M. Tech. Electrical Engineering / Determines whether Hidden Markov models (HMM) can be used to classify steady state visual evoked electroencephalogram signals in a BCI system. This is for the purpose of aiding disabled people in driving a wheelchair.

Made available in DSpace on 2014-12-17T14:56:05Z (GMT). No. of bitstreams: 1 AndreFB_DISSERT.pdf: 2147554 bytes, checksum: 3ed5f0d06e3b072597f2eae69b7d1ca2 (MD5) Previous issue date: 2012-02-24 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / The Brain-Computer Interfaces (BCI) have as main purpose to establish a communication path with the central nervous system (CNS) independently from the standard pathway (nervous, muscles), aiming to control a device. The main objective of the current research is to develop an off-line BCI that separates the different EEG patterns resulting from strictly mental tasks performed by an experimental subject, comparing the effectiveness of different signal-preprocessing approaches. We also tested different classification approaches: all versus all, one versus one and a hierarchic classification approach. No preprocessing techniques were found able to improve the system performance. Furthermore, the hierarchic approach proved to be capable to produce results above the expected by literature / As interfaces c?rebro-computador (ICC) t?m como objetivo estabelecer uma via de comunica??o com o sistema nervoso central (SNC) que seja independente das vias padr?o (nervos, m?sculos), visando o controle de algum dispositivo. O objetivo principal da presente pesquisa ? desenvolver uma ICC off-line que separe os diferentes padr?es de EEG resultantes de tarefas puramente mentais realizadas por um sujeito experimental, comparando a efic?cia de diferentes abordagens de pr?-processamento do sinal. Tamb?m foram testadas diferentes abordagens de classifica??o: todos contra todos, um contra um e uma abordagem hier?rquica de classifica??o. N?o foram encontradas t?cnicas de pr?-processamento que melhorem os resultados do sistema. Al?m disso, a abordagem hier?rquica sugerida mostrou-se capaz de produzir resultados acima do padr?o esperado pela literatura

Orientador: Prof. Dr. Diogo Coutinho Soriano / Dissertação (mestrado) - Universidade Federal do ABC. Programa de Pós-Graduação em Engenharia Biomédica, 2018. / A busca por materiais funcionais que possam desempenhar reparo e/ou regeneracao de Uma interface cérebro-computador (BCI) consiste em um sistema que busca extrair informações da atividade do sistema nervoso central e traduzi-las em comandos de saída, os quais podem eventualmente ser usados para controle de dispositivos assistivos. Mais do que contribuir para o controle de tecnologias assistivas ou reabilitação de pessoas com severas limitações, um sistema BCI pode contribuir para uma melhor compreensão do funcionamento cerebral e dos complexos mecanismos de cognição na medida em que se busca avaliar as variáveis mais relevantes para a eficiente decodificação de tarefas mentais. Entre as possíveis formas de se estudar o funcionamento cerebral destaca-se a quantificação da conectividade funcional, a qual visa estabelecer a similaridade observacional entre diferentes regiões cerebrais. Tal estratégia tem sido utilizada na caracterização e diagnóstico de patologias de grande relevância como depressão, Parkinson, Alzheimer, distúrbios de atenção, entre outras. Tendo isso em vista, este trabalho visou estudar o desempenho de decodificação de tarefas mentais a partir de métricas de grafos (grau, coeficiente de agregação, centralidade de intermediação e centralidade de autovetor) obtidas pela avaliação da conectividade funcional no contexto de sinais eletroencefalográficos na execução de paradigmas clássicos de sistemas BCI definidos pela imagética motora e os potenciais visualmente evocados em regime permanente (SSVEP). Além da análise comparativa entre tais métricas, o presente trabalho apresenta um estudo em relação ao desempenho de decodificação quando diferentes métodos de estimação da matriz de adjacência - forma de representação da conectividade funcional ¿ são utilizados, os quais abrangem as medidas de similaridade definidas pela correlação de Pearson, de Spearman e contagem de recorrência espaço-temporal (STR), sendo a última uma proposta original desta dissertação. Como resultado, para os sinais relacionados à BCIs baseadas em imaginação de movimentos, a STR obteve o melhor desempenho considerando todos os sujeitos e classes, mostrando-se uma possível abordagem para extração de características no contexto de sistemas BCI baseadas em imagética de tarefas. Para os sinais relacionados ao paradigma SSVEP, a decodificação baseada na conectividade funcional alcançou desempenhos satisfatórios, porém inferiores aos da análise em frequência classicamente utilizada neste contexto. / Brain-computer interface (BCI) consists of a system that aims to extract information from the activity of central nervous system and translate it into output commands, which can eventually be used to control assistive devices. More than contributing to the control of assistive technologies or rehabilitation of people with severe limitations, a BCI can also contribute to a better understanding of brain functioning and the complex mechanisms of cognition when evaluating the most relevant variables for the efficient decoding of mental tasks. Among the possible ways to study brain functioning, the functional connectivity quantification deserves careful attention, since it aims to establish the observational similarity between different brain regions. Such strategy has been used in the characterization and diagnosis of pathologies of great relevance such as depression, Parkinson, Alzheimer, attention disorders, among others. This work aimed to study the performance of decoding mental tasks from graph metrics (degree, clustering coefficient, betweenness centrality and eigenvector centrality) obtained by evaluation of functional connectivity in the context of electroencephalographic signals in the execution of classic BCI paradigms defined by motor imagery and steady state visually evoked potentials (SSVEP). In addition to the comparative analysis of such metrics, this work also presents a study regarding decoding performance when using different methods of adjacency matrix estimation - a functional connectivity representation - which include similarity measures defined by the correlation of Pearson, Spearman and Space-Time Recurrence counting (STR), being the latter an original proposal of this work. As main results, for signals related to motor imagery BCI, STR obtained the best performance considering all the subjects and classes, stablishing a possible approach for feature extraction in the context of motor imagery BCIs. For signals related to the SSVEP paradigm, decoding based on functional connectivity achieved satisfactory performance, but lower than the spectral analysis, classically used in this context.

Orientador: Prof. Dr. Ricardo Suyama / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia da Informação, Santo André, 2018. / Uma interface cerebro-computador (BCI) e projetada para que se consiga, de modo efetivo, fornecer uma via alternativa de comunicacao entre o cerebro do usuario e o computador. Sinais captados por meio de eletrodos, tipicamente posicionados no escalpo do individuo, sao previamente processados para que haja eliminacao de ruidos externos. A partir dai, diversas tecnicas para processamento de sinais sao utilizadas para posteriormente classificar os sinais registrados e realizar a traducao do estado mental do usuario em um comando especifico a ser executado pelo computador. No presente trabalho sao utilizadas tecnicas de representacao esparsa dos sinais para a extracao de caracteristicas relevantes para classificacao dos mesmos, com intuito de aumentar a robustez e melhorar o desempenho do sistema. Para a extracao de sinais esparsos, foram utilizados algoritmos de criacao de dicionarios, a partir dos quais e possivel obter uma representacao esparsa para todo o subespaco de sinal. No trabalho foram utilizados 5 diferentes algoritmos de criacao de dicionario: Metodo de direcoes otimas (MOD), K-SVD, RLS-DLA, LS-DLA e Aprendizado de dicionario Online (ODL). A classificacao dos sinais foi realizada com o metodo de .. vizinhos mais proximos (k - NN). Os resultados obtidos com a abordagem de representacao esparsa foram comparados com os resultados do BCI Competition IV dataset 2a. Para o primeiro colocado da competicao foi obtido, em termos do coeficiente kappa, uma acuracia de 0.57 enquanto que no trabalho utilizando os metodos esparsos, obteve-se, em coeficiente kappa, uma acuracia de 0.90. Em comparacao obteve-se um ganho de 0.33 de acuracia, onde se deduz que o uso de sinais esparsos pode ser benefico para o dificil problema de se projetar uma interface cerebro computador. / A brain computer interface (BCI) is designed to effectively translate commands thought by human individuals into commands that a computer can effectively understand. Electrical impulses generated from the brain sculp are recorded from a device called an electroencephalograph and are preprocessed for elimination of external noise. From there, several techniques for signal processing are used to later classify the signals obtained by the electroencephalograph. In this work, techniques for sparse representation of signals are used for feature extraction, in order to increase robustness and system performance. For the extraction of sparse signals, five different dictionary learning algorithms were used, being able to produce a basis capable of represensing the entire signal subspace. In this work, 5 different dictionary learning algorithms were used: Method of Optimal Directions (MOD), K-SVD, Recursive Least Square Dictionary Learning (RLS-DLA), Least Square Dictionary Learning (LS-DLA) and Online Dictionary Learning (ODL). For the classification task, the k-NN method was used. The simulation results obtained with this approach were compared with the best BCI Competition IV dataset 2a results. For the first place in the competition, an accuracy of 0.57 was obtained, in terms of the kappa coefficient, whereas in the work using the sparse methods, a kappa coefficient of 0.90 was obtainned, improving accuracy in 0.33 accuracy was obtained, which indicates that the use of sparse signals may be beneficial to the difficult problem of designing a brain computer interface.

Aujourd’hui, les modèles computationnels de l'apprentissage et de la prise de décision chez l'homme se sont raffinés et complexifiés pour prendre la forme de modèles génératifs des données psychophysiologiques de plus en plus réalistes d’un point de vue neurobiologique et biophysique. Dans le même temps, le nouveau champ de recherche des interfaces cerveau-machine (ICM) s’est développé de manière exponentielle. L'objectif principal de cette thèse était d'explorer comment le paradigme de l'électrophysiologie temps-réel peut contribuer à élucider les processus d'apprentissage et de prise de décision perceptive chez l’homme. Au niveau expérimental, j'ai étudié les décisions perceptives somatosensorielles grâce à des tâches de discrimination de fréquence tactile. En particulier, j'ai montré comment un contexte sensoriel implicite peut influencer nos décisions. Grâce à la magnétoencéphalographie (MEG), j'ai pu étudier les mécanismes neuronaux qui sous-tendent cette adaptation perceptive. L’ensemble de ces résultats renforce l'hypothèse de la construction implicite d’un a priori ou d'une référence interne au cours de l'expérience. Aux niveaux théoriques et méthodologiques, j'ai proposé une vue générique de la façon dont l'électrophysiologie temps-réel pourrait être utilisée pour optimiser les tests d'hypothèses, en adaptant le dessin expérimental en ligne. J'ai pu fournir une première validation de cette démarche adaptative pour maximiser l'efficacité du dessin expérimental au niveau individuel. Ce travail révèle des perspectives en neurosciences fondamentales et cliniques ainsi que pour les ICM / Today, psychological as well as physiological models of perceptual learning and decision-making processes have recently become more biologically plausible, leading to more realistic (and more complex) generative models of psychophysiological observations. In parallel, the young but exponentially growing field of Brain-Computer Interfaces (BCI) provides new tools and methods to analyze (mostly) electrophysiological data online. The main objective of this PhD thesis was to explore how the BCI paradigm could help for a better understanding of perceptual learning and decision making processes in humans. At the empirical level, I studied decisions based on tactile stimuli, namely somatosensory frequency discrimination. More specifically, I showed how an implicit sensory context biases our decisions. Using magnetoencephalography (MEG), I was able to decipher some of the neural correlates of those perceptual adaptive mechanisms. These findings support the hypothesis that an internal perceptual-reference builds up along the course of the experiment. At the theoretical and methodological levels, I propose a generic view and method of how real-time electrophysiology could be used to optimize hypothesis testing, by adapting the experimental design online. I demonstrated the validity of this online adaptive design optimization (ADO) approach to maximize design efficiency at the individual level. I also discussed the implications of this work for basic and clinical neuroscience as well as BCI itself

Les interfaces cerveau-ordinateur (ou BCI en anglais pour Brain-Computer Interfaces) mettent en place depuis le système nerveux central un circuit artificiel secondaire qui remplace l’utilisation des nerfs périphériques, permettant entre autres à des personnes ayant une déficience motrice grave d’interagir, uniquement à l’aide de leur activité cérébrale, avec différents types d’applications, tels qu’un système d’écriture, une neuro-prothèse, un fauteuil roulant motorisé ou un bras robotique. Une technique répandue au sein des BCI pour enregistrer l’activité cérébrale est l’électroencéphalographie (EEG), étant donné que contrairement à d’autres techniques d’imagerie, elle est non invasif et peu coûteuse. En outre, l’imagination motrice (MI), c’est-à-dire les oscillations des neurones du cortex moteur générées lorsque les sujets imaginent effectuer un mouvement sans réellement l’accomplir, est appropriée car détectable dans l’EEG et liée à l’activité motrice pour concevoir des interfaces comme des neuro-prothèses non assujetties à des stimuli. Cependant, même si des progrès importants ont été réalisés au cours des dernières années, un contrôle 3D complet reste un objectif à atteindre. Afin d’explorer de nouvelles solutions pour surmonter les limitations existantes, nous présentons une approche multiclasses qui considère la détection des imaginations motrices combinées. Le paradigme proposé comprend l’utilisation de la main gauche, de la main droite, et des deux pieds ensemble. Ainsi, par combinaison, huit commandes peuvent être fournies pour diriger un bras robotisé comprenant quatorze mouvements différents qui offrent un contrôle 3D complet. À cette fin, un système de commutation entre trois modes (déplacement du bras, du poignet ou des doigts) a été conçu et permet de gérer les différentes actions en utilisant une même commande. Ce système a été mis en oeuvre sur la plate-forme OpenViBE. En outre, pour l’extraction de caractéristiques une nouvelle approche de traitement d’information fournie par les capteurs a été développée sur la base de l’emplacement spécifique des sources d’activité liées aux parties du corps considérées. Cette approche permet de regrouper au sein d’une seule classe les différentes actions pour lesquelles le même membre est engagé, d’une manière que la tâche multiclasses originale se transforme en un problème équivalent impliquant une série de modèles de classification binaires. Cette approche permet d’utiliser l’algorithme de Common Spatial pattern (CSP) dont la capacité à discriminer des rythmes sensorimoteurs a été largement montrée mais qui présente l’inconvénient d’être applicable uniquement pour différencier deux classes. Nous avons donc également contribué à une nouvelle stratégie qui combine un ensemble de CSP et la géométrie riemannienne. Ainsi des caractéristiques plus discriminantes peuvent être obtenues comme les distances séparant les données des centres des classes considérées. Ces stratégies ont été appliquées sur trois nouvelles approches de classification qui ont été comparées à des méthodes de discrimination multiclasses classiques en utilisant les signaux EEG d’un groupe de sujets sains naïfs, montrant ainsi que les alternatives proposées permettent non seulement d’améliorer l’existant, mais aussi de réduire la complexité de la classification / Brain-Computer Interfaces (BCIs) replace the natural nervous system outputs by artificial ones that do not require the use of peripheral nerves, allowing people with severe motor impairments to interact, only by using their brain activity, with different types of applications, such as spellers, neuroprostheses, wheelchairs, or among others robotics devices. A very popular technique to record signals for BCI implementation purposes consists of electroencephalography (EEG), since in contrast with other alternatives, it is noninvasive and inexpensive. In addition, due to the potentiality of Motor Imagery (MI, i.e., brain oscillations that are generated when subjects imagine themselves performing a movement without actually accomplishing it) to generate suitable patterns for scheming self-paced paradigms, such combination has become a common solution for BCI neuroprostheses design. However, even though important progress has been made in the last years, full 3D control is an unaccomplished objective. In order to explore new solutions for overcoming the existing limitations, we present a multiclass approach that considers the detection of combined motor imageries, (i.e., two or more body parts used at the same time). The proposed paradigm includes the use of the left hand, right hand, and both feet together, from which eight commands are provided to direct a robotic arm comprising fourteen different movements that afford a full 3D control. To this end, an innovative switching-mode scheme that allows managing different actions by using the same command was designed and implemented on the OpenViBE platform. Furthermore, for feature extraction a novel signal processing scheme has been developed based on the specific location of the activity sources that are related to the considered body parts. This insight allows grouping together within a single class those conditions for which the same limb is engaged, in a manner that the original multiclass task is transformed into an equivalent problem involving a series of binary classification models. Such approach allows using the Common Spatial Pattern (CSP) algorithm; which has been shown to be powerful at discriminating sensorimotor rhythms, but has the drawback of being suitable only to differentiate between two classes. Based on this perspective we also have contributed with a new strategy that combines together the CSP algorithm and Riemannian geometry. In which the CSP projected trials are mapped into the Riemannian manifold, from where more discriminative features can be obtained as the distances separating the input data from the considered class means. These strategies were applied on three new classification approaches that have been compared to classical multiclass methods by using the EEG signals from a group of naive healthy subjects, showing that the proposed alternatives not only outperform the existing schema, but also reduce the complexity of the classification task

Cette thèse s'intéresse à un problème logique dont les enjeux théoriques et pratiques sont multiples. De manière simple, il peut être présenté ainsi : imaginez que vous êtes dans un labyrinthe, dont vous connaissez toutes les routes menant à chacune des portes de sortie. Derrière l'une de ces portes se trouve un trésor, mais vous n'avez le droit d'ouvrir qu'une seule porte. Un vieil homme habitant le labyrinthe connaît la bonne sortie et se propose alors de vous aider à l'identifier. Pour cela, il vous indiquera la direction à prendre à chaque intersection. Malheureusement, cet homme ne parle pas votre langue, et les mots qu'il utilise pour dire ``droite'' ou ``gauche'' vous sont inconnus. Est-il possible de trouver le trésor et de comprendre l'association entre les mots du vieil homme et leurs significations ? Ce problème, bien qu'en apparence abstrait, est relié à des problématiques concrètes dans le domaine de l'interaction homme-machine. Remplaçons le vieil homme par un utilisateur souhaitant guider un robot vers une sortie spécifique du labyrinthe. Ce robot ne sait pas en avance quelle est la bonne sortie mais il sait où se trouvent chacune des portes et comment s'y rendre. Imaginons maintenant que ce robot ne comprenne pas a priori le langage de l'humain; en effet, il est très difficile de construire un robot à même de comprendre parfaitement chaque langue, accent et préférence de chacun. Il faudra alors que le robot apprenne l'association entre les mots de l'utilisateur et leur sens, tout en réalisant la tâche que l'humain lui indique (i.e.trouver la bonne porte). Une autre façon de décrire ce problème est de parler d'auto-calibration. En effet, le résoudre reviendrait à créer des interfaces ne nécessitant pas de phase de calibration car la machine pourrait s'adapter,automatiquement et pendant l'interaction, à différentes personnes qui ne parlent pas la même langue ou qui n'utilisent pas les mêmes mots pour dire la même chose. Cela veut aussi dire qu'il serait facile de considérer d’autres modalités d'interaction (par exemple des gestes, des expressions faciales ou des ondes cérébrales). Dans cette thèse, nous présentons une solution à ce problème. Nous appliquons nos algorithmes à deux exemples typiques de l'interaction homme robot et de l'interaction cerveau machine: une tâche d'organisation d'une série d'objets selon les préférences de l'utilisateur qui guide le robot par la voix, et une tâche de déplacement sur une grille guidé par les signaux cérébraux de l'utilisateur. Ces dernières expériences ont été faites avec des utilisateurs réels. Nos résultats démontrent expérimentalement que notre approche est fonctionnelle et permet une utilisation pratique d’une interface sans calibration préalable. / This thesis investigates how a machine can be taught a new task from unlabeled humaninstructions, which is without knowing beforehand how to associate the human communicative signals withtheir meanings. The theoretical and empirical work presented in this thesis provides means to createcalibration free interactive systems, which allow humans to interact with machines, from scratch, using theirown preferred teaching signals. It therefore removes the need for an expert to tune the system for eachspecific user, which constitutes an important step towards flexible personalized teaching interfaces, a key forthe future of personal robotics.Our approach assumes the robot has access to a limited set of task hypotheses, which include the task theuser wants to solve. Our method consists of generating interpretation hypotheses of the teaching signalswith respect to each hypothetic task. By building a set of hypothetic interpretation, i.e. a set of signallabelpairs for each task, the task the user wants to solve is the one that explains better the history of interaction.We consider different scenarios, including a pick and place robotics experiment with speech as the modalityof interaction, and a navigation task in a brain computer interaction scenario. In these scenarios, a teacherinstructs a robot to perform a new task using initially unclassified signals, whose associated meaning can bea feedback (correct/incorrect) or a guidance (go left, right, up, ...). Our results show that a) it is possible tolearn the meaning of unlabeled and noisy teaching signals, as well as a new task at the same time, and b) itis possible to reuse the acquired knowledge about the teaching signals for learning new tasks faster. Wefurther introduce a planning strategy that exploits uncertainty from the task and the signals' meanings toallow more efficient learning sessions. We present a study where several real human subjects controlsuccessfully a virtual device using their brain and without relying on a calibration phase. Our system identifies, from scratch, the target intended by the user as well as the decoder of brain signals.Based on this work, but from another perspective, we introduce a new experimental setup to study howhumans behave in asymmetric collaborative tasks. In this setup, two humans have to collaborate to solve atask but the channels of communication they can use are constrained and force them to invent and agree ona shared interaction protocol in order to solve the task. These constraints allow analyzing how acommunication protocol is progressively established through the interplay and history of individual actions.

L'estimation de l'état mental d'un individu sur la base de son activité cérébrale et de ses activités physiologiques résultantes est devenue l'un des challenges des interfaces cerveau-machine (ICM) dites passives, dans le but notamment de répondre à un besoin en neuroergonomie. Ce travail de thèse se focalise sur l'estimation des états de fatigue et de charge mentale. Son objectif est de proposer des chaines de traitement efficaces et réalistes dans leur mise en œuvre. Ainsi, un des points à l'étude a été la modulation des indicateurs de charge ainsi que la robustesse des performances de classification en fonction du temps passé sur une tâche (TPT). L'impact de la charge et du TPT sur les marqueurs d'état attentionnel a aussi été évalué. Pour ce faire, un protocole expérimental a été mis en œuvre afin de recueillir les signaux électro-encéphalographiques (EEG), cardiaques (ECG) et oculaires (EOG) de participants volontaires sains lors de la réalisation prolongée d'une tâche combinant charge en mémoire de travail et attention sélective. Des chaînes de traitement performantes incluant une étape de filtrage spatial et une classification supervisée ont été mises en place afin de classer au mieux ces états. La pertinence de plusieurs marqueurs électrophysiologiques a été comparée, notamment l'activité EEG spontanée et les potentiels évoqués (PEs), ainsi que différentes étapes de prétraitement dont les méthodes de filtrage spatial pour PEs. Des effets d'interactions ont été mis au jour entre les différents états mentaux, dont un effet négatif du TPT sur les performances en classification de la charge mentale lorsque l'on utilise des marqueurs mesurant la puissance moyenne de l'EEG dans des bandes de fréquence d'intérêt. La chaîne basée sur les PEs est en revanche robuste à cet effet. Une comparaison du type de stimuli utilisables pour éliciter les PEs a révélé que des stimuli tâche-indépendants permettent tout de même d'obtenir des performances très élevées, ce qui montre leur pertinence pour une implémentation en situation réelle. En perspective seront présentés des travaux en cours visant à mettre en évidence des marqueurs de charge mentale robustes à la tâche, ainsi que l'utilité des potentiels évoqués auditifs en paradigme de simple stimulus. / Mental state estimation on the basis of cerebral activity and its resulting physiological activities has become a challenge for passive Brain-Computer Interfaces (BCI), in particular to address a need in neuroergonomics. This thesis work focuses on mental fatigue and workload estimation. Its purpose is to provide efficient and realistic processing chains. Thus, one issue was the modulation of workload markers as well as classification performance robustness depending on time-on-task (TOT). The impact of workload and TOT on attentional state markers was also assessed. For those purposes, an experimental protocol was implemented to collect the electroencephalographic (EEG), cardiac (ECG) and ocular (EOG) signals from healthy volunteers as they performed for a prolonged period of time a task that mixes working memory load and selective attention. Efficient signal processing chains that include spatial filtering and classification steps were designed in order to better estimate these mental states. The relevance of several electrophysiological markers was compared, among which spontaneous EEG activity and event-related potentials (ERPs), as well as various preprocessing steps such as spatial filtering methods for ERPs. Interaction effects between mental states were brought to light. In particular, TOT negatively impacted mental workload estimation when using power features. However, the chain based on ERPs was robust to this effect. A comparison of the type of stimuli that can be used to elicit the ERPs revealed that task-independent probes still allow very high performance, which shows their relevance for real-life implementation. Lastly, ongoing work that aims at assessing task-robust workload markers, as well as the usefulness of auditory ERPs in a single-stimulus paradigm will be presented as prospects.

Um dos desafios propostos por pesquisadores em neurociência aos engenheiros biomédicos é a interação cérebro-máquina. O sistema nervoso comunica-se interpretando sinais eletroquímicos, e circuitos implantáveis podem tomar decisões de modo a interagir com o meio biológico. Sabe-se também que a doença de Parkinson está relacionada a um déficit do neurotransmissor dopamina. Para controlar a concentração de dopamina diferentes técnicas tem sido empregadas como estimuladores elétricos, magnéticos e drogas. Neste trabalho obteve-se o controle da concentração do neurotransmissor de maneira automática uma vez que atualmente isto não é realizado. Para tanto, projetou-se e desenvolveu-se quatro sistemas: a estimulação cerebral profunda ou deep brain stimulation (DBS), a estimulação transmagnética ou transmagnetic stimulation (TMS), um controle de bomba de infusão ou infusion pump control (IPC) para a entrega de drogas e um sistema de voltametria cíclica de varredura rápida ou fast scan ciclic voltammetry (FSCV) (circuito que detecta variações de concentração de neurotransmissores como a dopamina - DA). Também foi necessário o desenvolvimento de softwares para a visualização de dados e análises em sincronia com acontecimentos ou experimentos correntes, facilitando a utilização destes dispositivos quando emprega-se bombas de infusão e a sua flexibilidade é tal que a DBS ou a TMS podem ser utilizadas de maneira manual ou automática além de outras técnicas de estimulação como luzes, sons, etc. O sistema desenvolvido permite controlar de forma automática a concentração da DA. A resolução do sistema é de 0.4 µmol/L podendo-se ajustar o tempo para correção da concentração entre 1 e 90 segundos. O sistema permite controlar concentrações entre 1 e 10 µmol/L, com um erro de cerca de +/- 0,8 µmol/L. Embora desenhado para o controle da concentração de dopamina o sistema pode ser utilizado para controlar outros neurotransmissores. Propõe-se continuar o desenvolvimento em malha fechada empregando FSCV e DBS (ou TMS, ou infusão), utilizando modelos animais parkinsonianos. / One of the challenges to biomedical engineers proposed by researchers in neuroscience is brain machine interaction. The nervous system communicates by interpreting electrochemical signals, and implantable circuits make decisions in order to interact with the biological environment. It is well known that Parkinson’s disease is related to a deficit of dopamine (DA). Different methods has been employed to control dopamine concentration like magnetic or electrical stimulators or drugs. In this work was automatically controlled the neurotransmitter concentration since this is not currently employed. To do that, four systems were designed and developed: deep brain stimulation (DBS), transmagnetic stimulation (TMS), Infusion Pump Control (IPC) for drug delivery, and fast scan cyclic voltammetry (FSCV) (sensing circuits which detect varying concentrations of neurotransmitters like dopamine caused by these stimulations). Some softwares also were developed for data display and analysis in synchronously with current events in the experiments. This allowed the use of infusion pumps and their flexibility is such that DBS or TMS can be used in single mode and other stimulation techniques and combinations like lights, sounds, etc. The developed system allows to control automatically the concentration of DA. The resolution of the system is around 0.4 µmol/L with time correction of concentration adjustable between 1 and 90 seconds. The system allows controlling DA concentrations between 1 and 10 µmol/L, with an error about +/- 0.8 µmol/L. Although designed to control DA concentration, the system can be used to control, the concentration of other substances. It is proposed to continue the closed loop development with FSCV and DBS (or TMS, or infusion) using parkinsonian animals models.

Um dos desafios propostos por pesquisadores em neurociência aos engenheiros biomédicos é a interação cérebro-máquina. O sistema nervoso comunica-se interpretando sinais eletroquímicos, e circuitos implantáveis podem tomar decisões de modo a interagir com o meio biológico. Sabe-se também que a doença de Parkinson está relacionada a um déficit do neurotransmissor dopamina. Para controlar a concentração de dopamina diferentes técnicas tem sido empregadas como estimuladores elétricos, magnéticos e drogas. Neste trabalho obteve-se o controle da concentração do neurotransmissor de maneira automática uma vez que atualmente isto não é realizado. Para tanto, projetou-se e desenvolveu-se quatro sistemas: a estimulação cerebral profunda ou deep brain stimulation (DBS), a estimulação transmagnética ou transmagnetic stimulation (TMS), um controle de bomba de infusão ou infusion pump control (IPC) para a entrega de drogas e um sistema de voltametria cíclica de varredura rápida ou fast scan ciclic voltammetry (FSCV) (circuito que detecta variações de concentração de neurotransmissores como a dopamina - DA). Também foi necessário o desenvolvimento de softwares para a visualização de dados e análises em sincronia com acontecimentos ou experimentos correntes, facilitando a utilização destes dispositivos quando emprega-se bombas de infusão e a sua flexibilidade é tal que a DBS ou a TMS podem ser utilizadas de maneira manual ou automática além de outras técnicas de estimulação como luzes, sons, etc. O sistema desenvolvido permite controlar de forma automática a concentração da DA. A resolução do sistema é de 0.4 µmol/L podendo-se ajustar o tempo para correção da concentração entre 1 e 90 segundos. O sistema permite controlar concentrações entre 1 e 10 µmol/L, com um erro de cerca de +/- 0,8 µmol/L. Embora desenhado para o controle da concentração de dopamina o sistema pode ser utilizado para controlar outros neurotransmissores. Propõe-se continuar o desenvolvimento em malha fechada empregando FSCV e DBS (ou TMS, ou infusão), utilizando modelos animais parkinsonianos. / One of the challenges to biomedical engineers proposed by researchers in neuroscience is brain machine interaction. The nervous system communicates by interpreting electrochemical signals, and implantable circuits make decisions in order to interact with the biological environment. It is well known that Parkinson’s disease is related to a deficit of dopamine (DA). Different methods has been employed to control dopamine concentration like magnetic or electrical stimulators or drugs. In this work was automatically controlled the neurotransmitter concentration since this is not currently employed. To do that, four systems were designed and developed: deep brain stimulation (DBS), transmagnetic stimulation (TMS), Infusion Pump Control (IPC) for drug delivery, and fast scan cyclic voltammetry (FSCV) (sensing circuits which detect varying concentrations of neurotransmitters like dopamine caused by these stimulations). Some softwares also were developed for data display and analysis in synchronously with current events in the experiments. This allowed the use of infusion pumps and their flexibility is such that DBS or TMS can be used in single mode and other stimulation techniques and combinations like lights, sounds, etc. The developed system allows to control automatically the concentration of DA. The resolution of the system is around 0.4 µmol/L with time correction of concentration adjustable between 1 and 90 seconds. The system allows controlling DA concentrations between 1 and 10 µmol/L, with an error about +/- 0.8 µmol/L. Although designed to control DA concentration, the system can be used to control, the concentration of other substances. It is proposed to continue the closed loop development with FSCV and DBS (or TMS, or infusion) using parkinsonian animals models.