sur des combinaisons linéaires des classificateurs. Ainsi, ces méthodes sont accordées un intérêt particulier tout au long de ce manuscrit. Dans la première contribution, je étudie l'utilisation de combinaisons linéaires des classificateurs dans les ICMs basées sur l'apprentissage par transfert et je propose une méthode de classification inter-sujets basée sur les combinaisons linéaires de classifieurs afin de réduire le temps de calibrage en ICMs. Je teste l'efficacité de la méthode de combinaison de classifieurs utilisée et j'étudie les cas ou l'apprentissage par transfert a un effet négatif sur les performances des ICMs. Dans la deuxième contribution, je propose une méthode de classification inter-sujets qui permet de combiner l'apprentissage par transfert l'adaptation en ligne. Dans cette méthode, l'apprentissage par transfert est effectué en combinant linéairement des classifieurs appris à partir de signaux EEG de différents sujets. L'adaptation en ligne est effectué en mettant à jours les poids de ces classifieurs d'une manière semi-superviséeA brain-computer interface (BCI) is a system that allows establishing direct communication between the brain and an external device, bypassing normal output pathways of peripheral neuromuscular system. Different types of BCIs exist in literature. Among them, BCIs based on motor imagery (MI) are the most promising ones. They rely on self-regulation of sensorimotor rhythms by imagination of movement of different limbs (e.g., left hand and right hand). MI-based BCIs are best candidates for applications dedicated to severely paralyzed patients but they are hard to set-up because self-regulation of brain rhythms is not a straightforward task.In early stages of BCI research, weeks and even months of user training was required in order to generate stable brain activity patterns that can be reliably decoded by the system. The development of user-specific supervised machine learning techniques allowed reducing considerably training periods in BCIs. However, these techniques are still faced with the problems of long calibration time and brain signals non-stationarity that limit the use of this technology in out-of-the-lab applications. Although many out-of-the-box machine learning techniques have been attempted, it is still not a solved problem.In this thesis, I thoroughly investigate supervised machine learning techniques that have been attempted in order to overcome the problems of long calibration time and brain signals non-stationarity in MI-based BCIs. These techniques can be mainly classified into two categories: techniques that are invariant to non-stationarity and techniques that adapt to the change. In the first category, techniques based on knowledge transfer between different sessions and/or subjects have attracted much attention during the last years. In the second category, different online adaptation techniques of classification models were attempted. Among them, techniques based on error-related potentials are the most promising ones. The aim of this thesis is to highlight some important points that have not been taken into consideration in previous work on supervised machine learning in BCIs and that have to be considered in future BCI systems in order to bring this technology out of the lab. The two main contributions of this thesis are based on linear combinations of classifiers. Thus, these methods are given a particular interest throughout this manuscript. In the first contribution, I study the use of linear combinations of classifiers in knowledge transfer-based BCIs and I propose a novel ensemble-based knowledge transfer framework for reducing calibration time in BCIs. I investigate the effectiveness of the classifiers combination scheme used in this framework when performing inter-subjects classification in MI-based BCIs. Then, I investigate to which extent knowledge transfer is useful in BCI applications by studying situations in which knowledge transfer has a negative impact on classification performance of target learning task. In the second contribution, I propose an online inter-subjects classification framework that allows taking advantage from both knowledge transfer and online adaptation techniques. In this framework, called “adaptive accuracy-weighted ensemble” (AAWE), inter-subjects classification is performed using a weighted average ensemble in which base classifiers are learned using EEG signals recorded from different subjects and weighted according to their accuracies in classifying brain signals of the new BCI user. Online adaptation is performed by updating base classifiers' weights in a semi-supervised way based on ensemble predictions reinforced by interaction error-related potentials
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Bendoukha, Hocine. "Détection automatique des évènements paroxystiques dans le signal EEG". Rouen, 1989. http://www.theses.fr/1989ROUES029.
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Etude des méthodes de détection automatiques des évènements paroxystiques électroencéphalographiques chez des malades épileptiques. Les signaux EEG proviennent d'enregistrements ambulatoires de longue durée. L'approche consiste à définir des descripteurs quantitatifs et qualitatifs du signal EEG
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Provencher, David. "Imagerie de l'activité cérébrale : structure ou signal?" Thèse, Université de Sherbrooke, 2017. http://hdl.handle.net/11143/10472.
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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.
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El, Sayed Hussein Jomaa Mohamad. "Signal processing of electroencephalograms with 256 sensors in epileptic children". Thesis, Angers, 2019. http://www.theses.fr/2019ANGE0028.
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Dans cette thèse, nous proposons des méthodes de traitement du signal et les appliquons à des signaux d’électro-encéphalographie (EEG) enregistrés chez des patients épileptiques. L’objectif est de pouvoir quantifier l’état du patient et d’étudier l’évolution du trouble neurologique au cours du temps. Les méthodes que nous avons développées sont basées sur des mesures d’entropie. Ainsi, nous introduisons la « multivariate Improved Weighted Multi-scale Permutation Entropy» (mvIWMPE) que nous appliquons à des signaux EEG d’enfants sains et épileptiques. Elle donne des résultats prometteurs. Nous proposons également une approche multivariée pour la « Sample Entropy». Les résultats montrent qu’elle permet de traiter correctement un plus grand nombre de canaux que la méthode existante. Nous présentons aussi une mesure de complexité temps-fréquence variable dans le temps, basée sur la « Singular Value Decomposition » et la « Rényi Entropy ». Ces mesures, appliquées sur l’EEG d’enfants épileptiques avant et 4-6 semaines après un traitement, conduisent à des résultats qui sont en accord avec le diagnostic clinique quant à l’évolution de la pathologie. La dernière partie de la thèse porte sur les mesures de connectivité fonctionnelle. Nous proposons une méthode de connectivité fonctionnelle basée sur la mvIWMPE et l’information mutuelle. Elle est appliquée sur des signaux EEG d’enfants sains au repos. A l’aide de mesures de réseau, nous pouvons identifier des régions cérébrales actives dans des réseaux précédemment découverts grâce à l’imagerie par résonance magnétique fonctionnelle. La méthode est également utilisée pour étudier les réseaux chez des enfants épileptiquesIn this thesis, our focus is to develop signal processing methods to be used on electroencephalography (EEG) signals recorded from epileptic patients. The aim of these methods is to be able to quantify the state of the patient with epilepsy and to study the progress of the neurological disorder over time. The methods we developed are based on entropy. From previous permutation entropy methods we introduce the multivariate Improved Weighted Multi-scale Permutation Entropy (mvIWMPE). This method is applied on EEG signals of both healthy and epileptic children and gives promising results. We also introduce a new multivariate approach for sample entropy and, when tested and compared with the existing multivariate approach, we find that the introduced approach is much betterin handling a larger numbers of channels. We also introduce a time-varying time frequency complexity measure based on Singular Value Decomposition and Rényi Entropy. These measures are applied on EEG of epileptic children before and after 4-6 weeks of treatment. The results come in correspondence with the clinical diagnosis from the hospital on whether the patients improve or not. The final part of the thesis focuses on functional connectivity measures. We introduce a new functional connectivity method based on mvIWMPE and Mutual Information. The method is applied on EEG signals of healthy children at rest. Using network measures, we are able to identify regions in the brain that are active in networks previously found using functional magnetic resonance imaging. The method is also used to study the networks of epileptic children at several points throughout the treatment
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Hodulíková, Tereza. "Analýza EEG během anestezie". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-220369.
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This master's thesis deals with the method of functional examination of brain electric activity. In the first part is description of central nervous system, method of electroencephalography and possible connections. Furthermor the project involves characteristic of EEG signal and its artifacts. It also includes signal processing and list of symptoms, which will be used for an analysis of the EEG during anesthesia. The second part of thesis involves development of application, which allow viewing and proccesing of EEG signal. In conclusion of thesis is carried out unequal segmentation and statistical processing.
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Kalunga, Emmanuel. "Vers des interfaces cérébrales adaptées aux utilisateurs : interaction robuste et apprentissage statistique basé sur la géométrie riemannienne". Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLV041/document.
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Au cours des deux dernières décennies, l'intérêt porté aux interfaces cérébrales ou Brain Computer Interfaces (BCI) s’est considérablement accru, avec un nombre croissant de laboratoires de recherche travaillant sur le sujet. Depuis le projet Brain Computer Interface, où la BCI a été présentée à des fins de réadaptation et d'assistance, l'utilisation de la BCI a été étendue à d'autres applications telles que le neurofeedback et l’industrie du jeux vidéo. Ce progrès a été réalisé grâce à une meilleure compréhension de l'électroencéphalographie (EEG), une amélioration des systèmes d’enregistrement du EEG, et une augmentation de puissance de calcul.Malgré son potentiel, la technologie de la BCI n’est pas encore mature et ne peut être utilisé en dehors des laboratoires. Il y a un tas de défis qui doivent être surmontés avant que les systèmes BCI puissent être utilisés à leur plein potentiel. Ce travail porte sur des aspects importants de ces défis, à savoir la spécificité des systèmes BCI aux capacités physiques des utilisateurs, la robustesse de la représentation et de l'apprentissage du EEG, ainsi que la suffisance des données d’entrainement. L'objectif est de fournir un système BCI qui peut s’adapter aux utilisateurs en fonction de leurs capacités physiques et des variabilités dans les signaux du cerveau enregistrés.À ces fins, deux voies principales sont explorées : la première, qui peut être considérée comme un ajustement de haut niveau, est un changement de paradigmes BCI. Elle porte sur la création de nouveaux paradigmes qui peuvent augmenter les performances de la BCI, alléger l'inconfort de l'utilisation de ces systèmes, et s’adapter aux besoins des utilisateurs. La deuxième voie, considérée comme une solution de bas niveau, porte sur l’amélioration des techniques de traitement du signal et d’apprentissage statistique pour améliorer la qualité du signal EEG, la reconnaissance des formes, ainsi que la tache de classification.D'une part, une nouvelle méthodologie dans le contexte de la robotique d'assistance est définie : il s’agit d’une approche hybride où une interface physique est complémentée par une interface cérébrale pour une interaction homme-machine plus fluide. Ce système hybride utilise les capacités motrices résiduelles des utilisateurs et offre la BCI comme un choix optionnel : l'utilisateur choisit quand utiliser la BCI et peut alterner entre les interfaces cérébrales et musculaire selon le besoin.D'autre part, pour l’amélioration des techniques de traitement du signal et d'apprentissage statistique, ce travail utilise un cadre Riemannien. Un frein majeur dans le domaine de la BCI est la faible résolution spatiale du EEG. Ce problème est dû à l'effet de conductance des os du crâne qui agissent comme un filtre passe-bas non linéaire, en mélangeant les signaux de différentes sources du cerveau et réduisant ainsi le rapport signal-à-bruit. Par conséquent, les méthodes de filtrage spatial ont été développées ou adaptées. La plupart d'entre elles – à savoir la Common Spatial Pattern (CSP), la xDAWN et la Canonical Correlation Analysis (CCA) – sont basées sur des estimations de matrice de covariance. Les matrices de covariance sont essentielles dans la représentation d’information contenue dans le signal EEG et constituent un élément important dans leur classification. Dans la plupart des algorithmes d'apprentissage statistique existants, les matrices de covariance sont traitées comme des éléments de l'espace euclidien. Cependant, étant symétrique et défini positive (SDP), les matrices de covariance sont situées dans un espace courbe qui est identifié comme une variété riemannienne. Utiliser les matrices de covariance comme caractéristique pour la classification des signaux EEG, et les manipuler avec les outils fournis par la géométrie de Riemann, fournit un cadre solide pour la représentation et l'apprentissage du EEGIn the last two decades, interest in Brain-Computer Interfaces (BCI) has tremendously grown, with a number of research laboratories working on the topic. Since the Brain-Computer Interface Project of Vidal in 1973, where BCI was introduced for rehabilitative and assistive purposes, the use of BCI has been extended to more applications such as neurofeedback and entertainment. The credit of this progress should be granted to an improved understanding of electroencephalography (EEG), an improvement in its measurement techniques, and increased computational power.Despite the opportunities and potential of Brain-Computer Interface, the technology has yet to reach maturity and be used out of laboratories. There are several challenges that need to be addresses before BCI systems can be used to their full potential. This work examines in depth some of these challenges, namely the specificity of BCI systems to users physical abilities, the robustness of EEG representation and machine learning, and the adequacy of training data. The aim is to provide a BCI system that can adapt to individual users in terms of their physical abilities/disabilities, and variability in recorded brain signals.To this end, two main avenues are explored: the first, which can be regarded as a high-level adjustment, is a change in BCI paradigms. It is about creating new paradigms that increase their performance, ease the discomfort of using BCI systems, and adapt to the user’s needs. The second avenue, regarded as a low-level solution, is the refinement of signal processing and machine learning techniques to enhance the EEG signal quality, pattern recognition and classification.On the one hand, a new methodology in the context of assistive robotics is defined: it is a hybrid approach where a physical interface is complemented by a Brain-Computer Interface (BCI) for human machine interaction. This hybrid system makes use of users residual motor abilities and offers BCI as an optional choice: the user can choose when to rely on BCI and could alternate between the muscular- and brain-mediated interface at the appropriate time.On the other hand, for the refinement of signal processing and machine learning techniques, this work uses a Riemannian framework. A major limitation in this filed is the EEG poor spatial resolution. This limitation is due to the volume conductance effect, as the skull bones act as a non-linear low pass filter, mixing the brain source signals and thus reducing the signal-to-noise ratio. Consequently, spatial filtering methods have been developed or adapted. Most of them (i.e. Common Spatial Pattern, xDAWN, and Canonical Correlation Analysis) are based on covariance matrix estimations. The covariance matrices are key in the representation of information contained in the EEG signal and constitute an important feature in their classification. In most of the existing machine learning algorithms, covariance matrices are treated as elements of the Euclidean space. However, being Symmetric and Positive-Definite (SPD), covariance matrices lie on a curved space that is identified as a Riemannian manifold. Using covariance matrices as features for classification of EEG signals and handling them with the tools provided by Riemannian geometry provide a robust framework for EEG representation and learning
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Maczka, Melissa May. "Investigations into the effects of neuromodulations on the BOLD-fMRI signal". Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:96d46d4d-480b-48d7-9f2d-060e76c5f8aa.
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The blood oxygen level dependent functional MRI (BOLD-fMRI) signal is an indirect measure of the neuronal activity that most BOLD studies are interested in. This thesis uses generative embedding algorithms to investigate some of the challenges and opportunities that this presents for BOLD imaging. It is standard practice to analyse BOLD signals using general linear models (GLMs) that assume fixed neurovascular coupling. However, this assumption may cause false positive or negative neural activations to be detected if the biological manifestations of brain diseases, disorders and pharmaceutical drugs (termed "neuromodulations") alter this coupling. Generative embedding can help overcome this problem by identifying when a neuromodulation confounds the standard GLM. When applied to anaesthetic neuromodulations found in preclinical imaging data, Fentanyl has the smallest confounding effect and Pentobarbital has the largest, causing extremely significant neural activations to go undetected. Half of the anaesthetics tested caused overestimation of the neuronal activity but the other half caused underestimation. The variability in biological action between anaesthetic modulations in identical brain regions of genetically similar animals highlights the complexity required to comprehensively account for factors confounding neurovascular coupling in GLMs generally. Generative embedding has the potential to augment established algorithms used to compensate for these variations in GLMs without complicating the standard (ANOVA) way of reporting BOLD results. Neuromodulation of neurovascular coupling can also present opportunities, such as improved diagnosis, monitoring and understanding of brain diseases accompanied by neurovascular uncoupling. Information theory is used to show that the discriminabilities of neurodegenerative-diseased and healthy generative posterior parameter spaces make generative embedding a viable tool for these commercial applications, boasting sensitivity to neurovascular coupling nonlinearities and biological interpretability. The value of hybrid neuroimaging systems over separate neuroimaging technologies is found to be greatest for early-stage neurodegenerative disease.
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Polanský, Štěpán. "Zpracování elektroencefalografických signálů". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-219242.
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This work describes basics of electroencaphalography, measuring electroencaphalography signals, their processing and evaluation. There is discussed method of topography mapping of brain activity called brainmapping. The practical part contains description of design aplication in Matlab.
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Courtellemont, Pierre. "Architecture multi-processeurs pour le traitement du signal EEG". Rouen, 1989. http://www.theses.fr/1989ROUES003.
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L'algorithme proposé repose sur le principe des moindres carrés récursifs. Il diffère des méthodes usuelles par une adaptation des paramètres qui se fait globalement sur une fenêtre d'observation et non à chaque nouvel échantillon. Cette technique a permis la mise au point d'un algorithme de détection à deux seuils successifs
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Trebaul, Lena. "Développement d'outils de traitement du signal et statistiques pour l'analyse de groupe des réponses induites par des stimulations électriques corticales directes chez l'humain". Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAS045/document.
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Introduction : La stimulation électrique directe basse fréquence est pratiquée sur des patients épileptiques pharmaco-résistants implantés avec des électrodes profondes. Elle induit de potentiels évoqués cortico-corticaux (PECC) qui permettent d’estimer la connectivité in vivo et ont permis de caractériser des réseaux locaux. Pour estimer la connectivité à l’échelle du cortex, le projet multicentrique F-TRACT vise à rassembler plusieurs centaines de patients dans une base de données pour proposer un atlas probabiliste de tractographie fonctionnelle.Méthodes : La construction de la base de données à nécessité la mise en place technique de pipelines de traitement semi-automatiques pour faciliter la gestion du nombre important de données de stéréo-électroencéphalographie (SEEG) et d’imagerie. Ces pipelines incluent des nouvelles méthodes de traitement du signal et d’apprentissage automatique, qui ont été développées pour identifier automatiquement les mauvais contacts et corriger l’artefact induit par la stimulation. Les analyses de groupe se sont basées sur des métriques des PECC et des cartes temps-fréquences des réponses à la stimulation.Résultats : La performance des méthodes développées pour le projet a été validée sur des données hétérogènes, en termes de paramètres d’acquisition et de stimulation, provenant de différents centres hospitaliers. L’atlas a été généré à partir d’un échantillon de 173 patients, fournissant une mesure de probabilité de connectivité pour 79% des connexions et d’estimer des propriétés biophysiques des fibres pour 46% d’entre elles. Son application à une sous-population de patients a permis d’étudier les réseaux impliqués dans la génération de symptômes auditifs. L’analyse de groupe oscillatoire a mis en avant l’influence de l’anatomie sur la réponse à la stimulation.Discussion : Cette thèse présente une méthodologie d’étude des PECC à l’échelle du cortex cérébral, utilisant des données hétérogènes en termes d’acquisition, de paramètres de stimulation et spatialement. L’incrémentation du nombre de patients dans l’atlas généré permettra d’étudier les interactions cortico-corticales de manière causaleIntroduction: Low-frequency direct electrical stimulation is performed in drug-resistant epileptic patients, implanted with depth electrodes. It induces cortico-cortical evoked potentials (CCEP) that allow in vivo connectivity mapping of local networks. The multicentric project F-TRACT aims at gathering data of several hundred patients in a database to build a propabilistic functional tractography atlas that estimates connectivity at the cortex level.Methods: Semi-automatic processing pipelines have been developed to handle the amount of stereo-electroencephalography (SEEG) and imaging data and store them in a database. New signal processing and machine-learning methods have been developed and included in the pipelines, in order to automatically identify bad channels and correct the stimulation artifact. Group analyses have been performed using CCEP features and time-frequency maps of the stimulation responses.Results: The new methods performance has been assessed on heterogeneous data, coming from different hospital center recording and stimulating using variable parameters. The atlas was generated from a sample of 173 patients, providing a connectivity probability value for 79% of the possible connections and estimating biophysical properties of fibers for 46% of them. The methodology was applied on patients who experienced auditory symptoms that allowed the identification of different networks involved in hallucination or illusion generation. Oscillatory group analysis showed that anatomy was driving the stimulation response pattern.Discussion: A methodology for CCEP study at the cerebral cortex scale is presented in this thesis. Heterogeneous data in terms of acquisition and stimulation parameters and spatially were used and handled. An increasing number of patients’ data will allow the maximization of the statistical power of the atlas in order to study causal cortico-cortical interactions
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Samandari, Rohan. "Integration of Bluetooth Sensors in a Windows-Based Research Platform". Thesis, Malmö universitet, Institutionen för datavetenskap och medieteknik (DVMT), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43037.
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This thesis describes how to build a solution for transmitting data from an Electroencephalography (EEG) device to a server in real-time while guiding the user through a number of predefined exercises. This solution will be used by Spinal Cord Injury (SCI) patients suffering from neuropathic pain, in order to understand if it is possible to predict such pain from EEG. The collected data will help clinicians analyze the brain activity data from patients who can submit the data from their home. To accomplish this development task, an application was built that connects to a portable EEG device, gather brain activity data from patients, guides patients through a set of imaginary tasks and sends the data to a server. This project made use of a Software Development Kit (SDK) for the Python programming language and a web sockets server written in JavaScript. The application was tested both in terms of usability and end-to-end latency, showing high usability and low latency. The proposed solution will support a clinical trial in Spain with 40 SCI patients.
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Ablin, Pierre. "Exploration of multivariate EEG /MEG signals using non-stationary models". Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT051.
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L'Analyse en Composantes Indépendantes (ACI) modèle un ensemble de signaux comme une combinaison linéaire de sources indépendantes. Cette méthode joue un rôle clé dans le traitement des signaux de magnétoencéphalographie (MEG) et électroencéphalographie (EEG). L'ACI de tels signaux permet d'isoler des sources de cerveau intéressantes, de les localiser, et de les séparer d'artefacts. L'ACI fait partie de la boite à outils de nombreux neuroscientifiques, et est utilisée dans de nombreux articles de recherche en neurosciences. Cependant, les algorithmes d'ACI les plus utilisés ont été développés dans les années 90. Ils sont souvent lents lorsqu'ils sont appliqués sur des données réelles, et sont limités au modèle d'ACI classique.L'objectif de cette thèse est de développer des algorithmes d'ACI utiles en pratique aux neuroscientifiques. Nous suivons deux axes. Le premier est celui de la vitesse : nous considérons le problème d'optimisation résolu par deux des algorithmes les plus utilisés par les praticiens: Infomax et FastICA. Nous développons une nouvelle technique se basant sur un préconditionnement par des approximations de la Hessienne de l'algorithm L-BFGS. L'algorithme qui en résulte, Picard, est conçu pour être appliqué sur données réelles, où l'hypothèse d’indépendance n'est jamais entièrement vraie. Sur des données de M/EEG, il converge plus vite que les implémentations `historiques'.Les méthodes incrémentales, qui traitent quelques échantillons à la fois au lieu du jeu de données complet, constituent une autre possibilité d’accélération de l'ACI. Ces méthodes connaissent une popularité grandissante grâce à leur faculté à bien passer à l'échelle sur de grands jeux de données. Nous proposons un algorithme incrémental pour l'ACI, qui possède une importante propriété de descente garantie. En conséquence, cet algorithme est simple d'utilisation, et n'a pas de paramètre critique et difficile à régler comme un taux d'apprentissage.En suivant un second axe, nous proposons de prendre en compte du bruit dans le modèle d'ACI. Le modèle resultant est notoirement difficile et long à estimer sous l'hypothèse standard de non-Gaussianité de l'ACI. Nous nous reposons donc sur une hypothèse de diversité spectrale, qui mène à un algorithme facile d'utilisation et utilisable en pratique, SMICA. La modélisation du bruit permet de nouvelles possibilités inenvisageables avec un modèle d'ACI classique, comme une estimation fine des source et l'utilisation de l'ACI comme une technique de réduction de dimension statistiquement bien posée. De nombreuses expériences sur données M/EEG démontrent l'utilité de cette nouvelle approche.Tous les algorithmes développés dans cette thèse sont disponibles en accès libre sur internet. L’algorithme Picard est inclus dans les librairies de traitement de données M/EEG les plus populaires en Python (MNE) et en Matlab (EEGlab)Independent Component Analysis (ICA) models a set of signals as linear combinations of independent sources. This analysis method plays a key role in electroencephalography (EEG) and magnetoencephalography (MEG) signal processing. Applied on such signals, it allows to isolate interesting brain sources, locate them, and separate them from artifacts. ICA belongs to the toolbox of many neuroscientists, and is a part of the processing pipeline of many research articles. Yet, the most widely used algorithms date back to the 90's. They are often quite slow, and stick to the standard ICA model, without more advanced features.The goal of this thesis is to develop practical ICA algorithms to help neuroscientists. We follow two axes. The first one is that of speed. We consider the optimization problems solved by two of the most widely used ICA algorithms by practitioners: Infomax and FastICA. We develop a novel technique based on preconditioning the L-BFGS algorithm with Hessian approximation. The resulting algorithm, Picard, is tailored for real data applications, where the independence assumption is never entirely true. On M/EEG data, it converges faster than the `historical' implementations.Another possibility to accelerate ICA is to use incremental methods, which process a few samples at a time instead of the whole dataset. Such methods have gained huge interest in the last years due to their ability to scale well to very large datasets. We propose an incremental algorithm for ICA, with important descent guarantees. As a consequence, the proposed algorithm is simple to use and does not have a critical and hard to tune parameter like a learning rate.In a second axis, we propose to incorporate noise in the ICA model. Such a model is notoriously hard to fit under the standard non-Gaussian hypothesis of ICA, and would render estimation extremely long. Instead, we rely on a spectral diversity assumption, which leads to a practical algorithm, SMICA. The noise model opens the door to new possibilities, like finer estimation of the sources, and use of ICA as a statistically sound dimension reduction technique. Thorough experiments on M/EEG datasets demonstrate the usefulness of this approach.All algorithms developed in this thesis are open-sourced and available online. The Picard algorithm is included in the largest M/EEG processing Python library, MNE and Matlab library, EEGlab
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Lahr, Jacob [Verfasser], i Andreas [Akademischer Betreuer] Schulze-Bonhage. "Electromyographic signals in intracranial electroencephalographic recordings = Elektromyographische Signale in intrakraniellen EEG-Aufnahmen". Freiburg : Universität, 2012. http://d-nb.info/1123473927/34.
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Hitziger, Sebastian. "Modélisation de la variabilité de l'activité électrique dans le cerveau". Thesis, Nice, 2015. http://www.theses.fr/2015NICE4015/document.
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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 pointeThis 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
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Colot, Olivier. "Apprentissage et détection automatique de changements de modèles : application aux signaux électroencéphalographiques". Rouen, 1993. http://www.theses.fr/1993ROUES012.
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La thèse présentée traite du problème de la détection de changements de modèles dans des signaux vectoriels lentement variables. L'étude s'articule autour de deux thèmes: modélisation vectorielle, détection de changements de modèles. Le premier thème est traité sous l'angle d'une technique récursive de modélisation linéaire, tirant profit des propriétés de stationnarité locale des signaux étudiés dans un contexte vectoriel. Dans une seconde partie, le problème de la détection de changements de modèles est étudié. Deux approches sont proposées et testées: la première s'appuie sur un critère d'énergie d'erreurs issues de la modélisation, la seconde est fondée sur la comparaison d'histogrammes approchant des lois de probabilité, les histogrammes étant construits à l'aide d'un critère de type Akaike. La détection de changements de modèles, synonymes de changements de lois, est effectuée à l'aide de mesure de dissemblance. La validation de ces méthodes est réalisée sur des signaux biomédicaux: les signaux électroencéphalographiques
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Rousseau, Sandra. "Influence du retour sensoriel dans les interfaces cerveau machine EEG : étude du potentiel d'erreur". Thesis, Grenoble, 2012. http://www.theses.fr/2012GRENT101/document.
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Dans cette thèse nous proposons d'étudier le potentiel d'erreur et sa possible intégration dans les ICMs (Interfaces cerveau machine). Le potentiel d'erreur (ErrP) est un potentiel généré par le cerveau lors de l'observation d'une erreur. Sa détection essai par essai pourrait permettre la mise en place d'une boucle de contrôle dans les ICMs. Cependant son RSB étant très faible cette détection est difficile. Ici nous proposons une étude complète de ce système. Dans un premier temps nous étudions de manière détaillée ses différentes caractéristiques (temporelles, fréquentielles..). A partir de ces observations nous proposons des méthodes de filtrage adaptées permettant d'augmenter le RSB de l'ErrP et ainsi d'améliorer les performances de détection essai par essai. Enfin nous étudions quelles stratégies d'intégration peuvent etre envisagées et quelles améliorations ces différentes stratégies peuvent apporter aux systèmes ICMsIn this thesis we study the error-related potential (ErrP) and its possible integration in BCIs (Brain Computer Interfaces). The error-related potential is an evoked potential generated by brain electrical activity when observing an error. Its single-trial detection would allow the integration of control loops in BCIs. However its signal to noise ratio (SNR) is very low making its single-trial detection difficult. In the first part of this thesis we study the different characteristics (temporal, frequential…) of the ErrP. Then from these observations we develop specific filtering methods in order to improve the ErrP SNR and thus improve its single trial detection. In the last part of the thesis we study several integration strategies and conclude on what kind of improvement might be reached by using these strategies in actual BCI systems
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Tano, Ange Guillaume. "Events prediction in electroencephalographic signals". Thesis, University of Strathclyde, 2016. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=27227.
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Brain Machine Interface (BMI) or Brain Computer Interface (BCI) technologies provide the prospect of regenerating or replacing functions lost due to motor disabilities. BCIs connect the brain to a computer which translates the electrical activity of the brain into commands used to control external devices, hence allowing people with motor disability to control their external environment through a non-muscular communication channel. A BCI operates by transforming electrophysiological signals, known as Electroencephalogram (EEG) signals, from the user into device commands under an operating protocol. The protocol initialises and defines the nature of the communication (i.e., discrete or continuous). It also determines the strategy which underpins the generation of the signals used by the system (i.e., what triggers the changes within the EEG signals). While protocols involving brisk and very constrained movements have been widely explored in BCI studies, more natural movements have barely been considered. The choice of protocols that entail non-realistic movements emanates from the generation of well understood neuronal correlates modulated by the execution of such movements, leading to a lack of freedom in the design of BCI protocols. The development of algorithms translating EEG signals into commands that control external devices,a task termed as event detection in the present research, are a central part of any BCI system. However, most of the time, complex methods are used and most event detection in BCI development is devoted to the optimisation of such methods. Furthermore, the methods require extensive training of the user, putting a mental load on the user. The present study aims to investigate simple, but powerful, event detection methods requiring minimal training and the use of a protocol involving natural movements. Scalp EEG data was recorded from nine participants using natural hand movements. In particular, self-paced reaching hand movements were considered. The data was investigated in a pseudo time frequency domain using continuous wavelet coefficients. Methods using wavelet modulus maxima, the Mahalanobis distance, and bootstrapping of the Mahalanobis distance were developed for event detection. The data was analysed over a frequency range from 0.1 Hz to 25 Hz, covering the Slow Cortical Potentials (SCP), Mu and Beta frequency bands. The results showed that the method using wavelet modulus maxima was able to predict reaching hand movements onset in the SCP, Mu and Beta frequency bands about 1 s before movement onsetand yielded an maximum average prediction rate of approximately 80%. The Mahalanolobisdistance and the bootstrap methods were able to predict reaching hand movements initiation inthe SCP band about 1 s before movement onset with a maximum prediction rate of approximately 70%. The study has demonstrated that human voluntary movements can be predicted approximatively 1 s prior to movement onset with a good prediction rate. The study may contribute to the understanding of the planning and the control of human voluntary movements. Furthermore, the present research may contribute in designing advanced assistive devices in general and in particular may contribute in improving BCI systems design. Finally, the results may encourage the use of natural movements during BCI protocols design aiming to predict movement initiation and the monitoring of the mental state of BCI users.
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Bhalotiya, Anuj Arun. "Brain Computer Interface (BCI) Applications: Privacy Threats and Countermeasures". Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984122/.
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In recent years, brain computer interfaces (BCIs) have gained popularity in non-medical domains such as the gaming, entertainment, personal health, and marketing industries. A growing number of companies offer various inexpensive consumer grade BCIs and some of these companies have recently introduced the concept of BCI "App stores" in order to facilitate the expansion of BCI applications and provide software development kits (SDKs) for other developers to create new applications for their devices. The BCI applications access to users' unique brainwave signals, which consequently allows them to make inferences about users' thoughts and mental processes. Since there are no specific standards that govern the development of BCI applications, its users are at the risk of privacy breaches. In this work, we perform first comprehensive analysis of BCI App stores including software development kits (SDKs), application programming interfaces (APIs), and BCI applications w.r.t privacy issues. The goal is to understand the way brainwave signals are handled by BCI applications and what threats to the privacy of users exist. Our findings show that most applications have unrestricted access to users' brainwave signals and can easily extract private information about their users without them even noticing. We discuss potential privacy threats posed by current practices used in BCI App stores and then describe some countermeasures that could be used to mitigate the privacy threats. Also, develop a prototype which gives the BCI app users a choice to restrict their brain signal dynamically.
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Acar, Erman. "Classification Of Motor Imagery Tasks In Eeg Signal And Its Application To A Brain-computer Interface For Controlling Assistive Environmental Devices". Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12612994/index.pdf.
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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 extractionCommon 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.
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Gallego, Jutglà Esteve. "New signal processing and machine learning methods for EEG data analysis of patients with Alzheimer's disease". Doctoral thesis, Universitat de Vic - Universitat Central de Catalunya, 2015. http://hdl.handle.net/10803/290853.
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Les malalties neurodegeneratives són un conjunt de malalties que afecten al cervell. Aquestes malalties estan relacionades amb la pèrdua progressiva de l'estructura o la funció de les neurones, incloent-hi la mort d'aquestes. La malaltia de l'Alzheimer és una de les malalties neurodegeneratives més comunes. Actualment, no es coneix cap cura per a l'Alzheimer, però es creu que hi ha un grup de medicaments que el que fan és retardar-ne els principals símptomes. Aquests s'han de prendre en les primeres fases de la malaltia ja que sinó no tenen efecte. Per tant, el diagnòstic precoç de la malaltia de l'Alzheimer és un factor clau.
En aquesta tesis doctoral s'han estudiat diferents aspectes relacionats amb la neurociència per investigar diferents eines que permetin realitzar un diagnòstic precoç de la malaltia en qüestió. Per fer-ho, s'han treballat diferents aspectes com el preprocessament de dades, l'extracció de característiques, la selecció de característiques i la seva posterior classificació.Neurodegenerative diseases are a group of disorders that affect the brain. These diseases are related with changes in the brain that lead to loss of brain structure or loss of neurons, including the dead of some neurons. Alzheimer's disease (AD) is one of the most well-known neurodegenerative diseases. Nowadays there is no cure for this disease. However, there are some medicaments that may delay the symptoms if they are used during the first stages of the disease, otherwise they have no effect. Therefore early diagnose is presented as a key factor. This PhD thesis works different aspects related with neuroscience, in order to develop new methods for the early diagnose of AD. Different aspects have been investigated, such as signal preprocessing, feature extraction, feature selection and its classification.
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Stefano, Filho Carlos Alberto 1991. "Performance analysis of graph metrics for assessing hand motor imagery tasks from electroencephalography data : Análise de desempenho de métricas de grafos para reconhecimento de tarefas de imaginação motora das mãos a partir de dados de eletroencefalografia". [s.n.], 2016. http://repositorio.unicamp.br/jspui/handle/REPOSIP/305739.
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Orientadores: Gabriela Castellano, Romis Ribeiro de Faissol AttuxDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Física Gleb Wataghin
Made available in DSpace on 2018-09-06T19:40:58Z (GMT). No. of bitstreams: 1 StefanoFilho_CarlosAlberto_M.pdf: 6581881 bytes, checksum: fb23f8cb938a72e69a97b2bf2ff14cab (MD5) Previous issue date: 2016
Resumo: Interfaces cérebro-computador (BCIs, brain-computer interfaces) são sistemas cuja finalidade é fornecer um canal de comunicação direto entre o cérebro e um dispositivo externo, como um computador, uma prótese ou uma cadeira de rodas. Por não utilizarem as vias fisiológicas convencionais, BCIs podem constituir importantes tecnologias assistivas para pessoas que sofreram algum tipo de lesão e, por isso, tiveram sua interação com o ambiente externo comprometida. Os sinais cerebrais a serem extraídos para utilização nestes sistemas devem ser gerados mediante estratégias específicas. Nesta dissertação, trabalhamos com a estratégia de imaginação motora (MI, motor imagery), e extraímos a resposta cerebral correspondente a partir de dados de eletroencefalografia (EEG). Os objetivos do trabalho foram caracterizar as redes cerebrais funcionais oriundas das tarefas de MI das mãos e explorar a viabilidade de utilizar métricas da teoria de grafos para a classificação dos padrões mentais, gerados por esta estratégia, de usuários de um sistema BCI. Para isto, fez-se a hipótese de que as alterações no espectro de frequências dos sinais de eletroencefalografia devidas à MI das mãos deveria, de alguma forma, se refletir nos grafos construídos para representar as interações cerebrais corticais durante estas tarefas. Em termos de classificação, diferentes conjuntos de pares de eletrodos foram testados, assim como diferentes classificadores (análise de discriminantes lineares ¿ LDA, máquina de vetores de suporte ¿ SVM ¿ linear e polinomial). Os três classificadores testados tiveram desempenho similar na maioria dos casos. A taxa média de classificação para todos os voluntários considerando a melhor combinação de eletrodos e classificador foi de 78%, sendo que alguns voluntários tiveram taxas de acerto individuais de até 92%. Ainda assim, a metodologia empregada até o momento possui várias limitações, sendo a principal como encontrar os pares ótimos de eletrodos, que variam entre voluntários e aquisições; além do problema da realização online da análise
Abstract: Brain-computer interfaces (BCIs) are systems that aim to provide a direct communication channel between the brain and an external device, such as a computer, a prosthesis or a wheelchair. Since BCIs do not use the conventional physiological pathways, they can constitute important assistive technologies for people with lesions that compromised their interaction with the external environment. Brain signals to be extracted for these systems must be generated according to specific strategies. In this dissertation, we worked with the motor imagery (MI) strategy, and we extracted the corresponding cerebral response from electroencephalography (EEG) data. Our goals were to characterize the functional brain networks originating from hands¿ MI and investigate the feasibility of using metrics from graph theory for the classification of mental patterns, generated by this strategy, of BCI users. We hypothesized that frequency alterations in the EEG spectra due to MI should reflect themselves, in some manner, in the graphs representing cortical interactions during these tasks. For data classification, different sets of electrode pairs were tested, as well as different classifiers (linear discriminant analysis ¿ LDA, and both linear and polynomial support vector machines ¿ SVMs). All three classifiers tested performed similarly in most cases. The mean classification rate over subjects, considering the best electrode set and classifier, was 78%, while some subjects achieved individual hit rates of up to 92%. Still, the employed methodology has yet some limitations, being the main one how to find the optimum electrode pairs¿ sets, which vary among subjects and among acquisitions; in addition to the problem of performing an online analysis
Mestrado
Física
Mestre em Física
165742/2014-3
1423625/2014
CNPQ
CAPES
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SAIBENE, AURORA. "A Flexible Pipeline for Electroencephalographic Signal Processing and Management". Doctoral thesis, Università degli Studi di Milano-Bicocca, 2022. http://hdl.handle.net/10281/360550.
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L'elettroencefalogramma (EEG) fornisce registrazioni non-invasive delle attività e delle funzioni cerebrali sotto forma di serie temporali, a loro volta caratterizzate da una risoluzione temporale e spaziale (dipendente dai sensori), e da bande di frequenza specifiche per alcuni tipi di condizioni cerebrali.
Tuttavia, i segnali EEG risultanti sono non-stazionari, cambiano nel tempo e sono eterogenei, essendo prodotti da differenti soggetti e venendo influenzati da specifici paradigmi sperimentali, condizioni ambientali e dispositivi. Inoltre, questi segnali sono facilmente soggetti a rumore e possono venire acquisiti per un tempo limitato, fornendo un numero ristretto di condizioni cerebrali sulle quali poter lavorare.
Pertanto, in questa tesi viene proposta una pipeline flessibile per l'elaborazione e la gestione dei segnali EEG, affinchè possano essere più facilmente comprensibili e quindi più facilmente sfruttabili in diversi tipi di applicazioni. Inoltre, la pipeline flessibile proposta è divisa in quattro moduli riguardanti la pre-elaborazione del segnale, la sua normalizzazione, l'estrazione e la gestione di feature e la classificazione dei dati EEG.
La pre-elaborazione del segnale EEG sfrutta la multivariate empirical mode decomposition (MEMD) per scomporre il segnale nelle sue modalità oscillatorie, chiamate intrinsic mode function (IMF), ed usa un criterio basato sull'entropia per selezionare le IMF più relevanti. Queste IMF dovrebbero mantenere le naturali dinamiche cerebrali e rimuovere componenti non-informative. Le risultati IMF rilevanti sono in seguito sfruttate per sostituire il segnale o aumentare la numerosità dei dati.
Nonostante MEMD sia adatto alla non-stazionarietà del segnale EEG, ulteriori passi computazionali dovrebbero essere svolti per mitigare la caratteristica eterogeneità di questi dati. Pertanto, un passo di normalizzazione viene introdotto per ottenere dati comparabili per uno stesso soggetto o più soggetti e tra differenti condizioni sperimentali, quindi permettendo di estrarre feature nel dominio temporale, frequenziale e tempo-frequenziale per meglio caratterizzare il segnale EEG.
Nonostante l'uso di un insieme di feature differenti fornisca la possibilità di trovare nuovi pattern nei dati, può altresì presentare alcune ridondanze ed incrementare il rischio di incorrere nella curse of dimensionality o nell'overfitting durante la classificazione.
Pertanto, viene proposta una selezione delle feature basata sugli algoritmi evolutivi con un approccio completamente guidato dai dati. Inoltre, viene proposto l'utilizzo di modelli di apprendimento non o supervisionati e di nuovi criteri di stop per un algoritmo genetico modificato.
Oltretutto, l'uso di diversi modelli di apprendimento automatico può influenzare il riconoscimento di differenti condizioni cerebrali. L'introduzione di modelli di deep learning potrebbe fornire una strategia in grado di apprendere informazioni direttamente dai dati disponibili, senza ulteriori elaborazioni. Fornendo una formulazione dell'input appropriata, le informazioni temporali, frequenziali e spaziali caratterizzanti i dati EEG potrebbero essere mantenute, evitando l'introduzione di architetture troppo complesse.
Pertato, l'utilizzo di differenti processi ed approcci di elaborazione potrebbe fornire strategie più generiche o più legate a specifici esperimenti per gestire il segnale EEG, mantenendone le sue naturali caratteristiche.The electroencephalogram (EEG) provides the non-invasive recording of brain activities and functions as time-series, characterized by a temporal and spatial (sensor-dependent) resolution, and by brain condition-bounded frequency bands. Moreover, it presents some cost-effective device solutions. However, the resulting EEG signals are non-stationary, time-varying, and heterogeneous, being recorded from different subjects and being influenced by specific experimental paradigms, environmental conditions, and devices. Moreover, they are easily affected by noise and they can be recorded for a limited time, thus they provide a restricted number of brain conditions to work with. Therefore, in this thesis a flexible pipeline for signal processing and management is proposed to have a better understanding of the EEG signals and exploit them for a variety of applications. Moreover, the proposed flexible pipeline is divided in 4 modules concerning signal pre-processing, normalization, feature computation and management, and EEG data classification. The EEG signal pre-processing exploits the multivariate empirical mode decomposition (MEMD) to decompose the signal in oscillatory modes, called intrinsic mode functions (IMFs), and uses an entropy criterion to select the most relevant IMFs that should maintain the natural brain dynamics, while discarding uninformative components. The resulting relevant IMFs are then exploited for signal substitution and data augmentation. Even though MEMD is adapt to the EEG signal non-stationarity, further processing steps should be undertaken to mitigate these data heterogeneity. Therefore, a normalization step is introduced to obtain comparable data inter- and intra-subject and between different experimental conditions, allowing the extraction of general features in the time, frequency, and time-frequency domain for EEG signal characterization. Even though the use of a variety of feature types may provide new data patterns, they may also present some redundancies and increase the risk of incurring in classification problems like curse of dimensionality and overfitting. Therefore, a feature selection based on evolutionary algorithms is proposed to have a completely data-driven approach, exploiting both supervised and unsupervised learning models, and suggesting new stopping criteria for a modified genetic algorithm implementation. Moreover, the use of different learning models may affect the discrimination of different brain conditions. The introduction of deep learning models may provide a strategy to learn directly from the available data. By suggesting a proper input formulation it could be possible to maintain the EEG data time, frequency, and spatial information, while avoiding too complex architectures. Therefore, using different processing steps and approaches may provide general or experimental specific strategies to manage the EEG signal, while maintaining its natural characteristics.
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Goh, Kwang Leng Alex. "Study of Human Postural Control based on Electroencephalography Signals". Thesis, Curtin University, 2017. http://hdl.handle.net/20.500.11937/68367.
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Human movement requires adequate postural control. Stimulation of the sensory systems induces alterations in body sway. However, the role of cortical activity in maintaining balance remains unclear. The purpose of this research was to extend the understanding of cortical involvement in human postural control and provide direct and indirect cortical evidences from the visual system and postural demand. Ultimately, this research provides critical insight into the mechanisms of adaptive and maladaptive postural control.
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Young, Andrew Coady. "A Consensus Model for Electroencephalogram Data Via the S-Transform". Digital Commons @ East Tennessee State University, 2012. https://dc.etsu.edu/etd/1424.
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A consensus model combines statistical methods with signal processing to create a better picture of the family of related signals. In this thesis, we will consider 32 signals produced by a single electroencephalogram (EEG) recording session. The consensus model will be produced by using the S-Transform of the individual signals and then normalized to unit energy. A bootstrapping process is used to produce a consensus spectrum. This leads to the consensus model via the inverse S-Transform of the consensus spectrum. The method will be applied to both a control and experimental EEG to show how the results can be used in clinical settings to analyze experimental outcomes.
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Aspiras, Theus H. "Emotion Recognition using Spatiotemporal Analysis of Electroencephalographic Signals". University of Dayton / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1343992574.
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Kawaguchi, Hirokazu. "Signal Extraction and Noise Removal Methods for Multichannel Electroencephalographic Data". 京都大学 (Kyoto University), 2014. http://hdl.handle.net/2433/188593.
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Hajipour, Sardouie Sepideh. "Signal subspace identification for epileptic source localization from electroencephalographic data". Thesis, Rennes 1, 2014. http://www.theses.fr/2014REN1S185/document.
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Lorsque l'on enregistre l'activité cérébrale en électroencéphalographie (EEG) de surface, le signal d'intérêt est fréquemment bruité par des activités différentes provenant de différentes sources de bruit telles que l'activité musculaire. Le débruitage de l'EEG est donc une étape de pré-traitement important dans certaines applications, telles que la localisation de source. Dans cette thèse, nous proposons six méthodes permettant la suppression du bruit de signaux EEG dans le cas particulier des activités enregistrées chez les patients épileptiques soit en période intercritique (pointes) soit en période critique (décharges). Les deux premières méthodes, qui sont fondées sur la décomposition généralisée en valeurs propres (GEVD) et sur le débruitage par séparation de sources (DSS), sont utilisées pour débruiter des signaux EEG épileptiques intercritiques. Pour extraire l'information a priori requise par GEVD et DSS, nous proposons une série d'étapes de prétraitement, comprenant la détection de pointes, l'extraction du support des pointes et le regroupement des pointes impliquées dans chaque source d'intérêt. Deux autres méthodes, appelées Temps Fréquence (TF) -GEVD et TF-DSS, sont également proposées afin de débruiter les signaux EEG critiques. Dans ce cas on extrait la signature temps-fréquence de la décharge critique par la méthode d'analyse de corrélation canonique. Nous proposons également une méthode d'Analyse en Composantes Indépendantes (ICA), appelé JDICA, basée sur une stratégie d'optimisation de type Jacobi. De plus, nous proposons un nouvel algorithme direct de décomposition canonique polyadique (CP), appelé SSD-CP, pour calculer la décomposition CP de tableaux à valeurs complexes. L'algorithme proposé est basé sur la décomposition de Schur simultanée (SSD) de matrices particulières dérivées du tableau à traiter. Nous proposons également un nouvel algorithme pour calculer la SSD de plusieurs matrices à valeurs complexes. Les deux derniers algorithmes sont utilisés pour débruiter des données intercritiques et critiques. Nous évaluons la performance des méthodes proposées pour débruiter les signaux EEG (simulés ou réels) présentant des activités intercritiques et critiques épileptiques bruitées par des artéfacts musculaires. Dans le cas des données simulées, l'efficacité de chacune de ces méthodes est évaluée d'une part en calculant l'erreur quadratique moyenne normalisée entre les signaux originaux et débruités, et d'autre part en comparant les résultats de localisation de sources, obtenus à partir des signaux non bruités, bruités, et débruités. Pour les données intercritiques et critiques, nous présentons également quelques exemples sur données réelles enregistrées chez des patients souffrant d'épilepsie partielleIn the process of recording electrical activity of the brain, the signal of interest is usually contaminated with different activities arising from various sources of noise and artifact such as muscle activity. This renders denoising as an important preprocessing stage in some ElectroEncephaloGraphy (EEG) applications such as source localization. In this thesis, we propose six methods for noise cancelation of epileptic signals. The first two methods, which are based on Generalized EigenValue Decomposition (GEVD) and Denoising Source Separation (DSS) frameworks, are used to denoise interictal data. To extract a priori information required by GEVD and DSS, we propose a series of preprocessing stages including spike peak detection, extraction of exact time support of spikes and clustering of spikes involved in each source of interest. Two other methods, called Time Frequency (TF)-GEVD and TF-DSS, are also proposed in order to denoise ictal EEG signals for which the time-frequency signature is extracted using the Canonical Correlation Analysis method. We also propose a deflationary Independent Component Analysis (ICA) method, called JDICA, that is based on Jacobi-like iterations. Moreover, we propose a new direct algorithm, called SSD-CP, to compute the Canonical Polyadic (CP) decomposition of complex-valued multi-way arrays. The proposed algorithm is based on the Simultaneous Schur Decomposition (SSD) of particular matrices derived from the array to process. We also propose a new Jacobi-like algorithm to calculate the SSD of several complex-valued matrices. The last two algorithms are used to denoise both interictal and ictal data. We evaluate the performance of the proposed methods to denoise both simulated and real epileptic EEG data with interictal or ictal activity contaminated with muscular activity. In the case of simulated data, the effectiveness of the proposed algorithms is evaluated in terms of Relative Root Mean Square Error between the original noise-free signals and the denoised ones, number of required ops and the location of the original and denoised epileptic sources. For both interictal and ictal data, we present some examples on real data recorded in patients with a drug-resistant partial epilepsy
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Anandani, Vijay. "Autonomous vehicle control using electroencephalography signals extracted from NeuroSky MindWave device". Thesis, California State University, Long Beach, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10182137.
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The current project presents the hardware implementation and experimental testing of a system that uses electroencephalography (EEG) signals to control the motions of a vehicle through a brain-computer interface device. The user's brain activity is monitored continuously by the NeuroSky MindWave headset, and the EEG signals are processed and provided as inputs to the vehicle control system. The brain functions of interest are the user's attention level, meditation level and ocular blink rate. The values of these signals are transmitted to a microcontroller, which will command the vehicle's motor to initiate motion, stop, or change direction based on the user's brain activity. The current project can find a significant number of applications, since about 17% of the population have disabilities and one million people use wheelchairs, including manually and electrically powered chairs.
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Foldes, Stephen Thomas. "Command of a Virtual Neuroprosthesis-Arm with Noninvasive Field Potentials". Case Western Reserve University School of Graduate Studies / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1290109568.
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Isaac, Yoann. "Représentations redondantes pour les signaux d’électroencéphalographie". Thesis, Paris 11, 2015. http://www.theses.fr/2015PA112072/document.
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L’électroencéphalographie permet de mesurer l’activité du cerveau à partir des variations du champ électrique à la surface du crâne. Cette mesure est utilisée pour le diagnostic médical, la compréhension du fonctionnement du cerveau ou dans les systèmes d’interface cerveau-machine. De nombreux travaux se sont attachés au développement de méthodes d’analyse de ces signaux en vue d’en extraire différentes composantes d’intérêt, néanmoins leur traitement pose encore de nombreux problèmes. Cette thèse s’intéresse à la mise en place de méthodes permettant l’obtention de représentations redondantes pour ces signaux. Ces représentations se sont avérées particulièrement efficaces ces dernières années pour la description de nombreuses classes de signaux grâce à leur grande flexibilité. L’obtention de telles représentations pour les mesures EEG présente certaines difficultés du fait d’un faible rapport signal à bruit des composantes recherchées. Nous proposons dans cette thèse de les surmonter en guidant les méthodes considérées vers des représentations physiologiquement plausibles des signaux EEG à l’aide de régularisations. Ces dernières sont construites à partir de connaissances a priori sur les propriétés spatiales et temporelles de ces signaux. Pour chacune d’entre elles, des algorithmes sont proposés afin de résoudre les problèmes d’optimisation associés à l’obtention de ces représentations. L’évaluation des approches proposées sur des signaux EEG souligne l’efficacité des régularisations proposées et l’intérêt des représentations obtenuesThe electroencephalography measures the brain activity by recording variations of the electric field on the surface of the skull. This measurement is usefull in various applications like medical diagnosis, analysis of brain functionning or whithin brain-computer interfaces. Numerous studies have tried to develop methods for analyzing these signals in order to extract various components of interest, however, none of them allows to extract them with sufficient reliabilty. This thesis focuses on the development of approaches considering redundant (overcomoplete) representations for these signals. During the last years, these representations have been shown particularly efficient to describe various classes of signals due to their flexibility. Obtaining such representations for EEG presents some difficuties due to the low signal-to-noise ratio of these signals. We propose in this study to overcome them by guiding the methods considered to physiologically plausible representations thanks to well-suited regularizations. These regularizations are built from prior knowledge about the spatial and temporal properties of these signals. For each regularization, an algorithm is proposed to solve the optimization problem allowing to obtain the targeted representations. The evaluation of the proposed EEG signals approaches highlights their effectiveness in representing them
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Martínez, Cristina G. B. "Nonlinear signal analysis of micro and macro electroencephalographic recordings from epilepsy patients". Doctoral thesis, Universitat Pompeu Fabra, 2020. http://hdl.handle.net/10803/670397.
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The use of nonlinear signal analysis measures to characterize electroencephalographic (EEG) recordings can be key for a better understanding of the underlying brain dynamics. In neurological disorders such as epilepsy, these dynamics are altered as result of a disturbed coordination between neuronal populations. The aim of this thesis is to characterize the seizure-free interval of EEG recordings from epilepsy patients by means of nonlinear signal analysis techniques to investigate whether this type of analysis can contribute to the localization of the seizure onset zone, the brain region from which initial seizure discharges can be recorded. For this purpose, we used a
surrogate-corrected nonlinear predictability score and a surrogatecorrected nonlinear interdependence measure to analyze all-night EEG recordings from epilepsy patients implanted with hybrid depth electrodes equipped with macro contacts and micro wires. Our results show that the combined analysis of macro and micro EEG recordings may help to further increase the degree to which quantitative EEG analysis can contribute to the diagnostics in epilepsy
patients.El uso de medidas de análisis no lineales de señales para caracterizar registros electroencefalográficos (EEG) puede ser clave para una mejor comprensión de las dinámicas cerebrales subyacentes. En trastornos neurológicos como la epilepsia, estas dinámicas están alteradas a consecuencia de una coordinación perturbada entrepoblaciones neuronales. El objetivo de esta tesis es caracterizarel intervalo de registros de EEG libre de crisis epilépticas de pacientes con epilepsia mediante técnicas de análisis no lineales de señales para investigar si este tipo de análisis puede contribuir ala localización del SOZ (en inglés, Seizure onset zone), la región del cerebro donde se pueden registrar las descargas iniciales de las crisis epilépticas. Con este propósito, utilizamos una puntuación de predictibilidad no lineal corregida por sustitutos y una medida de interdependencia no lineal corregida por sustitutos para analizar registros EEG de pacientes con epilepsia grabados durante noches completas implantados con electrodos híbridos equipados con macro- y microcontactos. Nuestros resultados demuestran que el análisis combinado de macro- y micro-registros de EEG puede ayudar a aumentar el grado en el que el análisis cuantitativo de EEG puede contribuir al diagnóstico de pacientes con epilepsia.
L’ús de mesures d’anàlisi de senyals no lineals per la caracterització de registres encefalogràfics (EEG) pot ser clau per una millor comprensió de les dinàmiques cerebrals subjacents. En trastorns neurològics com l’epilèpsia, aquestes dinàmiques estan alterades a conseqüència d’una coordinació pertorbada entre poblacions neuronals. L’objectiu d’aquesta tesi doctoral és caracteritzar l’interval de registres EEG lliures de crisis epilèptiques en pacients amb epilèpsia mitjançant tècniques d’anàlisi de senyals no lineals, per tal d’investigar si aquest tipus d’anàlisi pot contribuir a la localització de la SOZ (en anglès, Seizure onset zone), la regió del cervell on es poden registrar les primeres descàrregues de la crisi. Amb aquesta finalitat, utilitzem una puntuació de previsibilitat no lineal corregida mitjançant substituts i una mesura d’interdependència no lineal corregida per substituts per analitzar registres EEG de pacients amb epilèpsia. Aquests han sigut enregistrats durant nits completes amb elèctrodes híbrids equipats amb macro- i microcontactes. Els resultats obtinguts demostren que l’anàlisi combinat de macro- i microregistres en l’EEG pot ajudar a augmentar el grau de contribució de l’anàlisi quantitatiu de l’EEG dins el diagnòstic de pacients amb epilèpsia.
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Rasheed, S. "RECOGNITION OF PRIMARY COLOURS IN ELECTROENCEPHALOGRAPH SIGNALS USING SUPPORT VECTOR MACHINES". Doctoral thesis, Università degli Studi di Milano, 2011. http://hdl.handle.net/2434/155486.
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In this study we have worked on the classification of EEG signals produced by the exposure of primary colours (RGB). The main goal of this study was to perform an offline analysis and classification of color information obtained from EEG signals recorded in response to individual RGB colours presentation in order to verify our hypothesis, if the observation of different colors can be detected or not by selecting different frequency bands. We have also performed an offline analysis of EEG signals produced by the colour imagination to observe similarities in EEG signals between actual color exposure and their corresponding imagination in order to find a Way-In to further establish our argument for developing future BCI applications that utilizes colour information from EEG signals unlike the Wadsworth and Graz noninvasive BCI applications that utilizes sensory motor rhythm. It was seen that it is possible to detect the information, not only of actual colour exposure but also the information of colours imagination, from EEG signals. It was also seen that the colour information obtained through the imagination of colours was similar to the actual colour exposure in some subjects. The experiment was designed in a way to expose the colours to the subjects in random order of presentation and also their corresponding imaginations. Different features are extracted and analyzed. The EEG signals have to be classified into Red, Green and Blue classes.
We have used Support Vector Machines with event-related spectral perturbation as features for the classification task using three different kernels, linear, polynomial and RBF which came out with the average classification accuracy of 84% with linear, 89% with polynomial and 97% with RBF kernel for real exposure of colors whereas for imagination of colors accuracy was 64%, 70% and 76% respectively. As an alternative, we have also performed extreme energy ratio (EER) and extreme energy difference (EED) criterions to extract energy features using only linear kernel with SVM. The classification was performed on three different groups of colors i.e. (Blue, Green), (Red, Green) and (Red, Blue). The accuracies found with both of EER and EED are (79%, 78% and 80%) and (82%, 83% and 84%) respectively for real exposure of colors and for imagination of colors are (72%, 70% and 73%) and (73%, 75% and 72%) respectively. EED performed better than EER.
Another experiment was performed with different shapes of colors and the EEG data was categorized as four different groups for classification. In group1, the classification accuracies for circle, square and triangle are found to be (88%, 52%, 94%), (84%, 47%, 89%) and (84%, 49%, 94%) respectively as triplet (linear, polynomial, RBF). In group 2, 3 and 4 classification accuracies achieved are (71%, 50%, 94%), (60%, 48%, 92%) and (57%, 29%, 94%) respectively as triplet of (linear, polynomial, RBF) kernels. After the successful classification of colour information from EEG signals we are planning to work for online classification in order to implement with any possible future Brain-Computer Interface applications. We believe that this study could further be extended to find out the possibilities for e.g. simulating a scenario of traffic light signals in virtual environment or to identify and explore any possibility of analyzing the EEG signals and developing BCI applications for color blind and/or blind people. Since such applications are quite novel in their fields of BCI therefore requires extensive collaborative research work in different domains.
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Ziani, Abdellatif. "Etude et réalisation d'un système d'analyse automatique du sommeil". Rouen, 1989. http://www.theses.fr/1989ROUES028.
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Le système d'enregistrement et de lecture Medilog 9000 permet d'étudier le sommeil à domicile. Ce travail a pour but d'effectuer une analyse automatique des signaux à la sortie de l'appareil. Le traitement du signal est effectué en deux étapes: d'abord extraire les paramètres les plus informationnels en utilisant une technique de reconnaissance de formes et, plus particulièrement, une reconnaissance syntaxique - puis la quantification automatique des stades de sommeil en utilisant la programmation par arbre de décision
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Semeráková, Nikola. "Detekce bdělosti mozku ze skalpového EEG záznamu za pomoci vyšších statistických metod". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-378032.
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Presented master's thesis deals with detection of brain wakefulness from scalp EEG data with higher order statistics. Part of the thesis is a description of electroencephalography, from the method of signal generation, sensing, electroencephraphy, EEG signal artifacts, frequency bands of EEG signal to its possible processing. Furthermore, the concept of mental fatigue and the possibility of its detection in the EEG signal is described. Subsequently, the principles of higher statistical methods of PCA and ICA and the specific possibilities of decomposition of EEG signal are described using these methods, from which the method of group spatial-frequency ICA was chosen as a suitable method for selection of partial oscillatory sources in EEG signal. In the next part there is described a method of acquisition of data, a the suggestion of solution with selected method and a description of the implemented algorithm, that was applied to real 256-lead scalp EEG data captured during a block task focused on subject allertnes. The absolute and relative power of the EEG signal was decomposed. From the achieved results, we observe that the fluctuations of the spatial frequency patterns of relative power (especially for theta and alpha bands) significantly more closely correspond with the change of reaction time and the error of the subjects performing the task. These observations appear to be relatively consistent with previously published literature, and the current study shows that spatial frequency ICA is able to blindly isolate space-frequency patterns whose fluctuations are statistically significantly correlated with parameters (reaction time, error rate) directly flowing from the given task.
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Nagabushan, Naresh. "Analyzing and Classifying Neural Dynamics from Intracranial Electroencephalography Signals in Brain-Computer Interface Applications". Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/90183.
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Brain-Computer Interfaces (BCIs) that rely on motor imagery currently allow subjects to control quad-copters, robotic arms, and computer cursors. Recent advancements have been made possible because of breakthroughs in fields such as electrical engineering, computer science, and neuroscience. Currently, most real-time BCIs use hand-crafted feature extractors, feature selectors, and classification algorithms. In this work, we explore the different classification algorithms currently used in electroencephalographic (EEG) signal classification and assess their performance on intracranial EEG (iEEG) data. We first discuss the motor imagery task employed using iEEG signals and find features that clearly distinguish between different classes. Second, we compare the different state-of-the-art classifiers used in EEG BCIs in terms of their error rate, computational requirements, and feature interpret-ability. Next, we show the effectiveness of these classifiers in iEEG BCIs and last, show that our new classification algorithm that is designed to use spatial, spectral, and temporal information reaches performance comparable to other state-of-the-art classifiers while also allowing increased feature interpret-ability.Master of Science
Brain-Computer Interfaces (BCIs) as the name suggests allows individuals to interact with computers using electrical activity captured from different regions of the brain. These devices have been shown to allows subjects to control a number of devices such as quad-copters, robotic arms, and computer cursors. Applications such as these obtain electrical signals from the brain using electrodes either placed non-invasively on the scalp (also known as an electroencephalographic signal, EEG) or invasively on the surface of the brain (Electrocorticographic signal, ECoG). Before a participant can effectively communicate with the computer, the computer is calibrated to recognize different signals by collecting data from the subject and learning to distinguish them using a classification algorithm. In this work, we were interested in analyzing the effectiveness of using signals obtained from deep brain structures by using electrodes place invasively (also known as intracranial EEG, iEEG). We collected iEEG data during a two hand movement task and manually analyzed the data to find regions of the brain that are most effective in allowing us to distinguish signals during movements. We later showed that this task could be automated by using classification algorithms that are borrowed from electroencephalographic (EEG) signal experiments.
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PISANO, BARBARA. "Machine Learning Techniques for Detection of Nocturnal Epileptic Seizures from Electroencephalographic Signals". Doctoral thesis, Università degli Studi di Cagliari, 2018. http://hdl.handle.net/11584/255953.
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Epilepsy is one of the major neurological disorders that affects more than 50 million people around the world; it is characterized by unpredictable seizures due to an abnormal electrical activity in the brain.
In this thesis nocturnal epilepsy has been investigated. In particular, Nocturnal Frontal Lobe Epilepsy (NFLE), that is a form of epilepsy in which seizures occur predominantly during sleep with symptoms including nocturnal awakenings, dystonic and tonic postures and clonic limb convulsions.
The electroencephalographic (EEG) signals, which record the electrical activity of the brain, are used by neurologists to diagnose epilepsy. However, in almost 50% of NFLE cases, the EEG does not show abnormality during seizures, making the neurologists work to identify the epileptic events very difficult, thereby requiring the support of video recording to verify the epileptic events, with a subsequent time-consuming procedure.
In literature few scientific contributions address the classification of nocturnal epileptic seizures. In this thesis, the automatic systems, both customized for single patient and generalized have been developed to find the best nocturnal epileptic seizure detection system from EEG signals. The combination of feature extraction and selection methods, associated to classification models based on Self Organizing Map (SOM), have been investigated following the classical machine learning approach.
The ability of SOM to represent data from a high-dimensional space in a low-dimensional space, preserving the topological properties of the original space, has been exploited to identify nocturnal epileptic seizures and track the temporal projection of the EEG signals on the map. The proposed methods allow the definition of maps capable of presenting meaningful information on the actual brain state, revealing the mapping potential of clustering data coming from seizure and non-seizure states.
The results obtained show that the patient-specific system achieves better performance than a patient-independent system. Moreover, comparing the performances with those of a binary classifier, widely used in epileptic seizure detection problems, the Support Vector Machine (SVM), the SOM model achieves good and, for some patients, higher performances.
In particular, the patient-customized system using SOM model, reaches an average value of sensitivity and specificity equal to 82.85% and 89.92%, respectively; whereas the SVM classifier achieved an average sensitivity and specificity equal to 82.11% and 82.85%, respectively, suggesting the use of SOM model as a good alternative for nocturnal epileptic seizure detection.
The discriminating power of SOM and the possibility to follow the temporal sequence of the EEG recordings on the map can provide information on an imminent epileptic seizure, highlighting the possibility to promote therapies aimed at rapid and targeted disarming the seizures.