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

Orientador: Prof. Dr. Diogo Coutinho Soriano / Dissertação (mestrado) - Universidade Federal do ABC. Programa de Pós-Graduação em Engenharia Biomédica, 2017. / Interfaces cérebro-computador (BCIs) definem canais de comunicação capazes de mapear sinais cerebrais em sinais de controle para dispositivos externos, sem utilização dos eferentes biológicos, utilizando comumente estratégias não invasivas para tanto, tal como obtido pela eletroencefalografia de superfície. Dentre os principais paradigmas BCI têm-se os potenciais visualmente evocados em regime estacionário (SSVEP - steady state visually evoked potential), o qual se baseia no sincronismo da atividade elétrica do córtex visual com estímulos visuais externos, permitindo assim a identificação dos eletrodos e frequências estimulatórias mais eficientes para a discriminação dos estímulos escolhidos pelo usuário via modulação da sua atenção. Tal paradigma de sistema BCI tem sido utilizado como uma importante estratégia no âmbito do desenvolvimento de tecnologias assistivas, as quais visam aumentar a qualidade de vida de pacientes com severas limitações motoras e de comunicação. Neste contexto, o presente trabalho apresenta contribuições à implementação de sistemas BCI-SSVEP operando de modo off-line e em tempo de execução (on-line). Para tanto, analisa-se aqui um conjunto de estruturas de processamento de sinais que levam ao melhor desempenho na tarefa de reconhecimento de padrões considerando técnicas clássicas de estimação de espectro e análise de correlação canônica (CCA - Canonical Correlation Analysis), um método comumente referenciado por seus bons resultados. Comparativos envolvendo variantes de pré-processamento baseados na filtragem espacial e na seleção de atributos também são apresentados. Dois conjuntos de dados foram analisados em ambiente off-line e um em tempo de execução. O primeiro conjunto de dados off-line foi analisado a partir da coleta de dados em cooperação científica no contexto do projeto DesTiNe, enquanto o segundo conjunto envolveu coleta de dados off-line e em tempo de execução no próprio laboratório de Métodos Computacionais para a Bioengenharia da UFABC. Como contribuições centrais podem-se mencionar: 1) comparativo de desempenho utilizando variantes de técnicas de filtragem espacial, extração e seleção de características em ambiente off-line; 2) implementação de um setup completo experimental para realização de experimentos BCI-SSVEP com neuro-feedback visual e auditivo; 3) Disponibilização de uma base de dados BCI-SSVEP contendo aquisições de 15 sujeitos com 12 sessões de 6 segundos para cada uma das 4 frequências (10, 11, 12 e 13 Hz), totalizando 48 sessões por sujeito, i.e. um total de 720 sessões de 6 s ou 4.320 s de dados disponibilizados para a comunidade científica; 4) Comparação de 3 métodos de extração de características em âmbito off-line (FFT, Welch e CCA); 5) Comparação de 2 métodos de extração de características em âmbito on-line, FFT e CCA; 6) Análise de desempenho on-line versus off-line. / Brain-computer interfaces (BCIs) define communication channels capable of mapping brain signals in control signals to external devices, without the use of biological efferents, using commonly non-invasive strategies for both, as obtained by surface electroencephalography. Among the main BCI paradigms are the steady state visually evoked potentials (SSVEP), which is based on the synchronization of the electrical activity of the visual cortex with external visual stimuli, thus allowing the identification of the electrodes and frequencies stimulus for discriminating the stimuli chosen by the user by modulating its attention. This BCI system paradigm has been widely used in the development of assistive technologies, which aim to increase the quality of life of patients with severe motor and communication limitations. In this context, this work presents contributions to the implementation of BCI-SSVEP systems operating offline and at run-time. To do so, we analyze here a set of signal processing structures that lead to the best pattern recognition performance considering classical techniques as spectrum estimation and Canonical Correlation Analysis (CCA), a commonly cited method for its good results. Comparisons involving preprocessing variants based on spatial filtering and attribute selection are also presented. Two sets of data were analyzed in an offline environment and one at run time. The first set of off-line data was analyzed from data collection in scientific cooperation in the context of DesTiNe project, while the second set involved off-line and run time data analysis in the Laboratory of Computational Methods for Bioengineering at UFABC. As central contributions may be mentioned: 1) comparative performance using variants of techniques of spatial filtering, feature extraction and feature selection in an offline environment; 2) implementation of a complete experimental setup to perform BCI-SSVEP experiments with visual and auditory neuro-feedback; 3) Availability of a BCI-SSVEP database containing acquisitions of 15 subjects with 12 sessions of 6 seconds for each of the 4 frequencies (10, 11, 12 and 13 Hz), totaling 48 sessions per subject, ie a total of 720 sessions of 6 s or 4,320 s of data made available to the scientific community; 4) Comparison of 3 methods of feature extraction in off-line environment (FFT, Welch and CCA); 5) Comparison of 2 methods of feature extraction in online scope, FFT and CCA; 6) Analysis of performance online versus offline.

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.

Indiana University-Purdue University Indianapolis (IUPUI) / A Brain Computer Interface (BCI) is a hardware and software system that establishes direct communication between human brain and the environment. In a BCI system, brain messages pass through wires and external computers instead of the normal pathway of nerves and muscles. General work ow in all BCIs is to measure brain activities, process and then convert them into an output readable for a computer. The measurement of electrical activities in different parts of the brain is called electroencephalography (EEG). There are lots of sensor technologies with different number of electrodes to record brain activities along the scalp. Each of these electrodes captures a weighted sum of activities of all neurons in the area around that electrode. In order to establish a BCI system, it is needed to set a bunch of electrodes on scalp, and a tool to send the signals to a computer for training a system that can find the important information, extract them from the raw signal, and use them to recognize the user's intention. After all, a control signal should be generated based on the application. This thesis describes the step by step training and testing a BCI system that can be used for a person who has lost speaking skills through an accident or surgery, but still has healthy brain tissues. The goal is to establish an algorithm, which recognizes different vowels from EEG signals. It considers a bandpass filter to remove signals' noise and artifacts, periodogram for feature extraction, and Support Vector Machine (SVM) for classification.