Neuroimagiologia funcional é uma área da neurociência que visa o desenvolvimento de diversas técnicas para mapear a atividade do sistema nervoso e esteve sob constante desenvolvimento durante as últimas décadas devido à sua grande importância para aplicações clínicas e pesquisa. Técnicas usualmente utilizadas, como imagem por ressonância magnética functional (fMRI) e tomografia por emissão de pósitrons (PET) têm ótima resolução espacial (~ mm), mas uma resolução temporal limitada (~ s), impondo um grande desafio para nossa compreensão a respeito da dinâmica de funções cognitivas mais elevadas, cujas oscilações podem ocorrer em escalas temporais muito mais finas (~ ms). Tal limitação ocorre pelo fato destas técnicas medirem respostas biológicas lentas que são correlacionadas de maneira indireta com a atividade elétrica cerebral. As duas principais técnicas capazes de superar essa limitação são a Eletro- e Magnetoencefalografia (EEG/MEG), que são técnicas não invasivas para medir os campos elétricos e magnéticos no escalpo, respectivamente, gerados pelas fontes elétricas cerebrais. Ambas possuem resolução temporal na ordem de milisegundo, mas tipicalmente uma baixa resolução espacial (~ cm) devido à natureza mal posta do problema inverso eletromagnético. Um imenso esforço vem sendo feito durante as últimas décadas para melhorar suas resoluções espaciais através da incorporação de informação relevante ao problema de outras técnicas de imagens e/ou de vínculos biologicamente inspirados aliados ao desenvolvimento de métodos matemáticos e algoritmos sofisticados. Neste trabalho focaremos em EEG, embora todas técnicas aqui apresentadas possam ser igualmente aplicadas ao MEG devido às suas formas matemáticas idênticas. Em particular, nós exploramos esparsidade como uma importante restrição matemática dentro de uma abordagem Bayesiana chamada Aprendizagem Bayesiana Esparsa (SBL), que permite a obtenção de soluções únicas significativas no problema de reconstrução de fontes. Além disso, investigamos como incorporar diferentes estruturas como graus de liberdade nesta abordagem, que é uma aplicação de esparsidade estruturada e mostramos que é um caminho promisor para melhorar a precisão de reconstrução de fontes em métodos de imagens eletromagnéticos. / Functional Neuroimaging is an area of neuroscience which aims at developing several techniques to map the activity of the nervous system and has been under constant development in the last decades due to its high importance in clinical applications and research. Common applied techniques such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) have great spatial resolution (~ mm), but a limited temporal resolution (~ s), which poses a great challenge on our understanding of the dynamics of higher cognitive functions, whose oscillations can occur in much finer temporal scales (~ ms). Such limitation occurs because these techniques rely on measurements of slow biological responses which are correlated in a complicated manner to the actual electric activity. The two major candidates that overcome this shortcoming are Electro- and Magnetoencephalography (EEG/MEG), which are non-invasive techniques that measure the electric and magnetic fields on the scalp, respectively, generated by the electrical brain sources. Both have millisecond temporal resolution, but typically low spatial resolution (~ cm) due to the highly ill-posed nature of the electromagnetic inverse problem. There has been a huge effort in the last decades to improve their spatial resolution by means of incorporating relevant information to the problem from either other imaging modalities and/or biologically inspired constraints allied with the development of sophisticated mathematical methods and algorithms. In this work we focus on EEG, although all techniques here presented can be equally applied to MEG because of their identical mathematical form. In particular, we explore sparsity as a useful mathematical constraint in a Bayesian framework called Sparse Bayesian Learning (SBL), which enables the achievement of meaningful unique solutions in the source reconstruction problem. Moreover, we investigate how to incorporate different structures as degrees of freedom into this framework, which is an application of structured sparsity and show that it is a promising way to improve the source reconstruction accuracy of electromagnetic imaging methods.

<p>Emotional regulation is how people manage their emotions especially anxiety, anger, and frustration, which are all negative emotions. It is critical to health, academic achievement, and work performance to have proper emotion regulation skills. In order to facilitate participants to manage emotions, we developed a series of training programs by using HTC^© Vive^TM headset and Neuracle. The HTC Vive is to improve immersion in presence to lead to more effective training, and the Neuracle is using Electroencephalography (EEG) techniques for reading user’s brainwave signals which provide real time input for the training programs. We focused on analyzing if emotion, which was reflected in brainwave signals, had changes when participants were exposed to positive/negative stimuli. The testing results indicated that there were noticeable changes in brainwave signals to stimuli. The findings from the testing provide a solid foundation to use brainwave signals as real-time input in our game development for improving emotion regulation skills in the future. </p>

La stimulation cérébrale profonde (SCP) est un outil thérapeutique pour le traitement chronique des symptômes de nombreuses maladies, notamment les troubles moteurs et les maladies psychiatriques réfractaires. Cependant, ses mécanismes d’action sont encore peu connus, notamment en ce qui concerne les effets à large échelle sur les réseaux fonctionnels cérébraux. Dans ce travail de thèse, nous avons développé l’imagerie par électroencéphalographie des réseaux corticaux modulés par la SCP au travers de trois études sur des pathologies différentes :• une étude sur les réseaux de l’inhibition motrice chez 12 patients ayant des troubles obsessionnels compulsifs, traités par stimulation de la partie associativo-limbique du noyau subthalamique (NST),• une étude sur les réseaux du contrôle des interférences émotionnelles chez 5 patients dépressifs sévères, traités par stimulation du cortex cingulaire subgénual,• une étude sur le contrôle des interférences émotionnelles chez 16 patients parkinsoniens, traités par stimulation de la partie motrice du NST et comparés à 16 sujets sains.Nous avons mis en place une méthodologie EEG commune reposant sur la correction des artefacts de stimulation, le calcul des potentiels évoqués cognitifs en condition de stimulation ON et OFF, la localisation de sources et la modélisation causale dynamique permettant d’étudier la connectivité des réseaux corticaux-sous-corticaux. La première étude nous a montré que la SCP du NST diminue les sorties efférentes des ganglions de la base vers le cortex frontal inférieur droit qui est une zone centrale de l’inhibition, concomitante d’une altération des performances à l’inhibition. La deuxième étude a déterminé que l’un des corrélats neuronaux de l’amélioration clinique par la SCP de la dépression serait une diminution de l’influence limbique sur le système visuel ventral (hyper-actifs chez ces patients). Cet effet était accompagné d’une augmentation par la SCP des marqueurs cardiaques de l’activité vagale (sous active chez ces patients à risques). Enfin, la troisième étude a mis en évidence un effet différent et opposé des traitements dopaminergiques et de la SCP du NST sur le contrôle de l’interférence émotionnelle chez les patients parkinsoniens. Ces trois études ont donc montré que l’utilisation de la reconstruction de sources et de la MDC en EEG permet de mettre en évidence les effets de la SCP sur le cerveau et de mieux comprendre comment ce traitement modifie les réseaux neuronaux fonctionnels. / Deep Brain Stimulation (DBS) is a chronic clinical tool used to treat symptoms from several diseases, as motor disorders and refractory psychiatric diseases. However, its mechanism are still not well known, especially its large scale effects on brain functional networks. In this PhD, we have developed electroencephalographic imaging of cortical networks modulated by DBS through three studies on different diseases:• the first study on motor inhibition networks in 12 patients with obsessive compulsive disorders, treated with DBS of the associativo-limbic region of the subthalamic nucleus (STN),• the second study on emotional interferences control in 5 severe depressive patients, treated by DBS of the subgenual cingulate cortex,• the last study on emotional interferences control in 16 parkinson’s patients, treated by DBS of the motor region of the STN and compared with 16 healthy subjects.We have used the same EEG methods based on artefact (from stimulation) correction, computation of the cognitive evoked potentials in ON and OFF DBS conditions, source localization and dynamic causal modeling to study cortical-subcortical networks. The first study has shown that STN DBS decreases efferent outputs from the basal ganglia to the right inferior frontal cortex which is a key node of inhibition, simultaneously with an alteration of inhibition performance. The second study has shown that one of the correlates of clinical improvement with DBS in depression would be a decrease of the limbic influence on the ventral visual system (hyperactive in those patients). This effect was concomitant with an increase of vagal activity cardiac indexes with DBS (hypoactive in those patients at risk). Finally, the last study has shown that dopaminergic treatment and STN DBS have different and opposed effects on the emotional interference control in parkinsonian patients. Therefore, these three studies have shown that EEG source reconstruction and DCM are efficient to study DBS effect on the brain and open a way to better understand how DBS modulates neural functional networks.

A literatura tem mostrado que as deteriorações proprioceptivas relacionadas ao envelhecimento podem ter consequências funcionais graves na realização de tarefas sensório-motoras como postura e marcha. A avaliação do limiar de percepção cinestésica é uma forma de avaliação da acuidade proprioceptiva e representa uma tarefa sensório-motora, com envolvimento de receptores e vias periféricas bem como de níveis superiores do sistema nervoso central. O presente estudo teve como objetivo avançar no método de quantificação proprioceptiva do tornozelo em jovens e idosos e foi dividida em dois capítulos. O primeiro capítulo investigou aspectos eletrofisiológicos corticais associados à percepção cinestésica do tornozelo. O protocolo experimental consistiu de aquisição de sinais eletrencefalográficos durante a avaliação do tempo de resposta à percepção de movimento passivo de tornozelo em velocidades baixa (0,5º/s) e alta (22º/s). Foram realizadas análises de potencial relacionado a evento (ERP do inglês Event-Related Potential) e de dessincronização e sincronização relacionados a evento (ERD/ERS do inglês Event-Related Desynchronization/Synchronization) na faixa beta (14 a 37 Hz). Os resultados mostraram atrasos nos tempos de resposta à percepção cinestésica dos idosos correlacionados aos atrasos de ativação cortical. O componente inicial do ERP (N1) foi menor e mais tardio em idosos e pode indicar uma chegada de influxo aferente proprioceptivo atrasada e de menor magnitude ao córtex. Os idosos também apresentaram maior ativação cerebral (maior ERD), o que pode representar um maior esforço cognitivo para processar as informações proprioceptivas. Além disso, após finalizada a tarefa sensório-motora na condição de velocidade alta de movimentação passiva, a inibição cortical (ERS) esteve atenuada em idosos em comparação aos jovens. Na condição de velocidade baixa, ERS foi observada em idosos, mas não em jovens, o que indica diferenças em níveis corticais entre os grupos etários na preparação para o movimento seguinte. O segundo capítulo deste trabalho foi motivado por estudos que mostraram que níveis ótimos de ruído elétrico podem melhorar a detecção e transmissão de sinais neurais, melhorando o desempenho de tarefas sensório-motoras. O presente estudo investigou se a aplicação de estímulo elétrico (EE) na região posterior das pernas melhora a percepção cinestésica e o controle postural de jovens e idosos. O limiar cinestésico foi avaliado pelo tempo de resposta à percepção de movimento passivo de tornozelo na velocidade de 0,5º/s. O controle postural foi avaliado durante a manutenção da postura ereta em três condições: olhos fechados, olhos abertos com movimentação periódica ou não-periódica do cenário visual (paradigma da sala móvel). Os resultados indicaram que a aplicação de EE na perna levou a uma redução do tempo de resposta à percepção cinestésica em adultos jovens e idosos. A amplitude de oscilação corporal também foi reduzida em ambos os grupos etários com a aplicação de EE, porém somente na condição de movimentação não-periódica da sala móvel. A partir destes resultados, conclui-se que a aplicação de EE promove melhoras no desempenho sensório-motor, que estão possivelmente relacionadas com uma melhor sinalização de receptores periféricos. Estes achados podem ter implicações clínicas importantes para indivíduos idosos, cujas alterações proprioceptivas e posturais os tornam mais suscetíveis a quedas / The literature has shown that age-related proprioceptive impairments may have serious functional consequences in performing sensorimotor tasks such as posture and gait. The evaluation of threshold for kinesthetic perception is a way to assess the proprioceptive accuracy and represents a sensorimotor task involving receptors and peripheral pathways as well as processing in upper levels of the central nervous system. The present study aimed to advance the method for quantifying ankle joint proprioception in older and young adults, and was divided into two chapters. The first chapter investigated the cortical electrophysiological aspects of evaluation of the threshold for ankle kinesthetic perception. The experimental protocol consisted of acquisition of electroencephalographic signals during the evaluation of the response time to the perception of passive ankle movement at low (0.5 °/s) and high (22°/s) velocities. Event-related potentials (ERP) and event-related desynchronization and synchronization (ERD/ERS) in the beta band (14-37 Hz) were analyzed. The results showed delayed response times to the kinesthetic perception in older adults with correlated delay in cortical activation. The initial ERP component (N1) had lower amplitude and was delayed in the older group and may indicate delayed and deficient arrival of afferent inputs to the cortex. Older adults also showed larger cerebral activation (larger ERD), which may represent higher cognitive efforts to process the proprioceptive information. In addition, after completing the sensorimotor task in the high velocity condition of passive foot movement, cortical inhibition (ERS) was attenuated in older adults when compared to young adults. In the low velocity condition, ERS was observed in older but not in young adults, which indicates differences at cortical levels between age groups during the preparation for the next movement. The second chapter of this study was motivated by results from studies that have shown that optimal levels of electrical noise can enhance the detection and transmission of neural signals, thereby improving the performance of sensorimotor tasks. The present study investigated whether the application of electrical stimulation (ES) in the posterior region of the legs improves kinesthetic perception and postural control in young and elderly. The kinesthetic threshold was assessed by the response time to the perception of passive ankle movement at 0.5º/s. Postural control was assessed during the upright stance in three conditions: eyes closed and with periodic and non-periodic movement of the visual scenario by means of a moving room paradigm. The results indicated ES applied over the legs led to a reduction in the response time to kinesthetic perception in young and older adults. The body sway amplitude was also reduced in both age groups with application of ES, but only in the condition with non-periodic room movement. From these results, it is concluded that the application of ES promotes improvements in sensorimotor performance and it is possibly related to improvements of receptor signaling. These findings may have important clinical implications for older adults, whose proprioceptive and postural changes make them more susceptible to falls

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-supervisée. / A 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.

Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2017-05-09T13:37:10Z No. of bitstreams: 1 Ricardo Augusto Perera_.pdf: 6032138 bytes, checksum: a3f62ff5aeb1298ea332f8079b47bee9 (MD5) / Made available in DSpace on 2017-05-09T13:37:10Z (GMT). No. of bitstreams: 1 Ricardo Augusto Perera_.pdf: 6032138 bytes, checksum: a3f62ff5aeb1298ea332f8079b47bee9 (MD5) Previous issue date: 2017-03-03 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Utilizando o framework teórico denominado Predictive Processing (Clark, 2013), que assume que cérebros são essencialmente máquinas que predizem seus próximos inputs e o fazem minimizando o erro de predição (diferença entre o input previsto e o recebido) de modo semelhante a uma inferência bayesiana, procurou-se, nesta dissertação, encontrar padrões de atividade eletrofisiológica que sinalizassem o recrutamento de crenças epistêmicas. Conjecturou-se que enquanto frases são lidas, representações de estados de coisas e eventos são mentalmente modeladas de modo gradual e preditivo, e que os estímulos vindouros são antecipados com base naquilo que se afigura “o mais provável” em termos epistêmicos, semânticos, sintáticos, léxicos e perceptuais. Devido ao fato de já serem conhecidas as respostas eletrofisiológicas que são moduladas por violações de expectativa dos níveis semântico, sintático, léxico e perceptual, assim como o preciso intervalo temporal em que se manifestam, foi possível estudar como a expectativa gerada por crenças modula os sinais cerebrais. Os experimentos realizados no Laboratório de Filosofia Experimental e Estudos da Cognição, localizado na UNISINOS, apresentavam frases em um monitor (palavra por palavra) que aludiam a fatos conhecidos apenas por um grupo de participantes (filósofos), estruturadas de modo que apenas uma única palavra, aparecendo no final de cada sentença, fosse capaz de as tornar verdadeiras (e.g. “Teeteto é um diálogo escrito por Platão”). O modelo proposto assume que, antes de aparecer a palavra final, o significado dos termos é acessado (no caso exemplificado, um texto específico e uma relação), um estado de coisas representado (i.e. existir um x tal que x escreveu Teeteto), e uma busca iniciada pelo melhor candidato a x, tendo as crenças do sujeito como alvo do rastreio. A crença relevante é então recrutada e a representação do x identificado (Platão) acessada. Informações sobre a melhor predição semântica (o filósofo Platão) são utilizadas para selecionar o mais provável item léxico que tenha Platão como significado. Os participantes aos quais eram atribuídas crenças filosóficas apresentaram Event-Related Potentials correlacionados a processamento léxico-semântico significativamente diferentes dos participantes do grupo dos não-filósofos. Considerando que a única diferença relevante entre os dois grupos era a conjecturada posse ou ausência de determinadas crenças, os resultados foram interpretados como sinalizando o recrutamento de crenças em processos preditivos subjacentes à compreensão textual. Os resultados contrariam posições eliminativistas que consideram que o vocabulário mentalista acerca de crenças, intenções e desejos carece de significado, uma vez que a diferença encontrada sugere que de fato há algo em cérebros que é denotado por meio dessas expressões (ainda que de modo vago e grosseiro) e que está a modular os sinais. Ainda que não compreendamos prontamente o exato modo como se efetiva a instanciação de crenças por cérebros, podemos estudar o modo como elas são recrutadas e integram diversos processos cognitivos (i.e. seus papéis causais). / Using the theoretical framework called Predictive Processing (Clark, 2013), which assumes that brains are essentially machines that predict their next inputs and do so by minimizing prediction error (difference between predicted and received input) similar to a Bayesian inference, it was sought, in this dissertation, to find patterns of electrophysiological activity that signal the recruitment of epistemic beliefs. It has been conjectured that as sentences are read, representations of states of affairs and events are mentally modeled in a gradual and predictive fashion, and that the upcoming stimuli are anticipated on the basis of what appears to be "most likely" in epistemic, semantic, syntactic, lexical and perceptual terms. Due to the fact that the electrophysiological responses that are modulated by violations of expectation of the semantic, syntactic, lexical and perceptual levels are known, as well as the precise time interval in which they are manifested, it was possible to study how the expectation generated by beliefs modulates the cerebral signals. The experiments carried out in the Laboratory of Experimental Philosophy and Studies of Cognition, located at UNISINOS, presented sentences on a monitor (word by word) that alluded to facts known only by a group of participants (philosophers), structured in a way that only a single word, appearing at the end of each sentence, would be able to make them true (e.g. "Theaetetus is a dialogue written by Plato"). The proposed model assumes that, before the final word appears, the meaning of the terms is accessed (in the exemplified case, a specific text and a relation), a state of affairs represented (i.e. there is an x such that x wrote Theaetetus), and a search initiated to find the best candidate for x, taking the subject's beliefs as the target of tracking. The relevant belief is then recruited and the representation of the identified x (Plato) accessed. Information on the best semantic prediction (the philosopher Plato) is used to select the most likely lexical item that has Plato as meaning. Participants for which philosophical beliefs were ascribed presented Event-Related Potentials correlated to lexical-semantic processing significantly different from participants in the non-philosophers group. Considering that the only relevant difference between the two groups was the conjectured possession or absence of certain beliefs, the results were interpreted as signaling the recruitment of beliefs in predictive processes underlying textual comprehension. The results contradict eliminativist positions that consider that the mentalist vocabulary about beliefs, intentions, and desires is meaningless, since the difference found suggests that there is indeed something in brains that is denoted by these expressions (albeit vaguely and coarse) and that is modulating the signals. Although we do not readily understand exactly how belief instantiation by brains is effective, we can study how they are recruited and integrate various cognitive processes (i.e. their causal roles).

Sistemas para assistência de pessoas com sequelas de Acidente Vascular Cerebral (AVC) como, por exemplo, a Disartria apresenta interesse crescente devido ao aumento da parcela da população com esses distúrbios. Este trabalho propõe a aquisição e o processamento dos biosinais de Eletromiografia de Superficie (sEMG) no músculos do rosto ligados ao processo da fala e de Eletroencefalografia (EEG), sincronizados no tempo mediante um arquivo de áudio. Para isso realizaram-se coletas em voluntários saudáveis no Laboratório IEE e com voluntários com Disartria, previamente diagnosticados com AVC, no departamento de Fisioterapia do Hospital de Clínicas de Porto Alegre. O objetivo principal é classificar esses biosinais frente a comandos verbais estabelecidos, mediante o método computacional Support Vector Machine (SVM) para o sinal de sEMG e Naive Bayes (NB) para o sinal de EEG, visando o futuro estudo e classificação do grau de Disartria do paciente. Estes métodos foram comparados com o Linear Discriminant Analysis (LDA), que foi implementado para os sinais de sEMG e EEG. As características extraídas do sinal de sEMG foram: desvio padrão, média aritmética, skewness, kurtosis e RMS; para o sinal de EEG as características extraídas na frequência foram: Mínimo, Máximo, Média e Desvio padrão e Skewness e Kurtosis, no domínio do tempo. Como parte do pré-processamento também foi empregado o filtro espacial Common Spatial Pattern (CSP) de forma a aumentar a atividade discriminativa entre as classes de movimento no sinal de EEG. Foi avaliado através de um Projeto de Experimentos Fatorial, a natureza das coletas, o sujeito, o método computacional, o estado do sujeito e a banda de frequência filtrada para EEG. Os comandos verbais definidos: “Direita”, “Esquerda”, “Para Frente” e “Para Trás”, possibilitaram a identificação de tarefas mentais em sujeitos saudáveis e com Disartria, atingindo-se Accuracy de 77,6% - 80,8%. / Assistive technology for people with Cerebrovascular Accident (CVA) aftereffects, such as Dysarthria, is gaining interest due to the increasing proportion of the population with these disorders. This work proposes the acquisition and processing of Surface Electromyography (sEMG) signal from the speech process face muscles and Electroencephalography (EEG) signal, synchronized in time by an audio file. For that reason assays were carried out with healthy volunteers at IEE Laboratory and with dysarthric volunteers, previously diagnosed with CVA, at the physiotherapy department of the Porto Alegre University Hospital. The main objective is to classify these biosignals in front of verbal commands established, by computational method of Support Vector Machine (SVM) for the sEMG and Naive Bayes (NB) for EEG, regarding the future study and classification of pacient degree of Dysarthria. These methods were compared with Linear Discriminant Analysis (LDA), who was implemented for sEMG and EEG. The extracted features of sEMG signal were: standard deviation, arithmetic mean, skewness, kurtosis and RMS; for EEG signal extracted features in frequency domain were: minimum, maximum, average and standard deviation, skewness and kurtosis, were used for time domain extraction. As part of pre-processing, Common Spatial Pattern (CSP) filter was also employed, in order to increase the discriminating activity between motion classes in the EEG signal. Data were evaluated in a factorial experiment project, with nature of assays, subject, computational method, subject health state and specifically for EEG were evaluated frequency band filtered. Defined verbal commands, "Right", "Left", "Forward" and "Back", allowed the identification of mental tasks in healthy subjects and dysarthric subjects, reaching Accuracy of 77.6% - 80.8%.

Visual working memory (WM) is the ability to hold information in mind for a short time before acting on it. The capacity of WM is strikingly limited. To make the most of this precious resource, humans exhibit a high degree of cognitive flexibility: We can prioritize information that is relevant to behavior, and inhibit unnecessary distractions. This thesis examines some behavioral and neural correlates of flexibility in WM. When information is of particular importance, anticipatory attention can be directed to where it will likely appear. Oscillations in visual cortex, in the 10-Hz range, play an important role in regulating excitability of such prioritized locations. Chapter 4 describes how even spontaneous fluctuations in 10-Hz synchronization (measured by electroencephalography, EEG) before encoding influence WM. Chapters 2 and 3 describe how attention can be directed retrospectively to items even if they are already stored in WM. Chapter 3 discusses how retrospective cues change neural synchronization similarly to anticipatory cues. Behavioral and neural measures additionally indicate that the boosting of an item through retrospective cues does not require prolonged deployment of attention: rather, it may be a transient process. The second half of this thesis additionally examines how items are represented in visual WM. Chapter 5 summarizes a study using pattern analysis of magnetoencephalographic (MEG) and EEG data to decode features of visual templates stored in WM. Decoding appears transiently around the time when potential target stimuli are expected, in line with a flexible reactivation mechanism. Chapter 6 further examines separate cortical networks involved in protecting vs. updating items in WM, and tests whether task relevance changes how well WM contents can be decoded. Finally, Chapter 7 summarizes the thesis and discusses how attentional flexibility can merge WM with a wider range of sources of behavioral control.

As dificuldades no aprendizado da leitura podem ter uma causa neurológica ocasionada por fatores genéticos ou ambientais. O presente trabalho integra modelos matemáticos e neurocientíficos acerca dos processos neurais responsáveis pela leitura para estudar a dinâmica cerebral de alunos com e sem dificuldades no aprendizado da leitura. Utiliza-se, nesse estudo, uma técnica de mapeamento cerebral que considera o cérebro como uma rede complexa, onde os neurônios de diversas áreas cerebrais podem se organizar em subredes permitindo a execução paralela de diversos processos neurais. Mostra-se que os agrupamentos das áreas cerebrais de ambos os grupos, obtidos através de análise fatorial, são condizentes com modelos correntes em neurociências que sugerem para a leitura duas possíveis vias neurais, além do envolvimento de componentes de controle atencional (funções executivas). / Reading learning difficulties can be caused by genetic or environmental factors. The present work integrate graph theory and models from neuroscience about the neurological processes involved in reading to study the cerebral dynamics of children with and without learning difficulties. The brain mapping technique used in the present study, considers the brain as a complex network where neurons from several areas can be organized on sub-networks allowing a parallel execution of several neural processes. It is shown that the brain areas associations disclosed by factorial analysis, are congruent with the neuroscientific models that consider the existence of two possible neurological routes involved in reading besides the participation of the executive functions.

As dificuldades no aprendizado da leitura podem ter uma causa neurológica ocasionada por fatores genéticos ou ambientais. O presente trabalho integra modelos matemáticos e neurocientíficos acerca dos processos neurais responsáveis pela leitura para estudar a dinâmica cerebral de alunos com e sem dificuldades no aprendizado da leitura. Utiliza-se, nesse estudo, uma técnica de mapeamento cerebral que considera o cérebro como uma rede complexa, onde os neurônios de diversas áreas cerebrais podem se organizar em subredes permitindo a execução paralela de diversos processos neurais. Mostra-se que os agrupamentos das áreas cerebrais de ambos os grupos, obtidos através de análise fatorial, são condizentes com modelos correntes em neurociências que sugerem para a leitura duas possíveis vias neurais, além do envolvimento de componentes de controle atencional (funções executivas). / Reading learning difficulties can be caused by genetic or environmental factors. The present work integrate graph theory and models from neuroscience about the neurological processes involved in reading to study the cerebral dynamics of children with and without learning difficulties. The brain mapping technique used in the present study, considers the brain as a complex network where neurons from several areas can be organized on sub-networks allowing a parallel execution of several neural processes. It is shown that the brain areas associations disclosed by factorial analysis, are congruent with the neuroscientific models that consider the existence of two possible neurological routes involved in reading besides the participation of the executive functions.