Orientadora: Profa. Dra. Cristiane Otero Reis Salum / Coorientador: Prof. Dr. Francisco José Fraga da Silva / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Neurociência e Cognição, São Bernardo do Campo, 2017. / Pacientes de transtorno bipolar e esquizofrenia apresentam déficits no processamento de informação. Dentre esses déficits está uma disfunção do mecanismo de filtragem sensorial, que pode ser observada através do teste de Inibição Pré-Pulso (IPP), que acessa a inibição das respostas muscular, observada por eletromiografia (EMG) e neural, observada por eletroencefalograma (EEG) através da inibição de potenciais evocados, como o P2-N1. No fenômeno da IPP, é observado que a resposta iniciada por um estímulo de alta intensidade é reduzida quando este é precedido em alguns milissegundos (30-300ms) por outro estímulo de baixa intensidade. Esses estímulos são respectivamente chamados de Pulso (P) e Pré-Pulso (PP). A porcentagem de redução da resposta ao P, quando este é precedido por um PP é calculada em relação à magnitude de resposta que seria evocada pelo P quando este não é precedido por PP algum. O presente estudo visou avaliar o filtro sensorial através do registro simultâneo dos sinais eletromiográficos e eletroencefalográficos em pacientes brasileiros de primeiro episódio psicótico de transtorno bipolar (BP) e esquizofrenia (SZ). Vinte pacientes BP, quinze pacientes SZ e 22 sujeitos sadios participaram do estudo. Pacientes SZ apresentam redução da %IPP observada por EMG em relação a pessoas sadias, ao passo que pacientes do grupo BP não apresentam redução da filtragem sensório-motora. Para a IPP neural, foi observada redução na amplitude de P do grupo BP na região frontal, avaliada pelo eletrodo Fz e redução da amplitude de P e também na %IPP para os grupos BP e SZ na região parietal, avaliada pelo eletrodo Pz. Os resultados indicam que a redução da filtragem sensorial foi observada em diferentes estágios do processamento sensorial. E a divergência entre IPP clássica e IPP neural para o grupo BP sugere que a IPP medida por EMG clássica e medida por EEG refletem filtros sensoriais diferentes e que pacientes de diferentes grupos podem exibir déficits em um desses filtros apenas. O presente trabalho é o pioneiro na utilização de ferramentas de atenuação de artefatos contaminantes do sinal neural no teste de IPP neural. / Patients with bipolar disorder and schizophrenia have deficits in information processing. Among these deficits is a dysfunction of the sensory filtering mechanism, which can be observed through the Prepulse Inhibition (PPI) test, which accesses the inhibition of muscle responses, observed by electromyography (EMG) and neural, observed by electroencephalogram (EEG) through inhibition of evoked potentials, such as P2-N1. In the PPI phenomenon, it is observed that the response initiated by a high intensity stimulus is reduced when it is preceded in a few milliseconds (30-300ms) by another low intensity stimulus. These stimuli are respectively called Pulse (P) and Prepulse (PP). The reduction percentage of the response to P when it is preceded by a PP is calculated in relation to the magnitude of response that would be evoked by P when it is not preceded by any PP. The present study aimed to evaluate the sensory filter through the simultaneous recording of electromyographic and electroencephalographic signals in Brazilian patients with first psychotic episode of bipolar disorder (BP) and schizophrenia (SZ). Twenty BP patients, fifteen SZ patients and 22 healthy subjects participated in the study. SZ patients presented a reduction in the %PPI observed by EMG when compared to healthy individuals, whereas patients in the BP group did not show reduction of sensory-motor filter. For the neural PPI, a reduction in BP group P amplitude was observed in the frontal region, evaluated by the Fz electrode. Also, was observed a reduction in the P amplitude and in the %PPI for the BP and SZ groups in the parietal region, evaluated by the Pz electrode. These results indicate that the reduction of sensorial filtration was observed at different stages of sensorial processing. And the divergence between classical IPP and neural IPP for the BP group suggests that PPI measured by classical EMG and measured by EEG reflect different sensory filters and that patients from different groups may exhibit deficits in one of these filters only. The present work is the pioneer in the use of attenuation tools to reduce contaminating artifacts in PPI test neural signal.

Esta dissertação de mestrado tem por objetivo analisar os conjuntos-K, uma hierarquia de redes neurais biologicamente mais plausíveis, e aplicá-los ao problema de classificação de imagética motora através do eletroencefalograma (EEG). A imagética motora consiste no ato de processar um movimento motor da memória humana de longo tempo para a memória de curto prazo. A imagética motora deixa um rastro no sinal do EEG que torna possível a identificação e classificação dos diferentes movimentos motores. A tarefa de classificação de imagética motora através do EEG é reconhecida como complexa devido à não linearidade e quantidade de ruído da série temporal do EEG e da pequena quantidade de dados disponíveis para aprendizagem. Os conjuntos-K são um modelo conexionista que simula o comportamento dinâmico e caótico de populações de neurônios do cérebro e foram modelados com base em observações do sistema olfatório feitas por Walter Freeman. Os conjuntos-K já foram aplicados em diversos domínios de classificação diferentes, incluindo EEG, tendo demonstrado bons resultados. Devido às características da classificação de imagética motora, levantou-se a hipótese de que a aplicação dos conjuntos-K na tarefa pudesse prover bons resultados. Um simulador para os conjuntos-K foi construído para a realização dos experimentos. Não foi possível validar a hipótese levantada no trabalho, dado que os resultados dos experimentos realizados com conjuntos-K e imagética motora não apresentaram melhorias significativas para a tarefa nas comparações realizadas. / This dissertation aims to examine the K-sets, a hierarchy of biologically plausible neural networks, and apply them to the problem of motor imagery classification through electroencephalogram (EEG). Motor imagery is the act of processing a motor movement from long-term to short-term memory. Motor imagery leaves a trail in the EEG signal, which makes possible the identification and classification of different motor movements. Motor imagery classification is a complex problem due to non-linearity of the EEG time series, low signal-to-noise ratio, and the small amount of data typically available for learning. K-sets are a connectionist model that simulates the dynamic and chaotic behavior of populations of neurons in the brain, modeled based on observations of the olfactory system by Walter Freeman. K-sets have already been used in several different classification domains, including EEG, showing good results. Due to the characteristics of motor imagery classification, a hypothesis that the application of K-sets in the task could provide good results was raised. A simulator for K-sets was created for the experiments. Unfortunately, the hypothesis could not be validated, as the results of the conducted experiments with K-sets and motor imagery showed no significant improvements in comparison in the task performed.

abstract: The human brain controls a person's actions and reactions. In this study, the main objective is to quantify reaction time towards a change of visual event and figuring out the inherent relationship between response time and corresponding brain activities. Furthermore, which parts of the human brain are responsible for the reaction time is also of interest. As electroencephalogram (EEG) signals are proportional to the change of brain functionalities with time, EEG signals from different locations of the brain are used as indicators of brain activities. As the different channels are from different parts of our brain, identifying most relevant channels can provide the idea of responsible brain locations. In this study, response time is estimated using EEG signal features from time, frequency and time-frequency domain. Regression-based estimation using the full data-set results in RMSE (Root Mean Square Error) of 99.5 milliseconds and a correlation value of 0.57. However, the addition of non-EEG features with the existing features gives RMSE of 101.7 ms and a correlation value of 0.58. Using the same analysis with a custom data-set provides RMSE of 135.7 milliseconds and a correlation value of 0.69. Classification-based estimation provides 79% & 72% of accuracy for binary and 3-class classication respectively. Classification of extremes (high-low) results in 95% of accuracy. Combining recursive feature elimination, tree-based feature importance, and mutual feature information method, important channels, and features are isolated based on the best result. As human response time is not solely dependent on brain activities, it requires additional information about the subject to improve the reaction time estimation. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2019

Substantial research has been done on the electroencephalogram (EEG) neural signals generated by feedback within a simple choice task, and there is much evidence for the existence of a reward prediction error signal generated in the anterior cingulate cortex of the brain when the outcome of this type of choice does not match expectations. However, less research has been done to date on the neural responses to intermediate outcomes in a multi-step choice task. Here, I investigated the neural signals generated by a complex, non-deterministic task that involved multiple choices before final win/loss feedback in order to see if the observed signals correspond to predictions made by reinforcement learning theory. In Experiment One, I conducted an EEG experiment to record neural signals while participants performed a computerized task designed to elicit the reward positivity, an event-related brain potential (ERP) component thought to be a biological reward prediction error signal. EEG results revealed a difference in amplitude of the reward positivity ERP component between experimental conditions comparing unexpected to expected feedback, as well as an interaction between valence and expectancy of the feedback. Additionally, results of an ERP analysis of the amplitude of the P300 component also showed an interaction between valence and expectancy. In Experiment Two, I used machine learning to classify epoched EEG data from Experiment One into experimental conditions to determine if individual states within the task could be differentiated based solely on the EEG data. My results showed that individual states could be differentiated with above-chance accuracy. I conclude by discussing how these results fit with the predictions made by reinforcement learning theory about the type of task investigated herein, and implications of those findings on our understanding of learning and decision-making in humans. / Graduate

Malgré les récents progrès dans la prise en charge des patients en réanimation, la mortalité en réanimation reste élevée: environ 30%. L’atteinte cérébrale en est un déterminant majeur. Le concept de l'implication d'un dysfonctionnement du tronc cérébral dans la survenue de la mort en réanimation est bien établi. Cependant, les signatures neurophysiologiques de cette atteinte du tronc cérébral ne sont pas encore bien caractérisées. Certains paramètres de l'électroencéphalogramme (EEG) et des potentiels évoqués (PE) pourraient permettre l'évaluation de l'état fonctionnel du tronc cérébral en réanimation. La réactivité de l'EEG aux stimulations nociceptives permet de tester le fonctionnement du tronc cérébral par la voie extra-lemniscale (spino-thalamique). Les potentiels somesthésiques (PES) et auditifs précoces (PEAP) explorent respectivement les voies lemniscale médiane et lemniscale latérale dans le tronc cérébral.Dans les travaux présentés ici, nous avons évalué les anomalies de réponses neurophysiologiques du tronc cérébral et leur impact sur le pronostic dans différentes cohortes de patients en soins intensifs. En effet, le manque de réactivité de l'EEG à la stimulation douloureuse, ainsi que l'allongement du temps de conduction subcortico-corticale (PES P14-N20_ IPL) ont été chacun indépendamment corrélé à la mort en réanimation. L'allongement du temps de conduction ponto-mésencéphalique (PEAP III-V _ IPL) a tendance à être associé à l'apparition du délire ou du réveil retardé. Nos travaux fournissent des substrats neurophysiologiques des dysfonctionnements du tronc cérébral observés chez les patients gravement malades et leur relation avec le pronostic. Des études avec d'autres marqueurs neurophysiologiques ciblant le tronc cérébral comme les enregistrements du réflexe de clignement, des PE laser et des PE respiratoires, sont nécessaires. / Despite recent progress in the management of critically ill patients, mortality in the ICU remains high (around 30%). Neurological impairment is a major determinant of mortality in ICU. It has been hypothesithed that brainstem dysfunction might play a role in mortality in the ICU. However, neurophysiological signatures of brainstem failure in ICU patients have not yet been characterized. Electroencephalogram (EEG) and sensorial evoked potentials (EP) parameters could enable the assessment of the functional status of the brainstem at the bedside in the ICU. EEG reativity to intense painful stimuli allows testing the proper functioning of the brainstem via the extra-lemniscal pathway (spino-thalamic). Somatosensory (SSEP) and brainstem auditory (BAEP) evoked potentials respectively explore the median lemniscal and the lateral lemniscal pathways within the brainstem.In the works presented here, we assessed brainstem neurophysiological responses' and their impact on prognosis in various cohorts of critically ill patients. A lack of EEG reactivity to painful stimulation as well as the lengthening of subcortico-cortical conduction time (SSEP P14-N20_ IPL) was each independantly correlated with death in the ICU. The lengthening of ponto-mesencephalic conduction time (BAEP III-V _ IPL) tended to be associated with the onset of delirium or delayed awakening. Our work provides neurophysiological substratum for the concept of the brainstem dysfunctions in critically ill patients and their relationship with prognosis. Supplemental studies with other neurophysiological markers involving the brainstem such as recordings of blink reflexes, laser EP and respiratory EP, are needed to confirm these results.

No description available.

<p>  </p> <p>Hazard identification is one of the most significant components in safety management at construction jobsites to prevent undesired fatalities and injuries of construction workers. The current practice, which relies on a limited number of safety managers’ manual and subjective inspections, and existing research efforts analyzing workers’ physical and physiological signals have achieved limited success, leaving many hazards unidentified at the jobsites. Motivated by this critical need, this research aims to develop a human sensing network that allows for ubiquitous hazard identification in the construction workplace.</p> <p>To attain this overarching goal, this research analyzes construction workers’ collective EEG signals collected from wearable EEG sensors based on machine learning, virtual reality (VR), and advanced signal processing techniques. Three specific research objectives are: (1) establishing a relationship between EEG signals and the existence of construction hazards, (2) identifying correlations between EEG signals/physiological states (e.g., emotion) and different hazard types, and (3) developing an integrated platform for real-time construction hazard mapping and comparing the results developed based on VR and real-world experimental settings.</p> <p>Specifically, the first objective establishes the relationship by investigating the feasibility of identifying construction hazards using a binary EEG classifier developed in VR, which can capture EEG signals associated with perceived hazards. In the second objective, correlations are discovered by testing the feasibility of differentiating construction hazard types based on a multi-class classifier constructed in VR. In the first and second objectives, the complex relationships are also analyzed in terms of brain dynamics and EEG signal components. In the third objective, the platform is developed by fusing EEG signals with heterogeneous data (e.g., location), and the discrepancies in VR and real-world environments are quantitatively assessed in terms of hazard identification performance and human behavioral responses.</p> <p>The primary outcome of this research is that the proposed approach can be applied to actual construction jobsites and used to detect all potential hazards, which was challenging to be achieved based on the current practice and existing research efforts. Also, the human cognitive mechanisms revealed in this research discover new neurocognitive knowledge in construction workers’ hazard perception. As a result, this research contributes to enhancing current hazard identification capability and improving construction workers’ safety and health.</p>

NeuroGaze is a novel Virtual Reality (VR) interface that integrates electroencephalogram (EEG) and eye tracking technologies to enhance user interaction within virtual environments (VEs). Diverging from traditional VR input devices, NeuroGaze allows users to select objects in a VE through gaze direction and cognitive intent captured via EEG signals. The research assesses the performance of the NeuroGaze system against conventional input devices such as VR controllers and eye gaze combined with hand gestures. The experiment, conducted with 20 participants, evaluates task completion time, accuracy, cognitive load through the NASA-TLX surveys, and user preference through a post-evaluation survey. Results indicate that while NeuroGaze presents a learning curve, evidenced by longer average task durations, it potentially offers a more accurate selection method with lower cognitive load, as suggested by its lower error rate and significant differences in the physical demand and temporal NASA-TLX subscale scores. This study highlights the viability of incorporating biometric inputs for more accessible and less demanding VR interactions. Future work aims to explore a multimodal EEG-Functional near-infrared spectroscopy (fNIRS) input device, further develop machine learning models for EEG signal classification, and extend system capabilities to dynamic object selection, highlighting the progressive direction for the use of Brain-Computer Interfaces (BCI) in virtual environments.

The neural net computer simulations which will be presented here are based on the acceptance of a set of assumptions that for the last twenty years have been expressed in the fields of information processing, neurophysiology and cognitive sciences. First of all, neural networks and their dynamical behaviors in terms of attractors is the natural way adopted by the brain to encode information. Any information item to be stored in the neural net should be coded in some way or another in one of the dynamical attractors of the brain and retrieved by stimulating the net so as to trap its dynamics in the desired item's basin of attraction. The second view shared by neural net researchers is to base the learning of the synaptic matrix on a local Hebbian mechanism. The last assumption is the presence of chaos and the benefit gained by its presence. Chaos, although very simply produced, inherently possesses an infinite amount of cyclic regimes that can be exploited for coding information. Moreover, the network randomly wanders around these unstable regimes in a spontaneous way, thus rapidly proposing alternative responses to external stimuli and being able to easily switch from one of these potential attractors to another in response to any coming stimulus. In this thesis, it is shown experimentally that the more information is to be stored in robust cyclic attractors, the more chaos appears as a regime in the back, erratically itinerating among brief appearances of these attractors. Chaos does not appear to be the cause but the consequence of the learning. However, it appears as an helpful consequence that widens the net's encoding capacity. To learn the information to be stored, an unsupervised Hebbian learning algorithm is introduced. By leaving the semantics of the attractors to be associated with the feeding data unprescribed, promising results have been obtained in term of storing capacity.

Il existe peu d’études sur les effets physiologiques et pharmacologiques du médicament anesthésiant le plus utilisé chez les anoures, la tricaïne méthanesulfonate, et son utilisation chez la grenouille Xenopus laevis. Notre premier objectif était d’évaluer l’effet de bains d’immersion de 20 minutes de 1 et 2 g/L de tricaïne méthanesulfonate sur la fonction cardiorespiratoire, l’analgésie et les réflexes ainsi que d’étudier la pharmacocinétique. Nos résultats démontrent que des bains de 1 et 2 g/L produisent une anesthésie chirurgicale de 30 et 60 minutes respectivement, sans effet significatif sur le système cardiorespiratoire. À la suite d’une immersion à 2 g/L, on note une demi-vie terminale de 3,9 heures. Cette dose ne produit aucun effet sur l’histologie des tissus 24 heures après l’immersion. Dans une deuxième expérience, nous avons évalué les effets d’une surdose de tricaïne méthanesulfonate en bain d’immersion sur les systèmes cardiorespiratoire et nerveux central grâce à l’électroencéphalographie ainsi que l’effet d’une injection de pentobarbital sodique après 2 heures d’immersion. L’EEG montre un effet dépresseur sur le SNC avec l’utilisation de la tricaïne méthanesulfonate sans voir un arrêt de signal d’EEG sur la période de 2 heures d’enregistrement. Les surdoses à 1 g/L et 3 g/L n’ont pas d’effet significatif sur le rythme cardiaque, et l’injection de pentobarbital suite au bain d’immersion de tricaïne méthanesulfonate est nécessaire pour induire l’euthanasie. Nous avons démontré que le bain de tricaïne méthanesulfonate peut produire une anesthésie de 30 à 60 minutes avec dépression du SNC sans effet cardiovasculaire chez les Xenopus laevis. / Very few studies exist on the physiological and pharmacological effects of the most commonly used anesthetic agent used in amphibians, tricaine methanesulfonate, in Xenopus laevis frogs. Our first goal was to measure the effects of 20 minutes bath immersions of 1 and 2 g/L tricaine methanesulfonate on cardiorespiratory system, analgesia and reflexes. We also studied the pharmacokinetic of tricaine methanesulfonate following an immersion in a 2 g/L bath. Our results show that both 1 and 2 g/L baths produce surgical anesthesia during 30 and 60 minutes respectively, without significant effect on the cardiorespiratory system. Following the immersion in a 2 g/L bath, the tricaine methanesulfonate has a terminal half-life of 3,9 hours and no effect on tissue histology is observed 24 hours after anesthesia. In a second experiment, we evaluated the effects of tricaine methanesulfonate overdose on cardiorespiratory system and on central nervous system using electroencephalography. Moreover, we evaluated the effect of sodium pentobarbital injection after 2 hours of immersion. A significant EEG depression of central nervous system activity occurred with the use of tricaine methanesulfonate following 2 hours of recording and the pentobarbital injection was necessary to induce euthanasia. We showed that tricaine methanesulfonate can produce safe anesthesia of 30 to 60 minutes with reduction of CNS activity and without cardiorespiratory effect in Xenopus laevis.