abstract: In the last 15 years, there has been a significant increase in the number of motor neural prostheses used for restoring limb function lost due to neurological disorders or accidents. The aim of this technology is to enable patients to control a motor prosthesis using their residual neural pathways (central or peripheral). Recent studies in non-human primates and humans have shown the possibility of controlling a prosthesis for accomplishing varied tasks such as self-feeding, typing, reaching, grasping, and performing fine dexterous movements. A neural decoding system comprises mainly of three components: (i) sensors to record neural signals, (ii) an algorithm to map neural recordings to upper limb kinematics and (iii) a prosthetic arm actuated by control signals generated by the algorithm. Machine learning algorithms that map input neural activity to the output kinematics (like finger trajectory) form the core of the neural decoding system. The choice of the algorithm is thus, mainly imposed by the neural signal of interest and the output parameter being decoded. The various parts of a neural decoding system are neural data, feature extraction, feature selection, and machine learning algorithm. There have been significant advances in the field of neural prosthetic applications. But there are challenges for translating a neural prosthesis from a laboratory setting to a clinical environment. To achieve a fully functional prosthetic device with maximum user compliance and acceptance, these factors need to be addressed and taken into consideration. Three challenges in developing robust neural decoding systems were addressed by exploring neural variability in the peripheral nervous system for dexterous finger movements, feature selection methods based on clinically relevant metrics and a novel method for decoding dexterous finger movements based on ensemble methods. / Dissertation/Thesis / Doctoral Dissertation Bioengineering 2017

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No description available.

Mechanical trauma to the brain, both big and small, and the method to protect the brain in its presence is a crucial field of research given the large population exposed to neuronal trauma daily and the benefit available through better understanding and injury prevention. A population of particular interest and risk are youth athletes in contact sports due to large accelerations they expose themselves to and their developing brains. To better monitor the risk these athletes are exposed to, their accumulation of head acceleration events (HAEs), a measure correlated with harmful neurological changes, was tracked over sport seasons. It was observed that few significant differences in HAEs accumulated existed between players of ages from middle school to high school, but there did exist a difference between sports with girls' soccer players accumulating fewer HAEs than football players. This highlights to risk youth athletes are exposed to and the importance of improved technique and individual player size. To better monitor HAEs for each individual, a novel head segmentation program was developed that extracts player specific geometries from a single T1 MRI scan that can improve the accuracy of HAE monitoring. Acceleration measures processed with individualized head model versus those using a standardized head model typically displayed higher accelerations, highlighting the need for individualized measure for accurate monitoring of HAEs and risk of neurological changes. In addition to the large accelerations present in contact sports, the small but constant strains produced by neural implants embedded in the brain is also an important field of neuro-mechanical research as the physical properties of neural implants have been found to contribute to the chronic immune response, a major factor preventing the widespread implementation of neural implants. To reduce the severity of the immune response and provide improved chronic functionality, researchers have varied neural implant design and materials, finding general trends but not precise relationships between the design factors and how they contribute the mechanical strain in the brain. Performing a large series of mechanical simulations and Cotter's sensitivity analyses, the relationships between neural design factors and the stain they produce in the brain was examined. It was found that the direction which neural implants are loaded contributes the most to the strain produced in the brain followed by the degree of bonding between the brain and the electrode. Directly related to the design of electrodes themselves, it was found that in most cases reducing the cross-sectional area of the probe resulted in a larger decrease of mechanical strain compared to softening the implant. Finally, a study was performed quantifying the resting micromotion of the brain utilizing a novel method of soft tissue micromotion measurement via microCT, applicable within the skull and the throughout the rest of the body.

Conventional functional electrical stimulation (FES) therapy, if provided shortly after an incomplete spinal cord injury, is able to help an individual to restore voluntary hand function. This is thought to occur through the induction of neuroplasticity. However, conventional FES therapy employs a push-button-based control scheme, which does not fully require the recipient to generate volitional movements. The first study in this thesis therefore sought to determine, in an early proof-of-concept test with able-bodied participants, whether control strategies which are triggered by volitional activity (including an electroencephalography-based brain-machine interface (BMI-FES) and an electromyogram-based control scheme (EMG-FES)) might provide greater benefits to hand function. The results offer relatively weak evidence to suggest that BMI-FES, and especially EMG-FES, were able to induce greater neuroplasticity than conventional treatments in the corticospinal tract leading to the hands, but that this did not immediately translate to more functional improvements such as maximum grip force. ii The second study in this thesis focussed on spinal associative stimulation (SAS), which involves paired stimulation pulses at both the head (via transcranial magnetic stimulation), and the wrist (via peripheral nerve stimulation). The purpose of this, as with the first study, was to induce neuroplasticity and upregulate the corticospinal tract leading to the hands. While limited research has suggested that it is possible to produce neuroplasticity through SAS, all such studies have provided stimulation at a fixed frequency of 0.1 or 0.2 Hz. The present study therefore sought to compare the effectiveness of a typical 0.1 Hz paradigm with a 1 Hz paradigm, and a paradigm which provided stimulation in 5 Hz “bursts”. None of the paradigms were able to successfully induce neuroplasticity in a consistent manner. The increased variability in this study as compared to the previous one, despite the nearly identical assessment methodology, suggests that responses to the SAS treatment may have been highly individual. This serves to highlight a potential limitation of the treatment, which is that its effectiveness may not be universal, but rather dependent on each specific recipient. This may be a challenge faced by SAS should it continue to be tested as a novel therapy.

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

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.

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Un accès aux représentations mentales. Voici une phrase qui pourrait bientôt devenir une réalité. La recherche sur les interfaces cerveau-Machines est un champ de recherche en plein essor. En particulier, de grandes avancées ont été réalisées pour permettre par exemple à des tétraplégiques de retrouver une relative autonomie en actionnant un bras robotique par l'activité de leur cerveau. L'équipe de Hochberg a mis en évidence un système permettant à une femme tétraplégique d'attraper une boisson et de boire. Cela montre la précision incroyable que peut avoir une prothèse artificielle pilotée par le cerveau. Ma thèse porte sur un aspect peu exploré des interfaces cerveau-Machines, celui des interfaces cerveau-Machines cognitives, c'est-À-Dire utilisant le contenu représentationnel intime de l'activité du cerveau. Son objectif est de démontrer, sur un modèle primate non-Humaine, la possibilité d'accéder en temps-Réel à ce type de contenu complexe, y compris dans un environnement en perpétuel changement. L'adaptation des interfaces cerveau-Machines dans le monde réel, où nous sommes constamment confrontés à de nouvelles informations, est critique pour son fonctionnement. Un autre aspect, très important, porte sur l'exploration et la compréhension du système nerveux au niveau populationnel en utilisant des méthodes similaires à celles utilisées pour extraire de l'information dans les interfaces cerveau-Machines. Cela nous permet d'étudier le contenu instantané et sa dynamique dans l'évolution du temps. En résumé, nous démontrons la faisabilité d'accéder en temps-Réel à des informations complexes de l'attention spatiale et de la perception visuelle. Cet accès en temps-Réel n'est que peu affecté par un environnement qui change. Le potentiel de ce type d'interfaces cerveau-Machines est immense en vue du traitement de pathologies neurologiques aigües (suite à des accidents cérébraux vasculaires ou suite à des traumatismes accidentés) ou neurodégénératives (dans la maladie d'Alzheimer ou de Parkinson, pour ne parler que des plus connues) / The field of invasive Brain Machine Interfaces (iBMI) has during the last ten years proven its enormous potential in restoring movements in paralyzed patients. The present doctoral thesis introduces a new dimension to this field by using complex cognitive behavior to drive an iBMI. In this respect, visual processes including spatial attention and perception are of special interest. This thesis project has three principal objectives: first, show the feasibility of decoding cognitive information in an offline setup. Second, evaluate the decoding of cognitive information in a real time experimental setup and third, investigate the impact of this setup in a changing environment, this both from the perspective of driving real time brain-Machine interfaces and that of understanding distributed populational neuronal codes. In line with the first objective of this thesis, an evaluation of several different classification techniques has been carried out in order to choose the best suited method for reading out cognitive information. The study provides evidence that visual information can be read out with similar performance as cognitive information. This study is the first study aiming at explicitly comparing the read out of sensory and cognitive information. The two last objectives of the present thesis are carried out on data from a new real-Time experimental setup. First we demonstrate the feasibility of real-Time readout of spatial attention and perception and we bring about a novel understanding about these two cognitive processes. Second, we show that in a changing environment, remarkable reconfigurations of prefrontal neural populations occur under certain contexts while left unaffected by other contexts. This Ph.D. thesis has taken the field of cognitive brain-Machine interfaces one step further by establishing the impact on spatial attention and perception of a changing environment. Facing the many neurological and neurodegenerative pathologies existing today, this thesis provides a steady ground for the continuation of research in this area