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 obtenues. / The 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.

Les informations visuelles sont prépondérantes pour guider le comportement. Malgré une bonne connaissance du système visuel, un phénomène représentant une latéralisation de celui-ci, le phénomène de dominance oculaire (DO), reste mal compris et finalement peu étudié. Nos travaux de thèse ont permis de démontrer que cette DO exerce une large influence sur différentes étapes de la transformation des informations visuelles en mouvements manuels ou oculaires et que celle-ci s’exprime en interaction avec d’autres latéralisations du système nerveux telles que les latéralisations manuelle et des réseaux attentionnels. Nous montrons également son influence sur les processus de transfert d'information entre les deux hémisphères du cerveau. Enfin, nos travaux comportent des retombées cliniques directes : ils introduisent une méthode de quantification plus précise de la DO utilisable par les cliniciens pour une meilleure réussite de certaines techniques chirurgicales. En conclusion, nous montrons que la DO constitue un aspect important de la latéralisation du cerveau humain, relativement négligé jusqu’à présent. / Processing of visual information from the environment is preponderant for the successful performance of many motor behaviors. Despite a good knowledge of the visual system, a phenomenon corresponding to a lateralization of this system, called eye dominance (ED), remains not well understood and poorly studied. Our thesis demonstrated that ED has a widespread influence on different levels of the transformation of visual information into manual or ocular movements and interacts with other lateralizations of the central nervous system such as the manual and attentional networks. We also show the influence of ED on the process of information transfer between the two hemispheres of the brain. Finally, our work has direct clinical implications: it introduces a more accurate method of quantifying ED which is usable by clinicians for better success of some surgical techniques. In sum, we show that ED is an important aspect of the human brain lateralization which has been overlooked until now.

Although the mu rhythm is traditionally defined as a normal EEG variant, recent evidence suggests that mu may have functional significance in a variety of disorders such as autism, Parkinson's disease, and multiple sclerosis. While an increasing number of articles have focused on the blocking mechanism of mu in relation to various cognitive processes and disorders, few have examined the significance of a prominent mu rhythm in the background EEG. A few studies have examined the relationship between the mu rhythm and psychological disturbance, such as attentional and affective disorders. Increasing evidence suggests that EEG and qEEG variables may be useful in classifying psychiatric disorders, presenting a neurophysiological alternative to traditional symptom-based diagnosis and classification. Thus, the intention of the present study was to examine the relationship between neuropsychological variables, gathered from multiple assessment sources, and the presence of a prominent mu rhythm in the EEG. Results did not show a statistically significant difference between individuals with and without a prominent mu rhythm on the Test of Variables of Attention (TOVA); although individuals in the mu group showed a pattern of increased impulsivity and performance decrement over time. For adults, no significant differences were observed between groups on psychological variables measured by the Minnesota Multiphasic Personality Inventory-2 (MMPI-2). However, for children, the mu and control groups differed on several behavioral and emotional variables on the Child Behavior Checklist (CBCL). Results are examined in the context of other research and clinical implications are discussed.

Abuse and neglect occurring in childhood have been associated with a number of functional and physiological effects on the brain. This study extends previous research that investigated the quantitative electroencephalogram (qEEG) patterns in children with histories of relational trauma through the inclusion of additional participants and measures. As in previous studies, the relative power, absolute power, and coherence values in children with histories of abuse were compared to the Neuroguide database. Results did not show any significant differences in relative or absolute power in the theta range. Similarly, there were no significant coherence differences. Database comparisons were also made using low resolution electromagnetic tomography (LORETA) in order to determine which sub-cortical brain structures may be affected by abuse or trauma, though there were no significant differences in any frequency (0-30Hz). A review of the literature suggests that the prevalence of mu in normal adults and children ranges from 0 to 19%. The present study found a mu prevalence rate of 60.6% in the children who experienced abuse or neglect. Finally, comparisons were made between participants who demonstrate a mu pattern and those who do not to determine if this pattern is associated with certain behavioral and/or attention problems as assessed by the Child Behavior Checklist (CBCL) and the Tests of Variables of Attention (TOVA), respectively. There were no significant differences between children with a mu pattern versus children who did not exhibit a mu pattern on the Social Problems, Thought Problems, or Attention subscale scores on the CBCL or on the Commission subscale score on the TOVA.

Orientador: Marilisa Mantovani Guerreiro / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Ciencias Medicas / Made available in DSpace on 2018-08-08T00:49:32Z (GMT). No. of bitstreams: 1 Teixeira_KarineCoutoSarmento_M.pdf: 1612063 bytes, checksum: b67ceb8ce8a89e338f504be9b41ab015 (MD5) Previous issue date: 2006 / Resumo: Polimicrogiria é uma malformação da organização cortical que se caracteriza por múltiplos pequenos giros separados por espessos e rasos sulcos. O objetivo do presente estudo foi descrever as manifestações clínicas e eletroencefalográficas de pacientes com polimicrogiria, que têm epilepsia e/ou distúrbio específico de linguagem e dos familares dos pacientes com distúrbio específico de linguagem, correlacionando-os com a neuroimagem. Os pacientes foram submetidos a exame clínico e neurológico, com particular atenção aos sinais pseudobulbares, e realizaram eletroencefalograma de rotina com até 4 horas de duração. Quando possível, foram submetidos a vídeo-monitorização. Os dados de neuroimagem foram classificados em: polimicrogiria perisylviana (subdividida em holosylviana, parietal posterior bilateral, generalizada), polimicrogiria hemisférica e polimicrogiria frontal. Os achados eletroencefalográficos foram categorizados em: normal; anormal com atividade epileptiforme; anormal com atividade não epileptiforme; anormal com atividade epileptiforme e não epileptiforme; anormal com estado de mal elétrico (EME); anormal com atividade epileptiforme contínua e quanto à presença ou não de ativação da atividade epileptiforme pelo sono. Foram estudados 40 pacientes: 16 pacientes com polimicrogiria holosylviana, 14 com polimicrogiria parietal posterior, quatro com polimicrogiria generalizada, três com polimicrogiria hemisférica e três com polimicrogiria frontal. Observou-se nos pacientes com polimicrogiria holosylviana: sinais pseudobulbares em 11, hemiparesia em seis sendo um paciente com dupla hemiparesia. Três possuíam deficiência mental e cinco tinham epilepsia. O eletroencefalograma estava alterado em oito pacientes e atividade epileptiforme foi registrada em seis, sendo que em dois foi registrada atividade epileptiforme contínua focal. Entre os pacientes com polimicrogiria parietal posterior bilateral, cinco apresentavam sinais pseudobulbares e um possuía hemiparesia. Nenhum tinha alteração cognitiva ou epilepsia. Apenas um paciente apresentou ao eletroencefalograma atividade epileptiforme focal. Todos os pacientes com polimicrogiria generalizada tinham sinal pseudobulbar, deficiência mental e epilepsia. O quadro motor estava presente em três pacientes e apenas um deles não apresentava atividade epileptiforme no eletroencefalograma. Entre os pacientes com polimicrogiria hemisférica, todos apresentavam sinal pseudobulbar e hemiparesia, dois pacientes tinham deficiência mental e epilepsia, e um paciente atraso do desenvolvimento neuropsicomotor. Nos registros eletroencefalográficos, dois pacientes apresentaram EME focal. Os pacientes com polimicrogiria frontal não possuíam sinal pseudobulbar e em um único paciente foi detectada hemiparesia. Epilepsia estava presente em dois pacientes. No registro eletroencefalográfico, dois exames apresentaram anormalidades não-epileptiformes. Com os dados acima descritos foi possível observar que: os sinais pseudobulbares foram mais freqüentes em pacientes com idade menor ou igual a 15 anos; atividade epileptiforme e lentificação da atividade de base teve associação com epilepsia e alteração cognitiva e o distúrbio específico de linguagem apresentou uma relação inversa com estes achados eletroencefalográficos; a maioria dos nossos pacientes apresentou exame eletroencefalográfico normal; na polimicrogiria holosylviana, as atividades epileptiformes predominaram na região fronto-temporal e as atividades não epileptiformes predominaram na região fronto-centro-temporal; EME ocorreu em polimicrogiria hemisférica e atividade epileptiforme contínua focal ocorreu em polimicrogiria holosylviana bilateral assimétrica com provável displasia cortical associada; a ativação da atividade epileptiforme pelo sono foi um achado freqüente em nossa casuística; existe correlação direta entre as manifestações clínicas e anormalidades eletroencefalográficas e extensão do córtex polimicrogírico / Abstract: Polymicrogyria is a malformation of cortical organization that is characterized by multiple and small gyri. The aim of this study was do describe clinical and eletrographic features of patients with polymicrogyria, whom have epilepsia and/or developmental language disorder, and to correlate these data with neuroimaging findings. Our patients underwent clinical and neurological examination, and a routine electroencephalogram. Whenever possible, video-electroencephalographic monitoring was performed. Neuroimaging data were classified as: perisylvian polymicrogyria (subdivided as holosylvian, posterior parietal and generalized), hemispheric polymicrogyria and frontal polymicrogyria. Electrographic findings were classified as: normal; abnormal with epileptiform activity; abnormal with non-epileptiform activity; abnormal with epileptiform and non-epileptiform activities; abnormal with electrographic status (ES); abnormal with continuous epileptiform activity; sleep activation. We studied 40 patients: 16 with holosylvian polymicrogyria; 14 with posterior parietal polymicrogyria; four with generalized polymicrogyria; three with hemispheric polymicrogyria; and three with frontal polymicrogyria. Patients with holosylvian polymicrogyria showed: pseudobulbar signs in 11; hemiparesis in six one of them with double hemiparesis. Three had mental retardation and five had epilepsy. The elecgroencephalogram was abnormal in eight patients and epileptiform activity was registered in six, two of them with focal. Patients with posterior parietal polymicrogyria showed: five with pseudobulbar sign and one with hemiparesis. Cognitive delay and epilepsy was not found in this group. Only one patient had electroencephalogram with focal epileptiform discharges. Patients with generalized polymicrogyria had pseudobulbar sign, epilepsy and mental retardation. Motor deficit was found in three patients. Electroencephalogram findings showed epileptiform activity in three. Patients with hemispheric polymicrogyria had pseudobulbar sign and hemiparesis. Two of them had mental retardation and epilepsy. One had neurodevelopmental delay. Electrographic examinations showed focal ES in two patients. Patients with frontal polymicrogyria had no pseudobulbar sign and one of them had hemiparesis. Two patients had epilepsy. Electroencephalogram findings showed non-epileptiform activities in two patients. Our data demonstrated that: pseudobulbar sign are more frequent among patients under 15 years old; epilepsy and cognitive delay are both correlated with epileptiform activity and slowness of the background activity and developmental language disorder had a inverse correlation with this finds; in holosylvian polymicrogyria, epileptiform activities predominated in fronto-temporal regions and non-epileptiform activities predominated in centro-temporal regions; ES occurred in hemispheric polymicrogyria and focal continuous epileptiform activities in asymmetric bilateral holosylvian polymicrogyria with associated cortical dysplasia; sleep activation was a frequent finding; the severity of clinical and electrographic features correlated with the extent of cortical lesion / Mestrado / Neurologia / Mestre em Ciências Médicas

Orientador: Fernando Cendes / Dissertação (Mestrado) - Universidade Estadual de Campinas, Faculdade de Ciências Médicas / Made available in DSpace on 2018-08-22T01:34:33Z (GMT). No. of bitstreams: 1 Campos_BrunnoMachadode_M.pdf: 2725354 bytes, checksum: 1b7b94986b97dca79114bf54aaabe11b (MD5) Previous issue date: 2013 / Resumo: Introdução: A técnica multimodal de eletroencefalografia (EEG) acoplada à ressonância magnética (RM) funcional (RMf) apresenta características físicas complementares. Este método permite não só avaliar atividades neurais fisiológicas, mas também a dinâmica de neuropatologias como a epilepsia. Dentro do grupo das epilepsias, as epilepsias de lobo temporal (ELT) são particularmente importantes pela sua elevada prevalência e morbidade. Objetivo: Investigar e comparar os padrões de alterações hemodinâmicas associados a descargas epilépticas interictais (DEIs) em pacientes com ELT com (ELT-EH) ou sem (ELT-NL) sinais de esclerose hipocampal em exames de RM, através do uso combinado das técnicas de EEG e RMf (EEG-RMf). Métodos: Foram submetidos a exames de EEG-RMf, 25 pacientes com diagnóstico de ELT, sendo 12 ELT-NL e 13 ELT-EH. As imagens de RM foram adquiridas em aparelho de 3T e o EEG amostrado com 64 eletrodos compatíveis com RM. O tempo das DEIs foi utilizado para avaliar as respostas BOLD positivas (BOLDpos) e negativas (BOLDneg). Foram realizadas análises de EEG-RMf individuais e para grupos, além de análise estrutural, com o software SPM8-VBM8. As análises funcionais foram realizadas com pico da função resposta hemodinâmicas (FRH) em 0 segundo (precoce) e 5 segundos (tardio) após as DEIs. Resultados: Os mapas BOLDpos no grupo ELT-EH mostraram alterações hemodinâmicas precoces no lobo temporal ipsilateral, ínsula e giro precentral contralateral, e tardia no putâmem ipsilateral, cíngulo anterior bilateral, ínsula e lobos temporais. No grupo ELT-NL, BOLDpos precoce difuso foi observado, com alterações mais significativas no giro medial frontais ipsilateral, enquanto BOLDpos tardio foi observado na ínsula ipsilateral e giro temporal superior. Em ambos os grupos a análise estrutural mostrou redução significativa de substância cinzenta em áreas que se estendem além do lobo temporal, porém sem sobreposição significativa com áreas de BOLDpos. Em ambos os grupos de pacientes, BOLDneg foi observado em áreas compatíveis com default mode network (DMN). Interpretação: As redes funcionais relacionadas às DEIs diferem entre ELT-EH e ELT-NL. As regiões com atrofia mais significativa de substância cinzenta não coincidem com estas redes funcionais. Há possível supressão da atividade em áreas da DMN relacionadas com as DEIs em pacientes com ELT com ou sem sinais de EH / Abstract: Introduction: The multimodal technique of electroencephalography (EEG) coupled to functional magnetic resonance imaging (fMRI) presents additional physical characteristics. This method allows not only evaluating physiological neural activities, but also the dynamics of neuropathologies as epilepsy. Within the group of epilepsy, temporal lobe epilepsy (TLE) is particularly important due to its high prevalence and morbidity. Objective: To investigate and compare the patterns of hemodynamic changes associated with interictal epileptiform discharges (IEDs) in patients with TLE with (TLE-HS) or without (TLE-NL) signs of hippocampal sclerosis in MRI, through the combined use of EEG and fMRI techniques (EEG-fMRI). Methods: Twenty five patients diagnosed with TLE underwent EEG-fMRI scans, 12 with TLE-NL and 13 TLE-HS. MR images were acquired on a 3T scanner and EEG recorded with 64 electrodes compatible with MRI. The time for IED was used to assess the BOLD positive (BOLDpos) and negative (BOLDneg) responses. Analysis of EEG-fMRI MRI structural analyses were performed with SPM8-VBM8 software. The functional analyses were performed with the peak of the hemodynamic response function (HRF) in 0 second (early) and 5 seconds (late) after IED. Results: The BOLDpos maps in TLE-HS group showed early hemodynamic changes in ipsilateral temporal lobe, contralateral insula and precentral gyrus and late hemodynamic changes in ipsilateral putamen, bilateral anterior cingulate, insula and temporal lobes. In TLE-NL, diffuse early BOLDpos was observed, with the most significant changes in the ipsilateral medial frontal gyrus, while late BOLDpos was observed in the ipsilateral insula and superior temporal gyrus. In both groups the structural analysis showed significant reduction of gray matter in areas that extend beyond the temporal lobe, but with no significant overlap with areas of BOLDpos. In both groups of patients BOLDneg was observed in areas consistent with default mode network (DMN). Interpretation: The functional networks related to the IED differ in TLE-HS and TLE-NL. The regions with the most significant gray matter atrophy do not coincide with these functional networks. There is possible suppression of activity in the DMN areas related to IED in TLE patients with or without signs of HS / Mestrado / Fisiopatologia Médica / Mestre em Ciências

abstract: Aortic aneurysms and dissections are life threatening conditions addressed by replacing damaged sections of the aorta. Blood circulation must be halted to facilitate repairs. Ischemia places the body, especially the brain, at risk of damage. Deep hypothermia circulatory arrest (DHCA) is employed to protect patients and provide time for surgeons to complete repairs on the basis that reducing body temperature suppresses the metabolic rate. Supplementary surgical techniques can be employed to reinforce the brain's protection and increase the duration circulation can be suspended. Even then, protection is not completely guaranteed though. A medical condition that can arise early in recovery is postoperative delirium, which is correlated with poor long term outcome. This study develops a methodology to intraoperatively monitor neurophysiology through electroencephalography (EEG) and anticipate postoperative delirium. The earliest opportunity to detect occurrences of complications through EEG is immediately following DHCA during warming. The first observable electrophysiological activity after being completely suppressed is a phenomenon known as burst suppression, which is related to the brain's metabolic state and recovery of nominal neurological function. A metric termed burst suppression duty cycle (BSDC) is developed to characterize the changing electrophysiological dynamics. Predictions of postoperative delirium incidences are made by identifying deviations in the way these dynamics evolve. Sixteen cases are examined in this study. Accurate predictions can be made, where on average 89.74% of cases are correctly classified when burst suppression concludes and 78.10% when burst suppression begins. The best case receiver operating characteristic curve has an area under its convex hull of 0.8988, whereas the worst case area under the hull is 0.7889. These results demonstrate the feasibility of monitoring BSDC to anticipate postoperative delirium during burst suppression. They also motivate a further analysis on identifying footprints of causal mechanisms of neural injury within BSDC. Being able to raise warning signs of postoperative delirium early provides an opportunity to intervene and potentially avert neurological complications. Doing so would improve the success rate and quality of life after surgery. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2020

The theme of this dissertation focuses on developing statistical models to learn progression dynamics and mechanisms of neuropsychiatric disorders using data from various domains. Due to limited knowledge about the underlying pathological processes in neurological disorders, it remains a challenge to establish reliable diagnostic criteria and predict disease prognosis in the presence of substantial phenotypic heterogeneity. As a result, current diagnosis and treatment of neurological disorders often rely on late-stage clinical symptoms, which poses barriers for developing effective interventions at the premanifest stage. It is crucial to characterize the temporal disease progression course and study the underlying mechanisms using clinical assessments, blood biomarkers, and neuroimaging biomarkers to evaluate disease stages, identify markers that are useful for early clinical diagnosis, compare or monitor treatment effects and accelerate drug discovery. We propose three projects to tackle challenges in leveraging multi-domain biomarkers and clinical symptoms to learn disease dynamics and progression of neurological disorders: (1) A nonlinear mixture model with subject-specific random inflection points to jointly fit multiple longitudinal markers and estimate marker progression trajectories in a single modality; (2) A multi-layer exponential family factor model integrating multi-domain data to learn lower-dimensional latent space of disease impairment and fully map disease risk and progression; (3) A latent state space model that jointly analyzes multi-channel EEG signals and learns dynamics of different sources corresponding to brain cortical activities. In addition, motivated by the ongoing COVID-19 pandemic, we propose a parsimonious survival-convolution model to predict daily new cases and estimate the time-varying reproduction numbers to evaluate effects of mitigation strategies. In the first project, we propose a nonlinear mixture model with random time shifts to jointly estimate long-term progression trajectories using multivariate discrete longitudinal outcomes. The model can identify early disease markers, their orders of occurrence, and the rates of impairment. Specifically, a latent binary variable representing disease susceptibility status incorporates subject covariates (e.g., biological measures) in the mixture model to capture between-subject heterogeneity. Measures of disease impairment for susceptible patients are modeled jointly under the exponential family framework. Our model allows for subject-specific and marker-specific inflection points associated with patients' characteristics (e.g., genetic mutation) to indicate a critical time when the fastest degeneration occurs. Furthermore, it uses subject-specific latent scores shared among markers to improve efficiency. The model is estimated using an EM algorithm. Extensive simulation studies are conducted to demonstrate validity of the proposed method and algorithm. Lastly, we apply our method to the Parkinson's Progression Markers Initiative (PPMI), and show utility to identify early disease signs and compare clinical symptomatology for the genetic form of Parkinson's Disease (PD) and idiopathic PD. In the second project, we tackle challenges to leverage multi-domain markers to learn early disease progression of neurological disorders. We propose to integrate heterogeneous types of measures from multiple domains (e.g., discrete clinical symptoms, ordinal cognitive markers, continuous neuroimaging and blood biomarkers) using a hierarchical Multi-layer Exponential Family Factor (MEFF) model, where the observations follow exponential family distributions with lower-dimensional latent factors. The latent factors are decomposed into shared factors across multiple domains and domain-specific factors, where the shared factors provide robust information to perform behavioral phenotyping and partition patients into clinically meaningful and biologically homogeneous subgroups. Domain-specific factors capture the remaining unique variations for each domain. The MEFF model also captures the nonlinear trajectory of disease progression and order critical events of neurodegeneration measured by each marker. To overcome computational challenges, we fit our model by approximate inference techniques for large-scale data. We apply the developed method to Parkinson's Progression Markers Initiative (PPMI) data to integrate biological, clinical and cognitive markers arising from heterogeneous distributions. The model learns lower-dimensional representations of Parkinson's disease and the temporal ordering of the neurodegeneration of PD. In the third project, we propose methods that can be used to analyze multi-channel electroencephalogram (EEG) signals intensively measured at a high temporal resolution. Modern neuroimaging technologies have substantially advanced the measurement of brain activities. EEG as a non-invasive neuroimaging technique measures changes in electrical voltage on the scalp induced by cortical activities. With its high temporal resolution, EEG has emerged as an increasingly useful tool to study brain connectivity. Challenges with modeling EEG signals of complex brain activities include interactions among unknown sources, low signal-to-noise ratio and substantial between-subject heterogeneity. In this work, we propose a state space model that jointly analyzes multi-channel EEG signals and learns dynamics of different sources corresponding to brain cortical activities. Our model borrows strength from spatially correlated measurements and uses low-dimensional latent sources to explain all observed channels. The model can account for patient heterogeneity and quantify the effect of a subject's covariates on the latent space. The EM algorithm, Kalman filtering, and bootstrap resampling are used to fit the state space model and provide comparisons between patient diagnostic groups. We apply the developed approach to a case-control study of alcoholism and reveal significant attenuation of brain activities in response to visual stimuli in alcoholic subjects compared to healthy controls. Lastly, motivated by the ongoing COVID-19 pandemic, we propose a robust and parsimonious survival-convolution model aiming to predict COVID-19 disease course and compare effectiveness of mitigation measures across countries to inform policy decision making. We account for transmission during a pre-symptomatic incubation period and use a time-varying effective reproduction number to reflect the temporal trend of transmission and change in response to a public health intervention. We estimate the intervention effect on reducing the infection rate using a natural experiment design and quantify uncertainty by permutation. In China and South Korea, we predicted the entire disease epidemic using only early phase data (two to three weeks after the outbreak). A fast rate of decline in reproduction number was observed and adopting mitigation strategies early in the epidemic was effective in reducing the infection rate in these two countries. The nationwide lockdown in Italy did not accelerate the speed at which the infection rate decreases. In the United States, the reproduction number significantly decreased during a 2-week period after the declaration of national emergency, but declines at a much slower rate afterwards. If the trend continues after May 1, COVID-19 may be controlled by late July. However, a loss of temporal effect (e.g., due to relaxing mitigation measures after May 1) could lead to a long delay in controlling the epidemic.

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.

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.