Made available in DSpace on 2016-03-15T19:39:55Z (GMT). No. of bitstreams: 1 Olivia Morgan Lapenta.pdf: 1151745 bytes, checksum: 91e51a2bad20664489c6aae6c59924b6 (MD5) Previous issue date: 2012-08-17 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The Mental Simulation theory suggests activation of the motor network during imagery and execution of movements, similarly to the activation during observation and execution of actions, which is mediated by the Mirror Neuron System. This activation can be measured using eletroencefalography register of Mu rhythm suppression. It is propose that motor network activation and therefore increase of cortical excitability at primary motor cortex and Mu dessynchronization are due to premotor Miror-Neuron System inputs. Transcranial direct current stimulation is a neuromodulation technique that induce facilitation and inhibition of neural firing leading to enhance or decrease in cortical excitability, respectively. Thus, we propose to evaluate the polarity dependent effects of this technique in the Mu rhythm during biological and non-biological movements observation and imagery tasks. Therefore we applied anodal, cathodal and sham stimulation in 21 male subjects (mean age 23.8+3,06), over left primary motor cortex (2mA for 20min) and immediately after we registered the electroencephalography considering the electrodes C3, C4 and surrounding C3 and C4 and Cz. Analyses of C3 and C4 showed significant effects according to Movement (p=0.005), and also for the interactions between type of stimulation and hemisphere (p=0.04) and type of stimulation, movement and hemisphere (p=0.02). Surrounding electrodes analyses revealed significant effect for the interaction between stimulation type, task condition and movement type (p=0.03). Thus, the main findings of this study were i. Mu suppression for biological movement (in both imagery and observation) of the hand region in the contralateral hemisphere after sham stimulation, ii. reverse effect for the surrounding electrodes during imagery condition and iii. polarity-dependent neuromodulation of the Mu rhythm. The results are discussed considering focal ERD/ surrounding ERS according to the type of task. We concluded that there are contralateral focal Mu dessynchronization during observation and imagery of biological movements together with syncronizarion of the motor areas not involved in the task only for the imagery condition and that transcranial direct current stimulation has a significant effect under the entire electrode and according to the applied polarity. The use of transcranial direct current stimulation followed by observation and imagery tasks might be an interesting intervention strategy for disturbances involving motor ability impairment as well as deficits related to imitation and comprehension of other s actions. / A teoria de simulação mental sugere ativação da rede neural motora durante mentalização e execução de movimentos, de maneira análoga à ativação em observação e execução de ações, o que é mediado pelo Sistema de Neurônios-Espelho. Esta ativação pode ser mensurada por supressão do ritmo Mu registrado por eletroencefalografia. É proposto que a ativação de áreas motoras e, portanto, o aumento de excitabilidade cortical em cortex motor primário e a dessincronização do ritmo Mu ocorram em consequência de insumo proveniente do Sistema Neurônios-Espelho pré-motor. A estimulação transcraniana por corrente contínua consiste numa técnica de neuromodulação por facilitação e inibição de disparo neuronal levando a aumento e redução de excitabilidade cortical, respectivamente. Assim, foi proposto avaliar os efeitos polaridade dependentes desta técnica sobre ritmo Mu durante tarefas de observação e mentalização de movimentos biológicos e não biológicos. Para tal, aplicamos estimulação anódica, catódica e placebo em 21 homens destros (idade média de 23.8+3,06), sobre córtex motor primário esquerdo (2mA por 20min) e, em seguida foi feito o registro eletroencefalográfico considerando os eletrodos C3, C4 e entorno de C3 e C4 e Cz. A análise de C3 e C4 apresentou efeitos significativos quanto ao tipo de Movimento (p=0.005) e ainda quanto as interações entre tipo de estimulação e hemisfério (p=0.04) e tipo de estimulação, de movimento e hemisfério (p=0.02). A análise dos eletrodos do entorno revelou efeito significativo para a interação entre tipo de estimulação, condição da tarefa e tipo de movimento (p=0.03). Assim, os principais achados do estudo foram i. supressão de Mu para movimento biológico (em mentalização e observação) da região da mão em hemisfério contralateral após estimulação placebo, ii. efeitos inversos para eletrodos de entorno em condição de mentalização e iii. neuromodulação polaridade dependente de ritmo Mu. Os resultados de oscilação de Mu são discutidos considerando ERD focal/ ERS entorno de acordo com o tipo de tarefa. Concluímos que há dessincronização contralateral focal de Mu durante observação e mentalização de movimentos biológicos, acompanhada por sincronização de áreas motoras não envolvidas na tarefa apenas na condição de mentalização e que a estimulação transcraniana por corrente contínua tem efeito sob toda a superfície do eletrodo e difere de acordo com a polaridade aplicada. O uso da estimulação transcraniana por corrente contínua combinada com tarefas de observação e mentalização pode conferir uma estratégia interessante de intervenção em distúrbios envolvendo comprometimento das habilidades motoras bem como comprometimento de habilidades de imitação e compreensão das ações do outro.

[pt] Interfaces Cérebro Computador (BCIs) são sistemas artificiais que permitem a interação entre a pessoa e seu ambiente empregando a tradução de sinais elétricos cerebrais como controle para qualquer dispositivo externo. Um Sistema de neuroreabilitação baseado em EEG pode combinar portabilidade e baixo custo com boa resolução temporal e nenhum risco para a vida do usuário. Este sistema pode estimular a plasticidade cerebral, desde que ofereça confiabilidade no reconhecimento das tarefas de imaginação motora realizadas pelo usuário. Portanto, o objetivo deste trabalho é o projeto de um sistema de aprendizado de máquinas que, baseado no sinal de EEG de somente dois eletrodos, C3 e C4, consiga classificar tarefas de imaginação motora com alta acurácia, robustez às variações do sinal entre experimentos e entre sujeitos, e tempo de processamento razoável. O sistema de aprendizado de máquina proposto é composto de quatro etapas principais: pré-processamento, extração de atributos, seleção de atributos, e classificação. O pré-processamento e extração de atributos são implementados mediante a extração de atributos estatísticos, de potência e de fase das sub-bandas de frequência obtidas utilizando a Wavelet Packet Decomposition. Já a seleção de atributos é efetuada por um Algoritmo Genético e o modelo de classificação é constituído por um Sistema de Múltiplos Classificadores, composto por diferentes classificadores, e combinados por uma rede neural Multi-Layer Perceptron. O sistema foi testado em seis sujeitos de bases de dados obtidas das Competições de BCIs e comparados com trabalhos benchmark da literatura, superando os resultados dos outros métodos. Adicionalmente, um sistema real de BCI para neurorehabilitação foi projetado, desenvolvido e testado, produzindo também bons resultados. / [en] Brain Computer Interfaces (BCIs) are artificial systems that allow the interaction between a person and their environment using the translated brain electrical signals to control any external device. An EEG neurorehabilitation system can combine portability and affordability with good temporal resolution and no health risks to the user. This system can stimulate the brain plasticity, provided that the system offers reliability on the recognition of the motor imagery (MI) tasks performed by the user. Therefore, the aim of this work is the design of a machine learning system that, based on the EEG signal from only C3 and C4 electrodes, can classify MI tasks with high accuracy, robustness to trial and inter-subject signal variations, and reasonable processing time. The proposed machine learning system has four main stages: preprocessing, feature extraction, feature selection, and classification. The preprocessing and feature extraction are implemented by the extraction of statistical, power and phase features of the frequency sub-bands obtained by the Wavelet Packet Decomposition. The feature selection process is effectuated by a Genetic Algorithm and the classifier model is constituted by a Multiple Classifier System composed by different classifiers and combined by a Multilayer Perceptron Neural Network as meta-classifier. The system is tested on six subjects from datasets offered by the BCIs Competitions and compared with benchmark works founded in the literature, outperforming the other methods. In addition, a real BCI system for neurorehabilitation is designed and tested, producing good results as well.

The goal of this investigation was to explore the efficacy of implementing a rehabilitation robot controlled by a noninvasive brain-computer interface (BCI) to influence brain plasticity and facilitate motor learning. The motivation of this project stemmed from the need to address the population of stroke survivors who have few or no options for therapy. A stroke occurs every 40 seconds in the United States and it is the leading cause of long-term disability [1-3]. In a country where the elderly population is growing at an astounding rate, one in six persons above the age of 55 is at risk of having a stroke. Internationally, the rates of strokes and stroke-induced disabilities are comparable to those of the United States [1, 4-6]. Approximately half of all stroke survivors suffer from immediate unilateral paralysis or weakness, 30-60% of which never regain function [1, 6-9]. Many individuals who survive stroke will be forced to seek institutional care or long-term assistance. Clinicians have typically implemented stroke rehabilitative treatment using active training techniques such as constraint induced movement therapy (CIMT) and robotic therapy [10-12]. Such techniques restore motor activity by forcing the movement of weakened limbs. That active engagement of the weakened limb movement stimulates neural pathways and activates the motor cortex, thus inducing brain plasticity and motor learning. Several studies have demonstrated that active training does in fact have an effect on the way the brain restores itself and leads to faster rehabilitation [10, 13-15]. In addition, studies involving mental practice, another form of rehabilitation, have shown that mental imagery directly stimulates the brain, but is not effective unless implemented as a supplemental to active training [16, 17]. Only stroke survivors retaining residual motor ability are able to undergo active rehabilitative training; the current selection of therapies has overlooked the significant population of stroke survivors suffering from severe control loss or complete paralysis [6, 10]. A BCI is a system or device that detects minute changes in brain signals to facilitate communication or control. In this investigation, the BCI was implemented through an electroencephalograph (EEG) device. EEG devices detect electrical brain signals transmitted through the scalp that corresponded with imagined motor activity. Within the BCI, a linear transformation algorithm converted EEG spectral features into control commands for an upper-limb rehabilitative robot, thus implementing a closed-looped feedback-control training system. The concept of the BCI-robot system implemented in this investigation may provide an alternative to current therapies by demonstrating the results of bypassing motor activity using brain signals to facilitate robotic therapy. In this study, 24 able-bodied volunteers were divided into two study groups; one group trained to use sensorimotor rhythms (SMRs) (produced by imagining motor activity) to control the movement of a robot and the other group performed the 'guided-imagery' task of watching the robot move without control. This investigation looked for contrasts between the two groups that showed that the training involved with controlling the BCI-robot system had an effect on brain plasticity and motor learning. To analyze brain plasticity and motor learning, EEG data corresponding to imagined arm movement and motor learning were acquired before, during, and after training. Features extracted from the EEG data consisted of frequencies in the 5-35Hz range, which produced amplitude fluctuations that were measurably significant during reaching. Motor learning data consisted of arm displacement measures (error) produced during an motor adaptation task performed daily by all subjects. The results of the brain plasticity analysis showed persistent reductions in beta activity for subjects in the BCI group. The analysis also showed that subjects in the Non-BCI group had significant reductions in mu activity; however, these results were likely due to the fact that different EEG caps were used in each stage of the study. These results were promising but require further investigation. The motor learning data showed that the BCI group out-performed non-BCI group in all measures of motor learning. These findings were significant because this was the first time a BCI had been applied to a motor learning protocol and the findings suggested that BCI had an influence on the speed at which subjects adapted to a motor learning task. Additional findings suggested that BCI subjects who were in the 40 and over age group had greater decreases in error after the learning phase of motor assessment. These finding suggests that BCI could have positive long term effects on individuals who are more likely to suffer from a stroke and possibly could be beneficial for chronic stroke patients. In addition to exploring the effects of BCI training on brain plasticity and motor learning this investigation sought to detect whether the EEG features produced during guided-imagery could differentiate between reaching direction. While the analysis presented in this project produced classification accuracies no greater than ~77%, it formed the basis of future studies that would incorporate different pattern recognition techniques. The results of this study show the potential for developing new rehabilitation therapies and motor learning protocols that incorporate BCI.

Brain-computer interfaces (BCI) have been used to control the gait of a virtual self-avatar with the aim of being used in gait rehabilitation. A BCI decodes the brain signals representing a desire to do something and transforms them into a control command for controlling external devices. The feelings described by the participants when they control a self-avatar in an immersive virtual environment (VE) demonstrate that humans can be embodied in the surrogate body of an avatar (ownership illusion). It has recently been shown that inducing the ownership illusion and then manipulating the movements of one’s self-avatar can lead to compensatory motor control strategies. In order to maximize this effect, there is a need for a method that measures and monitors embodiment levels of participants immersed in virtual reality (VR) to induce and maintain a strong ownership illusion. This is particularly true given that reaching a high level of both BCI performance and embodiment are inter-connected. To reach one of them, the second must be reached as well. Some limitations of many existing systems hinder their adoption for neurorehabilitation: 1- some use motor imagery (MI) of movements other than gait; 2- most systems allow the user to take single steps or to walk but do not allow both, which prevents users from progressing from steps to gait; 3- most of them function in a single BCI mode (cue-paced or self-paced), which prevents users from progressing from machine-dependent to machine-independent walking. Overcoming the aforementioned limitations can be done by combining different control modes and options in one single system. However, this would have a negative impact on BCI performance, therefore diminishing its usefulness as a potential rehabilitation tool. In this case, there will be a need to enhance BCI performance. For such purpose, many techniques have been used in the literature, such as providing modified feedback (whereby the presented feedback is not consistent with the user’s MI), sequential training (recalibrating the classifier as more data becomes available). This thesis was developed over 3 studies. The objective in study 1 was to investigate the possibility of measuring the level of embodiment of an immersive self-avatar, during the performing, observing and imagining of gait, using electroencephalogram (EEG) techniques, by presenting visual feedback that conflicts with the desired movement of embodied participants. The objective of study 2 was to develop and validate a BCI to control single steps and forward walking of an immersive virtual reality (VR) self-avatar, using mental imagery of these actions, in cue-paced and self-paced modes. Different performance enhancement strategies were implemented to increase BCI performance. The data of these two studies were then used in study 3 to construct a generic classifier that could eliminate offline calibration for future users and shorten training time. Twenty different healthy participants took part in studies 1 and 2. In study 1, participants wore an EEG cap and motion capture markers, with an avatar displayed in a head-mounted display (HMD) from a first-person perspective (1PP). They were cued to either perform, watch or imagine a single step forward or to initiate walking on a treadmill. For some of the trials, the avatar took a step with the contralateral limb or stopped walking before the participant stopped (modified feedback). In study 2, participants completed a 4-day sequential training to control the gait of an avatar in both BCI modes. In cue-paced mode, they were cued to imagine a single step forward, using their right or left foot, or to walk forward. In the self-paced mode, they were instructed to reach a target using the MI of multiple steps (switch control mode) or maintaining the MI of forward walking (continuous control mode). The avatar moved as a response to two calibrated regularized linear discriminant analysis (RLDA) classifiers that used the μ power spectral density (PSD) over the foot area of the motor cortex as features. The classifiers were retrained after every session. During the training, and for some of the trials, positive modified feedback was presented to half of the participants, where the avatar moved correctly regardless of the participant’s real performance. In both studies, the participants’ subjective experience was analyzed using a questionnaire. Results of study 1 show that subjective levels of embodiment correlate strongly with the power differences of the event-related synchronization (ERS) within the μ frequency band, and over the motor and pre-motor cortices between the modified and regular feedback trials. Results of study 2 show that all participants were able to operate the cued-paced BCI and the selfpaced BCI in both modes. For the cue-paced BCI, the average offline performance (classification rate) on day 1 was 67±6.1% and 86±6.1% on day 3, showing that the recalibration of the classifiers enhanced the offline performance of the BCI (p < 0.01). The average online performance was 85.9±8.4% for the modified feedback group (77-97%) versus 75% for the non-modified feedback group. For self-paced BCI, the average performance was 83% at switch control and 92% at continuous control mode, with a maximum of 12 seconds of control. Modified feedback enhanced BCI performances (p =0.001). Finally, results of study 3 show that the constructed generic models performed as well as models obtained from participant-specific offline data. The results show that there it is possible to design a participant-independent zero-training BCI. / Les interfaces cerveau-ordinateur (ICO) ont été utilisées pour contrôler la marche d'un égo-avatar virtuel dans le but d'être utilisées dans la réadaptation de la marche. Une ICO décode les signaux du cerveau représentant un désir de faire produire un mouvement et les transforme en une commande de contrôle pour contrôler des appareils externes. Les sentiments décrits par les participants lorsqu'ils contrôlent un égo-avatar dans un environnement virtuel immersif démontrent que les humains peuvent être incarnés dans un corps d'un avatar (illusion de propriété). Il a été récemment démontré que provoquer l’illusion de propriété puis manipuler les mouvements de l’égo-avatar peut conduire à des stratégies de contrôle moteur compensatoire. Afin de maximiser cet effet, il existe un besoin d'une méthode qui mesure et surveille les niveaux d’incarnation des participants immergés dans la réalité virtuelle (RV) pour induire et maintenir une forte illusion de propriété. D'autre part, atteindre un niveau élevé de performances (taux de classification) ICO et d’incarnation est interconnecté. Pour atteindre l'un d'eux, le second doit également être atteint. Certaines limitations de plusieurs de ces systèmes entravent leur adoption pour la neuroréhabilitation: 1- certains utilisent l'imagerie motrice (IM) des mouvements autres que la marche; 2- la plupart des systèmes permettent à l'utilisateur de faire des pas simples ou de marcher mais pas les deux, ce qui ne permet pas à un utilisateur de passer des pas à la marche; 3- la plupart fonctionnent en un seul mode d’ICO, rythmé (cue-paced) ou auto-rythmé (self-paced). Surmonter les limitations susmentionnées peut être fait en combinant différents modes et options de commande dans un seul système. Cependant, cela aurait un impact négatif sur les performances de l’ICO, diminuant ainsi son utilité en tant qu'outil potentiel de réhabilitation. Dans ce cas, il sera nécessaire d'améliorer les performances des ICO. À cette fin, de nombreuses techniques ont été utilisées dans la littérature, telles que la rétroaction modifiée, le recalibrage du classificateur et l'utilisation d'un classificateur générique. Le projet de cette thèse a été réalisé en 3 études, avec objectif d'étudier dans l'étude 1, la possibilité de mesurer le niveau d'incarnation d'un égo-avatar immersif, lors de l'exécution, de l'observation et de l'imagination de la marche, à l'aide des techniques encéphalogramme (EEG), en présentant une rétroaction visuelle qui entre en conflit avec la commande du contrôle moteur des sujets incarnés. L'objectif de l'étude 2 était de développer un BCI pour contrôler les pas et la marche vers l’avant d'un égo-avatar dans la réalité virtuelle immersive, en utilisant l'imagerie motrice de ces actions, dans des modes rythmés et auto-rythmés. Différentes stratégies d'amélioration des performances ont été mises en œuvre pour augmenter la performance (taux de classification) de l’ICO. Les données de ces deux études ont ensuite été utilisées dans l'étude 3 pour construire des classificateurs génériques qui pourraient éliminer la calibration hors ligne pour les futurs utilisateurs et raccourcir le temps de formation. Vingt participants sains différents ont participé aux études 1 et 2. Dans l'étude 1, les participants portaient un casque EEG et des marqueurs de capture de mouvement, avec un avatar affiché dans un casque de RV du point de vue de la première personne (1PP). Ils ont été invités à performer, à regarder ou à imaginer un seul pas en avant ou la marche vers l’avant (pour quelques secondes) sur le tapis roulant. Pour certains essais, l'avatar a fait un pas avec le membre controlatéral ou a arrêté de marcher avant que le participant ne s'arrête (rétroaction modifiée). Dans l'étude 2, les participants ont participé à un entrainement séquentiel de 4 jours pour contrôler la marche d'un avatar dans les deux modes de l’ICO. En mode rythmé, ils ont imaginé un seul pas en avant, en utilisant leur pied droit ou gauche, ou la marche vers l’avant . En mode auto-rythmé, il leur a été demandé d'atteindre une cible en utilisant l'imagerie motrice (IM) de plusieurs pas (mode de contrôle intermittent) ou en maintenir l'IM de marche vers l’avant (mode de contrôle continu). L'avatar s'est déplacé en réponse à deux classificateurs ‘Regularized Linear Discriminant Analysis’ (RLDA) calibrés qui utilisaient comme caractéristiques la densité spectrale de puissance (Power Spectral Density; PSD) des bandes de fréquences µ (8-12 Hz) sur la zone du pied du cortex moteur. Les classificateurs ont été recalibrés après chaque session. Au cours de l’entrainement et pour certains des essais, une rétroaction modifiée positive a été présentée à la moitié des participants, où l'avatar s'est déplacé correctement quelle que soit la performance réelle du participant. Dans les deux études, l'expérience subjective des participants a été analysée à l'aide d'un questionnaire. Les résultats de l'étude 1 montrent que les niveaux subjectifs d’incarnation sont fortement corrélés à la différence de la puissance de la synchronisation liée à l’événement (Event-Related Synchronization; ERS) sur la bande de fréquence μ et sur le cortex moteur et prémoteur entre les essais de rétroaction modifiés et réguliers. L'étude 2 a montré que tous les participants étaient capables d’utiliser le BCI rythmé et auto-rythmé dans les deux modes. Pour le BCI rythmé, la performance hors ligne moyenne au jour 1 était de 67±6,1% et 86±6,1% au jour 3, ce qui montre que le recalibrage des classificateurs a amélioré la performance hors ligne du BCI (p <0,01). La performance en ligne moyenne était de 85,9±8,4% pour le groupe de rétroaction modifié (77-97%) contre 75% pour le groupe de rétroaction non modifié. Pour le BCI auto-rythmé, la performance moyenne était de 83% en commande de commutateur et de 92% en mode de commande continue, avec un maximum de 12 secondes de commande. Les performances de l’ICO ont été améliorées par la rétroaction modifiée (p = 0,001). Enfin, les résultats de l'étude 3 montrent que pour la classification des initialisations des pas et de la marche, il a été possible de construire des modèles génériques à partir de données hors ligne spécifiques aux participants. Les résultats montrent la possibilité de concevoir une ICO ne nécessitant aucun entraînement spécifique au participant.