Προτείνουμε μια προσέγγιση για να αναλύσουμε τα δεδομένα που συλλέγουμε από το παράδειγμα του Ορθογράφου P300, χρησιμοποιώντας την τεχνική μηχανικής μάθησης, support vector machines. Στο συγκεκριμένο πλαίσιο κατηγοριοποίησης, πετύχαμε την σωστή λύση μετά από πέντε επαναλήψεις. Ενώ η κατηγοριοποίηση στους διαγωνισμούς BCI είναι για offline ανάλυση, η προσέγγιση μας μπορεί να θεωρηθεί ως μια online λύση για τον πραγματικό κόσμο. Είναι γρήγορη, απαιτεί λίγες (λιγότερες από 10) θέσεις ηλεκτροδίων, απαιτεί μόνο ένα μικρό ποσοστό προεπεξεργασίας και η επιλογή των τιμών για κρίσιμες παραμέτρους έχει αυτοματοποιηθεί. / We propose an approach to analyze data from the P300 speller paradigm using the machine-learning technique support vector machines. In a conservative classification scheme, we found the correct solution after five repetitions. While the classification within the BCI competition is designed for offline analysis, our approach is also well-suited for a real-world online solution: It is fast, requires few electrode positions (less than 10), demands only a small amount of preprocessing and selection of values for critical parameters is automated.

Τα συστήματα διεπαφών ανθρώπινου εγκεφάλου-υπολογιστή (BCIs: Brain-Computer Interfaces) απαιτούν την πραγματικού χρόνου, αποτελεσματική επεξεργασία των μετρήσεων των ηλεκτροεγκεφαλογραφικών (ΗΕΓ) σημάτων του χρήστη τους, προκειμένου να μεταφράσουν τις νοητικές διεργασίες/προθέσεις του σε σήματα ελέγχου εξωτερικών διατάξεων ή συστημάτων. Στο πλαίσιο της εργασίας αυτής μελετήθηκε το θεωρητικό υπόβαθρο του προβλήματος και αναλύθηκαν συνοπτικά οι κυριότερες τεχνικές που χρησιμοποιούνται σήμερα. Επιπρόσθετα, παρουσιάστηκε μία μέθοδος ταξινόμησης των νοητικών προθέσεων της αριστερής και δεξιάς κίνησης των χεριών ενός χρήστη η οποία εφαρμόστηκε σε πραγματικά ιατρικά δεδομένα. Η εξαγωγή των χαρακτηριστικών που διαφοροποιούνται μεταξύ των δύο καταστάσεων βασίστηκε σε πληροφορίες του πεδίου χρόνου-συχνότητας, οι οποίες αντλούνται με το φιλτράρισμα των ακατέργαστων ΗΕΓ δεδομένων και με τη βοήθεια των αιτιατών κυματιδίων Morlet, ενώ για την επακόλουθη ταξινόμηση των χαρακτηριστικών αναπτύχθηκαν και συγκρίθηκαν δύο αξιόπιστες μέθοδοι. Η πρώτη αφορά στη δημιουργία πιθανοθεωρητικών προτύπων κανονικής κατανομής για κάθε κατηγορία πρόθεσης κίνησης, με την τελική απόφαση ταξινόμησης να λαμβάνεται με εφαρμογή του απλού ταξινομητή του Bayes, ενώ η δεύτερη δημιουργεί ένα πρότυπο ταξινόμησης με βάση το θεωρητικό πλαίσιο των Μηχανών Διανυσμάτων Υποστήριξης (SVM). Στόχος του προβλήματος της δυαδικής ταξινόμησης είναι να αποφασίζεται σε ποια από τις δύο κατηγορίες ανήκει μία δεδομένη νοητική πρόθεση όσο το δυνατόν ταχύτερα και αξιόπιστα, έτσι ώστε ο σχεδιαζόμενος αλγόριθμος να εξυπηρετήσει ένα πλαίσιο ανατροφοδότησης της τελικής απόφασης στο χρήστη σε συνθήκες πραγματικού χρόνου. / Brain-Computer Interfaces (BCIs) demand the efficient processing of EEG data in order to translate one's thought or wish into a control signal that can be applied as input to external devices. Here we present a method to classify left from right hand movements, by extracting features from the data with Morlet wavelets and classifying with two different models, SVMs and Naive Bayes Classifier.

Orientador: Aparecido Augusto de Carvalho / Resumo: There are, nowadays, about 110 million people in the world who live with some type of severe motor disability. Specifically in Brazil, about 2.2% of the population are estimated to live with a condition of difficult locomotion. Aiming to help these people, a vast variety of devices, techniques and services are currently being developed. Among those, one of the most complex and challenging techniques is the study and development of Brain-Computer Interfaces (BCIs). BCIs are systems that allow the user to communicate with the external world controlling devices without the use of muscles or peripheral nerves, using only his decoded brain activity. To achieve this, there is a need to develop robust pattern recognition systems, that must be able to detect the user’s intention through electroencephalography (EEG) signals and activate the corresponding output with reliable accuracy and within the shortest possible processing time. In this work, different EEG signal processing techniques were studied, and it is presented the development of a EEG under visual stimulation (Steady-State Visual Evoked Potentials - SSVEP) pattern recognition system. Using only Open Source tools and Python programming language, modules to manage datasets, reduce noise, extract features and perform classification of EEG signals were developed, and a comparative study of different techniques was performed, using filter banks and Discrete Wavelet Transforms (DWT) as feature extraction approach... (Resumo completo, clicar acesso eletrônico abaixo) / Mestre

[pt] Os acidentes vasculares cerebrais (AVC) são um tipo de lesão cerebral que afeta mais de 750.000 pessoas anualmente. Aproximadamente metade dos pacientes com diagnóstico de AVC sofre dano crônico da função da extremidade superior. A reabilitação ajuda o paciente a manter as habilidades e recuperar algumas das perdidas. Uma órtese automatizada de mão é uma potencial ferramenta terapêutica para tratamento da debilidade da parte distal da extremidade superior, sendo uma abordagem promissora para melhorar os comportamentos motores perdidos em pacientes com AVC. Hoje em dia, a maioria dispositivos de assistência para o movimento de mão desenvolvidos são caros, pesados e volumosos, além de, muitas vezes, não oferecer controle sobre toda a sua operação. Este trabalho propõe e desenvolve um novo sistema de atuação que pode contribuir para a criação de um dispositivo de assistência a movimentação da mão, que seja fácil de operar, portátil, de baixo custo e totalmente controlado. O sistema é controlado por meio de um software com uma interface gráfica de usuário que permite que os usuários configurem os parâmetros do sistema de acordo com suas capacidades específicas. O software de controle também permite a comunicação com uma Interface Cérebro-Computador, que possibilita sincronizar os movimentos do sistema de acordo com as intenções do usuário, aumentando as taxas de recuperação dos pacientes. / [en] Strokes are a type of brain injury that affects over 750,000 people annually. Approximately half of the patients diagnosed with a stroke suffer chronic damage of the upper extremity function. Rehabilitation helps the patient to keep abilities and recover some of the lost ones, to become more independent. Hand rehabilitation exercises aim at assisting patients so they can regain finger mobility and strength. An actuated hand orthosis is a potential therapy tool for distal upper extremity weakness, since it can offer a promising approach to improve lost motor behaviors in stroke patients. Nowadays, hand movement assisting devices are developed for research applications, and even for commercial purposes. However, most of them are expensive, heavy and bulky or do not offer control over their whole operation. This work proposes and develops a new actuation system that can contribute to the construction of an easy to operate, portable, low cost and fully controlled hand movement assisting device. Several advances towards the creation of an actuated hand orthosis were achieved, leading to the creation of an electromechanical system capable of assisting finger movement along their full range of motion, while keeping low weight in the distal upper limb. The system is controlled by a computer software with a graphic user interface that allows the users to configure the system s parameters to their specific needs. The control software also allows the communication with a Brain-Computer Interface (BCI) in order to synchronize the system s movements with the user intentions, improving the recovery rates.

Restorer la faculté de parler chez des personnes paralysées et aphasiques pourrait être envisagée via l’utilisation d’une interface cerveau-machine permettant de contrôler un synthétiseur de parole en temps réel. L’objectif de cette thèse était de développer trois aspects nécessaires à la mise au point d’une telle preuve de concept.Premièrement, un synthétiseur permettant de produire en temps-réel de la parole intelligible et controlé par un nombre raisonable de paramètres est nécessaire. Nous avons choisi de synthétiser de la parole à partir des mouvements des articulateurs du conduit vocal. En effet, des études récentes ont suggéré que l’activité neuronale du cortex moteur de la parole pourrait contenir suffisamment d’information pour décoder la parole, et particulièrement ses propriété articulatoire (ex. l’ouverture des lèvres). Nous avons donc développé un synthétiseur produisant de la parole intelligible à partir de données articulatoires. Dans un premier temps, nous avons enregistré un large corpus de données articulatoire et acoustiques synchrones chez un locuteur. Ensuite, nous avons utilisé des techniques d’apprentissage automatique, en particulier des réseaux de neurones profonds, pour construire un modèle permettant de convertir des données articulatoires en parole. Ce synthétisuer a été construit pour fonctionner en temps réel. Enfin, comme première étape vers un contrôle neuronal de ce synthétiseur, nous avons testé qu’il pouvait être contrôlé en temps réel par plusieurs locuteurs, pour produire de la parole inetlligible à partir de leurs mouvements articulatoires dans un paradigme de boucle fermée.Deuxièmement, nous avons étudié le décodage de la parole et de ses propriétés articulatoires à partir d’activités neuronales essentiellement enregistrées dans le cortex moteur de la parole. Nous avons construit un outil permettant de localiser les aires corticales actives, en ligne pendant des chirurgies éveillées à l’hôpital de Grenoble, et nous avons testé ce système chez deux patients atteints d’un cancer du cerveau. Les résultats ont montré que le cortex moteur exhibe une activité spécifique pendant la production de parole dans les bandes beta et gamma du signal, y compris lors de l’imagination de la parole. Les données enregistrées ont ensuite pu être analysées pour décoder l’intention de parler du sujet (réelle ou imaginée), ainsi que la vibration des cordes vocales et les trajectoires des articulateurs principaux du conduit vocal significativement au dessus du niveau de la chance.Enfin, nous nous sommes intéressés aux questions éthiques qui accompagnent le développement et l’usage des interfaces cerveau-machine. Nous avons en particulier considéré trois niveaux de réflexion éthique concernant respectivement l’animal, l’humain et l’humanité. / Restoring natural speech in paralyzed and aphasic people could be achieved using a brain-computer interface controlling a speech synthesizer in real-time. The aim of this thesis was thus to develop three main steps toward such proof of concept.First, a prerequisite was to develop a speech synthesizer producing intelligible speech in real-time with a reasonable number of control parameters. Here we chose to synthesize speech from movements of the speech articulators since recent studies suggested that neural activity from the speech motor cortex contains relevant information to decode speech, and especially articulatory features of speech. We thus developed a speech synthesizer that produced intelligible speech from articulatory data. This was achieved by first recording a large dataset of synchronous articulatory and acoustic data in a single speaker. Then, we used machine learning techniques, especially deep neural networks, to build a model able to convert articulatory data into speech. This synthesizer was built to run in real time. Finally, as a first step toward future brain control of this synthesizer, we tested that it could be controlled in real-time by several speakers to produce intelligible speech from articulatory movements in a closed-loop paradigm.Second, we investigated the feasibility of decoding speech and articulatory features from neural activity essentially recorded in the speech motor cortex. We built a tool that allowed to localize active cortical speech areas online during awake brain surgery at the Grenoble Hospital and tested this system in two patients with brain cancer. Results show that the motor cortex exhibits specific activity during speech production in the beta and gamma bands, which are also present during speech imagination. The recorded data could be successfully analyzed to decode speech intention, voicing activity and the trajectories of the main articulators of the vocal tract above chance.Finally, we addressed ethical issues that arise with the development and use of brain-computer interfaces. We considered three levels of ethical questionings, dealing respectively with the animal, the human being, and the human species.

The autism treatment status quo was reviewed and accompanied by a narrative contextualizing past and present progress with my younger brother Jim’s journey with the condition, sharing proposed next steps for bettering the current state of affairs in the space. The impetus for this piece was to share in the lessons of Jim’s life thus far and the revelations of those who have supported him, as well as to determine ways to create more impactful, lasting change in the limited window of early intervention therapy whilst empowering individuals on the spectrum to optimize for their skills and talents rather than just simply mitigating the downsides of autism spectrum disorder. Feedback as to how to improve the prevailing course of treatment: (education and therapy) was solicited by leading experts in the fields of Applied Behavior Analysis (ABA), Electroencephalography (EEG), and autism more generally in the context of politics, insurability, and savant syndrome and splinter skills. The advice of the various vertical experts were synthesized and distilled into a new proposed course of treatment which were submitted to all respective experts for further feedback and review prior to publication. It was discovered that there is significant feedback to suggest that the prevailing wisdom that splinter skills and savant syndrome are found in a small minority of individuals with autism spectrum disorder may not be true and that further research is warranted that would implement the new proposed course of treatment and attempt to unlock the talents and gifts of these individuals consistent with the success we encountered raising Jim. While our methods were resource-intensive and conducted manually with many hours of intensive in-home therapy, there is significant feedback to suggest that a technology-driven approach to reforming autism treatment would achieve same or greater results with far fewer resources in the near and long term. By unlocking the greatest minds of our society (the majority of savants have historically been autistic) to take on the greatest challenges of our time, we can rapidly accelerate the progress of humanity and exponentially better the trajectory of society’s future at the global scale.

O uso de sistemas denominados Brain Computer Interface, ou simplesmente BCI, para controle de dispositivos tem gerado cada vez mais trabalhos de análise de sinais de EEG, principalmente devido ao fato do desenvolvimento tecnológico dos sistemas de processamento de dados, trazendo novas perspectiva de desenvolvimento de equipamentos que auxiliem pessoas com debilidades motoras. Neste trabalho é abordado o comportamento dos classificadores LDA (Discriminante Linear de Fisher) e o classificador Naive Bayes para classificação de movimento de mão direita e mão esquerda a partir da aquisição de sinais eletroencefalográficos. Para análise destes classificadores foram utilizadas como características de entrada a energia de trechos do sinal filtrados por um passa banda com frequências dentro dos ritmos sensório-motor e também foram utilizadas componentes de energia espectral através do periodograma modificado de Welch. Como forma de pré-processamento também é apresentado o filtro espacial Common Spatial Pattern (CSP) de forma a aumentar a atividade discriminativa entre as classes de movimento. Foram obtidas taxas de acerto de até 70% para a base de dados geradas neste trabalho e de até 88% utilizando a base de dados do BCI Competition II, taxas de acertos compatíveis com outros trabalhos na área. / Brain Computer Interface (BCI) systems usage for controlling devices has increasingly generated research on EEG signals analysis, mainly because the technological development of data processing systems has been offering a new perspective on developing equipment to assist people with motor disability. This study aims to examine the behavior of both Fisher's Linear Discriminant (LDA) and Naive Bayes classifiers in determining both the right and left hand movement through electroencephalographic signals. To accomplish this, we considered as input feature the energy of the signal trials filtered by a band pass with sensorimotor rhythm frequencies; spectral power components from the Welch modified periodogram were also used. As a preprocessing form, the Common Spatial Pattern (CSP) filter was used to increase the discriminative activity between classes of movement. The database created from this study reached hit rates of up to 70% while the BCI Competition II reached hit rates up to 88%, which is consistent with the literature.

Brain-computer interfaces (BCIs) blend methods and concepts researched by cognitive neuroscience, electrophysiology, computer science and engineering, resulting in systems of bi-directional information exchange directly between brain and computer. BCIs contribute to medical applications that restore communication and mobility for disabled patients and provide new forms of sending information to devices for enhancement and entertainment. Virtual reality (VR) introduces humans into a computer-generated world, tackling immersion and involvement. VR technology extends the classical multimedia experience, as the user is able to move within the environment, interact with other virtual participants, and manipulate objects, in order to generate the feeling of presence. This essay presents the possibilities of merging BCI with VR and the challenges to be tackled in the future. Current attempts to combine BCI and VR technology have shown that VR is a useful tool to test the functioning of BCIs, with safe, controlled and realistic experiments; there are better outcomes for VR and BCI combinations used for medical purposes compared to solely BCI training; and, enhancement systems for healthy users seem promising with VR-BCIs designed for home users. Future trends include brain-to-brain communication, sharing of several users’ brain signals within the virtual environment, and better and more efficient interfaces.

Submitted by Renato de Macedo Giovanini null (renato81243@aluno.feis.unesp.br) on 2017-09-25T14:52:54Z No. of bitstreams: 1 dissertacao_renato_de_macedo_giovanini_2017_final.pdf: 10453769 bytes, checksum: 7f7e2415a0912fae282affadea2685b8 (MD5) / Approved for entry into archive by Monique Sasaki (sayumi_sasaki@hotmail.com) on 2017-09-27T20:24:55Z (GMT) No. of bitstreams: 1 giovanini_rm_me_ilha.pdf: 10453769 bytes, checksum: 7f7e2415a0912fae282affadea2685b8 (MD5) / Made available in DSpace on 2017-09-27T20:24:55Z (GMT). No. of bitstreams: 1 giovanini_rm_me_ilha.pdf: 10453769 bytes, checksum: 7f7e2415a0912fae282affadea2685b8 (MD5) Previous issue date: 2017-08-18 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / There are, nowadays, about 110 million people in the world who live with some type of severe motor disability. Specifically in Brazil, about 2.2% of the population are estimated to live with a condition of difficult locomotion. Aiming to help these people, a vast variety of devices, techniques and services are currently being developed. Among those, one of the most complex and challenging techniques is the study and development of Brain-Computer Interfaces (BCIs). BCIs are systems that allow the user to communicate with the external world controlling devices without the use of muscles or peripheral nerves, using only his decoded brain activity. To achieve this, there is a need to develop robust pattern recognition systems, that must be able to detect the user’s intention through electroencephalography (EEG) signals and activate the corresponding output with reliable accuracy and within the shortest possible processing time. In this work, different EEG signal processing techniques were studied, and it is presented the development of a EEG under visual stimulation (Steady-State Visual Evoked Potentials - SSVEP) pattern recognition system. Using only Open Source tools and Python programming language, modules to manage datasets, reduce noise, extract features and perform classification of EEG signals were developed, and a comparative study of different techniques was performed, using filter banks and Discrete Wavelet Transforms (DWT) as feature extraction approaches, and the classifiers K-Nearest Neighbors, Multilayer Perceptron and Random Forests. Using DWT approach with Random Forest and Multilayer Perceptron classifiers, high accuracy rates up to 92 % were achieved in deeper decomposition levels. Then, the small-size microcomputer Raspberry Pi was used to perform time processing evaluation, obtaining short processing times for every classifiers. This work is a preliminary study of BCIs at the Laboratório de Instrumentação e Engenharia Biomédica, and, in the future, the system here presented may be part of a complete SSVEP-BCI system. / Existem, atualmente, cerca de 110 milhões de pessoas no mundo que vivem com algum tipo de deficiência motora severa. Especificamente no Brasil, é estimado que cerca de 2.2% da população conviva com alguma condição que dificulte a locomoção. Com o intuito de auxiliar tais pessoas, uma grande variedade de dispositivos, técnicas e serviços são atualmente desenvolvidos. Dentre elas, uma das técnicas mais complexas e desafiadoras é o estudo e o desenvolvimento de Interfaces Cérebro-Computador (ICMs). As ICMs são sistemas que permitem ao usuário comunicar-se com o mundo externo, controlando dispositivos sem o uso de músculos ou nervos periféricos, utilizando apenas sua atividade cerebral decodificada. Para alcançar isso, existe a necessidade de desenvolvimento de sistemas robustos de reconhecimento de padrões, que devem ser capazes de detectar as intenções do usuáro através dos sinais de eletroencefalografia (EEG) e ativar a saída correspondente com acurácia confiável e o menor tempo de processamento possível. Nesse trabalho foi realizado um estudo de diferentes técnicas de processamento de sinais de EEG, e o desenvolvimento de um sistema de reconhecimento de padrões de sinais de EEG sob estimulação visual (Potenciais Evocados Visuais de Regime Permanente - PEVRP). Utilizando apenas técnicas de código aberto e a linguagem Python de programação, foram desenvolvidos módulos para realizar o gerenciamento de datasets, redução de ruído, extração de características e classificação de sinais de EEG, e um estudo comparativo de diferentes técnicas foi realizado, utilizando-se bancos de filtros e a Transformada Wavelet Discreta (DWT) como abordagens de extração de características, e os classificadores K-Nearest Neighbors, Perceptron Multicamadas e Random Forests. Utilizando-se a DWT juntamente com Random Forests e Perceptron Multicamadas, altas taxas de acurácia de até 92 % foram obtidas nos níveis mais profundos de decomposição. Então, o computador Raspberry Pi, de pequenas dimensões, foi utilizado para realizar a avaliação do tempo de processamento, obtendo um baixo tempo de processamento para todos os classificadores. Este trabalho é um estudo preliminar em ICMs no Laboratório de Instrumentação e Engenharia Biomédica e, no futuro, pode ser parte de um sistema ICM completo.

O uso de sistemas denominados Brain Computer Interface, ou simplesmente BCI, para controle de dispositivos tem gerado cada vez mais trabalhos de análise de sinais de EEG, principalmente devido ao fato do desenvolvimento tecnológico dos sistemas de processamento de dados, trazendo novas perspectiva de desenvolvimento de equipamentos que auxiliem pessoas com debilidades motoras. Neste trabalho é abordado o comportamento dos classificadores LDA (Discriminante Linear de Fisher) e o classificador Naive Bayes para classificação de movimento de mão direita e mão esquerda a partir da aquisição de sinais eletroencefalográficos. Para análise destes classificadores foram utilizadas como características de entrada a energia de trechos do sinal filtrados por um passa banda com frequências dentro dos ritmos sensório-motor e também foram utilizadas componentes de energia espectral através do periodograma modificado de Welch. Como forma de pré-processamento também é apresentado o filtro espacial Common Spatial Pattern (CSP) de forma a aumentar a atividade discriminativa entre as classes de movimento. Foram obtidas taxas de acerto de até 70% para a base de dados geradas neste trabalho e de até 88% utilizando a base de dados do BCI Competition II, taxas de acertos compatíveis com outros trabalhos na área. / Brain Computer Interface (BCI) systems usage for controlling devices has increasingly generated research on EEG signals analysis, mainly because the technological development of data processing systems has been offering a new perspective on developing equipment to assist people with motor disability. This study aims to examine the behavior of both Fisher's Linear Discriminant (LDA) and Naive Bayes classifiers in determining both the right and left hand movement through electroencephalographic signals. To accomplish this, we considered as input feature the energy of the signal trials filtered by a band pass with sensorimotor rhythm frequencies; spectral power components from the Welch modified periodogram were also used. As a preprocessing form, the Common Spatial Pattern (CSP) filter was used to increase the discriminative activity between classes of movement. The database created from this study reached hit rates of up to 70% while the BCI Competition II reached hit rates up to 88%, which is consistent with the literature.