Les interfaces cerveau-machine (ICM) permettent de contrôler un appareil électronique grâce aux signaux cérébraux. Plusieurs méthodes de mesure de ces signaux, invasives ou non, peuvent être utilisées. L'électro-encéphalographie (EEG) est la méthode non-invasive la plus étudiée car elle propose une bonne résolution temporelle et le matériel nécessaire est bien moins volumineux que les systèmes de mesure des champs magnétiques.L'EEG a cependant une faible résolution spatiale, ce qui limite les performances des ICM utilisant cette méthode de mesure. Ce souci de résolution spatiale peut être réglé en utilisant le problème inverse de l'EEG, qui permet de passer des potentiels mesurés en surface à une distribution volumique des sources de courant dans le cerveau. Le principal verrou de cette technique est le temps nécessaire (plusieurs heures) pour calculer avec une station de travail la matrice permettant de résoudre leproblème inverse. Dans le cadre de cette thèse, nous avons étudié les solutions actuelles pour accélérer matériellement la conception de cette matrice. Nous avons ainsi proposé, conçu et testé une architecture électronique dédiée à ces traitements pour ICM. Les premiers résultats démontrent que notre solution permet de passer de plusieurs heures de calcul sur une station de travail à quelques minutes sur circuit reconfigurable. Cette accélération des traitements d'imagerie par EEG facilitera grandement la recherche sur l'utilisation du problème inverse et ouvrira ainsi de nouvelles perspectives pour le domaine de l'ICM. / Brain-Computer Interfaces (BCI) are systems that use brain activity to control an external device. Various techniques can be used to collect the neural signals. The measurement can be invasive ornon-invasive. Electroencephalography (EEG) is the most studied non-invasive method. Indeed, EEG offers a fine temporal resolution and ease of use but its spatial resolution limits the performances of BCI based on EEG. The spatial resolution of EEG can be improved by solving the EEG inverse problem, which allows to determine the distribution of electrical sources in the brain from EEG. Currently, the main difficulty is the time needed(several hours) to compute the matrix which is used to solve the EEG inverse problem. This document describes the proposed solution to provide a hardware acceleration of the matrix computation. A dedicated electronic architecture has been implemented and tested. First results show that the proposed architecture divides the calculation time by a factor of 60 on a programmable circuit. This acceleration opens up new perspectives for EEG BCI.

Orientador: Douglas Domingues Bueno / Resumo: A busca por uma melhor compreensão das regiões do cérebro e suas funções nas ações humanas tem sido uma tarefa árdua, porém muito útil, principalmente para aplicações da engenharia de interface cérebro-máquina (ICM), bem como para o auxílio a diagnósticos médicos a partir de sinais obtidos dos pacientes em avaliação. No contexto do presente trabalho, destacam-se os trabalhos de interface cérebro-máquina (ICM) pela abrangência no envolvimento de técnicas, métodos e ferramentas comumente estudadas nos cursos de engenharia. Em particular, análises envolvendo técnicas de processamento de sinais de eletroencefalograma (EEG) têm se mostrado de significativa importância para o desenvolvimento dessa área. Uma abordagem amplamente utilizada nesse contexto é a ICM usando Potenciais Visuais Evocados de Estados Estacionários (SSVEP, do inglês Steady-State Visual Evoked Potentials), que, de forma geral, são sinais caracterizados pela resposta evocada do cérebro a estímulos visuais modulados em uma frequência específica. Assim, este trabalho tem o objetivo de propor uma generalização do coeficiente de correlação, conceito-base da análise de correlação canônica (CCA), técnica que tem se mostrado robusta e eficiente no reconhecimento de padrões, especialmente no caso dos SSVEP, e detalhar seu comportamento em função dos parâmetros relevantes para se estabelecer melhores práticas de uso em aplicações de ICM, incluindo fatores fisiológicos, técnicos e operacionais. / Abstract: The search for a better understanding of the brain's anatomy and its functions on human actions has been a harsh yet very useful task, especially for brain-computer interface engineering applications, as well as for medical diagnosis using signals from patients. In the context of this work, brain-computer interface (BCI) applications are highlighted due to their compreehensiveness related to techniques, methods and tools commonly studied in engineering. In particular, analyses involving eletroencephalogram (EEG) signals processing have proven to be of great significance for developing this field of study. A widely used approach is Steady State Visual Evoked Potentials (SSVEP) based BCI, which, in general, are signals characterized by the brain’s evoked response to visual stimuli modulated at a certain frequency. This work aims thus to propose a generalization of the correlation coefficient, which entails Canonical Correlation Analysis (CCA), a technique that has presented robustness and efficiency for pattern recognition, especially in SSVEP-based BCIs, and describe its behavior under relevant varying parameters to stablish better use practices in BCI applications, comprising physiological, technical and operational factors. / Mestre

Introdução: A Esclerose Lateral Amiotrófica (ELA) é uma neuronopatia de curso progressivo, caracterizada pela morte dos neurônios motores superiores e inferiores. Devido a rápida progressão da doença e ao aparecimento dos sintomas de incapacidade funcional os indivíduos com ELA buscam uma forma alternativa de comunicação e interação. Com isso, o desenvolvimento tecnológico utilizando programas de realidade virtual com ajuda de dispositivos de interação pode viabilizar mais função e auxiliar indivíduos com ELA a obter autonomia, independência, melhor qualidade de vida e inclusão. Objetivo: Identificar qual dispositivo de interação virtual é melhor para propiciar desempenho e funcionalidade em uma tarefa de realidade virtual para indivíduos com ELA. Método: Participaram do estudo 30 indivíduos que formaram o grupo ELA e 30 indivíduos com desenvolvimento típico que formaram o grupo controle, com idade entre 44 a 74 anos, pareados por idade e sexo. A tarefa utilizada, foi um jogo no computador, que consiste em estourar o maior número de bolhas possíveis durante 30 segundos. Os indivíduos foram separados em 3 grupos, cada qual utilizando uma interface diferente (Kinect, Leap Motion Controller ou Touchscreen) na fase de aquisição e retenção da tarefa. Após essas fases, foi realizada a fase de transferência com a troca de dispositivos e assim todos os grupos tiveram contato com todas as interfaces. Para análise estatística utilizou-se o número de bolhas alcançadas para cada participante, durante as fases de aquisição, retenção e transferências. Resultados: Todos os participantes, tanto do grupo ELA como do grupo controle, apresentaram melhor performance motora na utilização do dispositivo Touchscreen, porém o grupo ELA apresentou desempenho inferior com a prática de todos os dispositivos. A prática com o dispositivo Touchscreen não permitiu a transferência para os dispositivos Leap Motion Controller e Kinect, isso significa que a prática com dispositivo de característica mais real (Touchscreen) não permitiu a transferência para os dispositivos com características mais virtuais (Kinect® e Leap Motion Controller®), porém considerando a prática com os dispositivos virtuais essa transferência ocorre. Conclusão: O trabalho apresenta um avanço na compreensão de dispositivos apropriados para a utilização na reabilitação da funcionalidade de indivíduos com ELA. O dispositivo Touchscreen foi o que apresentou melhor desempenho funcional para essa população, podendo oferecer mais funcionalidades para os indivíduos na execução de tarefas virtuais / Introdution: Amyotrophic Lateral Sclerosis (ALS) is a progressive course of neuronopathy, characterized by the motor neurons death (MN) upper and lower. Due to rapid disease progression and the onset of symptoms of functional disability individuals with ALS seek an alternative form of communication and interaction. Technological development using virtual reality programs with the help of interaction devices can offer more function and assist individuals with ALS to obtain autonomy, independence, quality of life and inclusion. Objective: to identify which low-cost non-immersive interaction device, using a virtual task, is better for providing performance and functionality for individuals with ALS. Method This is an analytical cross-sectional study. A total of 60 people participated in this study, 30 individuals with ALS (18 men and 12 women, mean age = 59 years, range 44-74 years), while 30 people with normal development that were matched for age and gender with individuals with ALS formed the control group. The task used was a computer game, which consists of blowing the largest possible number of bubbles for 30 seconds. The subjects were divided into 3 groups, each using a different interface (Kinect®, Leap Motion Controller® or Touchscreen) in the task acquisition and retention stage. After these phases was carried out the transfer phase with the switching devices, then all groups had contact with all interfaces. For statistical analysis we used the number of bubbles achieved for each participant during the phases of acquisition, retention and transfer. Results: All participants, both the ALS group, both the control group showed better motor performance in the use of the Touchscreen device, but the ALS group had underperformed the practice of all devices. Practice with the touchscreen device did not allow the transfer to the Leap Motion Controller® and Kinect® devices, this means that the practice more real feature device (Touchscreen) did not allow the transfer to devices with more virtual features (Kinect® and Leap Motion controller®), but considering the practice with virtual devices that transfer occurs. Conclusion: This work presents a breakthrough in the understanding of appropriate devices for use in the rehabilitation of people with ALS functionality. The Touchscreen device showed the best functional performance for this population and can offer more features for individuals in executing virtual tasks

Normal movement execution requires proper coupling of motor and sensory activation. An increasing body of literature supports the idea that incorporation of sensory stimulation into motor rehabilitation practices increases its effectiveness. Paired associative stimulation (PAS) studies, in which afferent and efferent pathways are activated in tandem, have brought attention to the importance of well-timed stimulation rather than non-associative (i.e., open-loop) activation. In patients with tetraplegia resulting from spinal cord injury (SCI), varying degrees of upper limb function may remain and could be harnessed for rehabilitation. Incorporating associative sensory stimulation coupled with self-paced motor training would be a means for supplementing sensory deficits and improving functional outcomes. In a motor rehabilitation setting, it seems plausible that sensory feedback stimulation in response to volitional movement execution (to the extent possible), which is not utilized in most PAS protocols, would produce greater benefits. This capability is developed and tested in the present study by implementing a brain-computer interface (BCI) to apply sensory stimulation synchronized with movement execution through the detection of movement intent in real time from electroencephalography (EEG). The results demonstrate that accurate sensory stimulation application in response to movement intent is feasible in SCI patients with chronic motor deficit and often precedes the onset of movement, which is deemed optimal by PAS investigations that do not involve a volitional movement task.

<p>Computer architecture and assembly language programming microprocessor execution are basic courses taught in every computer science department. Generally, however, students have&nbsp / difficulties in mastering many of the concepts in the courses, particularly students whose first language is not English. In addition to their difficulties in understanding the purpose of given&nbsp / instructions, students struggle to mentally visualize the data movement, control and processing operations. To address this problem, this research proposed a graphical visualization approach&nbsp / and investigated the visual illustrations of such concepts and instruction execution by implementing a graphical visualization simulator as a teaching aid. The graphical simulator developed during the course of this research was applied in a computer architecture course at Kabul University, Afghanistan. Results obtained from student evaluation of the simulator show significant&nbsp / levels of success using the visual simulation teaching aid. The results showed that improved learning was achieved, suggesting that this approach could be useful in other computer science departments in Afghanistan, and elsewhere where similar challenges are experienced.</p>

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En el presente trabajo, utilizamos técnicas de microfabricación, simulaciones numéricas, experimentos de electrofisiología para explorar la viabilidad en me- jorar la interface ordenador-neurona a través de microcanales, y la biofísica para la generación de señales en los dispositivos con microcanales. También demos- tramos que los microcanales pueden ser usados como una técnica prometedora con alto rendimiento en el muestreo automático de canales iónicos a nivel subce- lular. Finalmente, se ha diseñado, fabricado y probado el micropozo-microcanal como modificación adicional a los arreglos de multielectrodos, permitiendo una alta ganancia en la relación señal/ ruido (en inglés Signal to Noise Ratio SNR), y el registro de múltiples-lugares en poblaciones de baja densidad de redes neu- ronales del hipocampo in vitro. Primero, demostramos que son de alto rendimiento los microcanales de bajo costo con interface neurona-electrodo, para el registro extracelular de la activi- dad neuronal con baja complexidad, por periodos estables de larga duración y con alta ganancia SNR. En seguida, se realiza un estudio mediante experimentos y simulaciones nu- méricas de la biofísica para la generación de las señales obtenidas de los dispositi- vos con microcanales. Basados en los resultados, racionalizamos y demostramos como es que la longitud del canal (siendo 200 μm) y la sección transversal del microcanal (siendo 12 μm2) canaliza a los potenciales de acción para estar dentro del rango de milivolts. A pesar del bajo grado de complexidad envuelto en la fabricación y aplicación, los dispositivos con microcanales otorgan una sola media de valor SNR de 101 76, lo cual es favorablemente comparable con la SNR que se obtiene de desarrollos recientes que emplean electrodos curados con CNT y Si-NWFETs. Más aún, nosotros demostramos que el microcanal es una técnica promete- dora para el alto rendimiento del muestro automático de canales iónicos a nivel subcelular: (1) Información experimental y simulaciones numéricas sugieren que las señales registradas sólo afectan los parches membranales localizados dentro del microcanal o alrededor de 100 μm de las entradas del microcanal. (2) La transferencia de masa de los componentes químicos en los microcanales fue ana- lizada por experimentos y simulaciones FEM. Los resultados muestran que los microcanales que contienen glía y tejido neuronal pueden funcionar como barre- ra de fluido/química. Los componentes químicos pueden ser solamente aplicados a diferentes compartimentos a nivel subcelular. Finalmente, basado en simulaciones numéricas y resultados experimentales, se propone que del micropozo-microcanal, obtenido de la modificación de MEA (MWMC-MEA), la longitud óptima del canal debe ser 0,3 mm y la posición 1 óptima del electrodo intracanal, hacia la entrada más cercana del microcanal, debe ser 0,1 mm. Nosotros fabricamos un prototipo de MWMC-MEA, cuyo hoyo pasante sobre las películas de Polydimethylsiloxane (PDMS) fue microtrabajado a través de la técnica de grabados reactivos de plasma de iones. La baja densidad del cultivo (57 neuronas /mm2) en el MWMC-MEAs permitió que las neuronas vivieran al menos 14 días, con lo que la señal neuronal con la máxima SNR obtenida fue de 142. 2 / In this present work, we used microfabrication techniques, numerical simulations, electrophysiological experiments to explore the feasibility of enhancing neuron-computer interfaces with microchannels and the biophysics of the signal generation in microchannel devices. We also demonstrate the microchannel can be used as a promising technique for high-throughput automatic ion-channel screening at subcellular level. Finally, a microwell-microchannel enhanced multielectrode array allowing high signal-to-noise ratio (SNR), multi-site recording from the low-density hippocampal neural network in vitro was designed, fabricated and tested. First, we demonstrate using microchannels as a low-cost neuron-electrode interface to support low-complexity, long-term-stable, high SNR extracellular recording of neural activity, with high-throughput potential. Next, the biophysics of the signal generation of microchannel devices was studied by experiments and numerical simulations. Based on the results, we demonstrate and rationalize how channels with a length of 200 μm and channel cross section of 12 μm2 yielded spike sizes in the millivolt range. Despite the low degree of complexity involved in their fabrication and use, microchannel devices provided a single-unit mean SNR of 101 76, which compares favourably with the SNR obtained from recent developments employing CNT-coated electrodes and Si-NWFETs. Moreover, we further demonstrate that the microchannel is a promising technique for high-throughput automatic ion-channel screening at subcellular level: (1) Experimental data and numerical simulations suggest that the recorded signals are only affected by the membrane patches located inside the microchannel or within 100 μm to the microchannel entrances. (2) The mass transfer of chemical compounds in microchannels was analyzed by experiments and FEM simulations. The results show that the microchannel threaded by glial and neural tissue can function as fluid/chemical barrier. Thus chemical compounds can be applied to different subcellular compartments exclusively. Finally, a microwell-microchannel enhanced MEA (MWMC-MEA), with the optimal channel length of 0.3 mm and the optimal intrachannel electrode position of 0.1 mm to the nearest channel entrance, was proposed based on numerical simulation and experiment results. We fabricated a prototype of the MWMCMEA, whose through-hole feature of Polydimethylsiloxane film (PDMS) was micromachined by reactive-ion etching. The low-density culture (57 neurons/mm2) were survived on the MWMC-MEAs for at least 14 days, from which the neuronal signal with the maximum SNR of 142 was obtained.

The main aim of this study, in which 310 university students participated, is to investigate whether or not computer interfaces offering human-like apologetic error messages influence users&rsquo / self-appraisals of performances and actual performances in the computerized environment. For the study, an online instructional material which includes deliberate design problems leading to user frustration was developed. The study is comprised of three phases. In the first phase, based on the CCSARP (Cross-Cultural Study of Speech Act Realization Patterns) coding manual and the studies conducted with the framework provided by the manual, apology strategy sequences were elicited from Turkish participants. Two of these apology strategy sequences were selected for producing two apology error messages. In addition to these apology messages, one plain computer error message was also developed for experimental control. The second phase of the study was conducted to determine whether these three messages were perceived as apologies. It was found out that the two apology messages were perceived as apologies and the plain computer message was not perceived as an apology. In the third phase these three messages were used to investigate the relationship between mood, self-appraisal of performance and actual performance after the transmission of the apologetic error messages. The findings of this study show that the frequencies of apology strategies preferred in the computerized environment are similar with those utilized in the social context. Statistical analyses also reveal that the influence of apology messages on self-appraisal of performance depends on participants&rsquo / mood state and the contents of the apology messages.

In this study, a P300 based Brain-Computer Interface (BCI) system design is realized by the implementation of the Spelling Paradigm. The main challenge in these systems is to improve the speed of the prediction mechanisms by the application of different signal processing and pattern classification techniques in BCI problems. The thesis study includes the design and implementation of a 10 channel Electroencephalographic (EEG) data acquisition system to be practically used in BCI applications. The electrical measurements are realized with active electrodes for continuous EEG recording. The data is transferred via USB so that the device can be operated by any computer. v Wiener filtering is applied to P300 Speller as a signal enhancement tool for the first time in the literature. With this method, the optimum temporal frequency bands for user specific P300 responses are determined. The classification of the responses is performed by using Support Vector Machines (SVM&rsquo / s) and Bayesian decision. These methods are independently applied to the row-column intensification groups of P300 speller to observe the differences in human perception to these two visual stimulation types. It is observed from the investigated datasets that the prediction accuracies in these two groups are different for each subject even for optimum classification parameters. Furthermore, in these datasets, the classification accuracy was improved when the signals are preprocessed with Wiener filtering. With this method, the test characters are predicted with 100% accuracy in 4 trial repetitions in P300 Speller dataset of BCI Competition II. Besides, only 8 trials are needed to predict the target character with the designed BCI system.

This thesis study focuses on the realization of an online cue based Motor Imagery (MI) Brain Computer Interface (BCI). For this purpose, some signal processing and classification methods are investigated. Specifically, several time-spatial-frequency methods, namely the Short Time Fourier Transform (STFT), Common Spatial Frequency Patterns (CSFP) and the Morlet Transform (MT) are implemented on a 2-class MI BCI system. Distinction Sensitive Learning Vector Quantization (DSLVQ) method is used as a feature selection method. The performance of these methodologies is evaluated with the linear and nonlinear Support Vector Machines (SVM), Multilayer Perceptron (MLP) and Naive Bayesian (NB) classifiers. The methodologies are tested on BCI Competition IV dataset IIb and an average kappa value of 0.45 is obtained on the dataset. According to the classification results, the algorithms presented here obtain the 4th level in the competition as compared to the other algorithms in the competition. Offline experiments are performed in METU Brain Research Laboratories and Hacettepe Biophysics Department on two subjects with the original cue-based MI BCI paradigm. Average prediction accuracy of the methods on a 2-class BCI is evaluated to be 76.26% in these datasets. Furthermore, two online BCI applications are developed: the ping-pong game and the electrical wheelchair control. For these applications, average classification accuracy is found to be 70%. During the offline experiments, the performance of the developed system is observed to be highly dependent on the subject training and experience. According to the results, the EEG channels P3 and P4, which are considered to be irrelevant with the motor imagination, provided the best classification performance on the offline experiments. Regarding the observations on the experiments, this process is related to the stimulation mechanism in the cue based applications and consequent visual evoking effects on the subjects.