These studies were completed to examine how status epilpeticus seizure characteristics are modulated during post-juvenile brain development. This may determine if postnatal age in rats is a better identifier of stages of post-juvenile brain development. The first study fully detailed the acute discrete seizure phase of the rat pilocarpine-SE model. Results for this study showed that Racine behavioral severity score, spike frequency, and seizure severity during the acute discrete seizure phase change in relation to post-juvenile brain developmental stages. The second study fully detailed early and late patterns of status epilepticus. Results for this study displayed modulation of time in pattern, spike frequency, and relative delta power for seizure pattern during post-juvenile ages. The third study displayed modulation of diazepam efficacy during post-juvenile ages. The data suggest characteristics in the acute discrete seizure pliase, chronic SE phase, and therapeutic window of SE change in relation to age during post-juvenile brain development. This establishes that age is a better estimator of developmental stage than animal bodyweight.

A epilepsia, cujas crises são resultantes de distúrbios elétricos temporários no cérebro, é a quarta enfermidade neurológica mais comum, atingindo aproximadamente 50 milhões de pessoas. Essa enfermidade pode ser diagnosticada por meio de eletroencefalogramas (EEG), que são de elevada importância para o diagnóstico de enfermidades cerebrais. As informações consideradas relevantes desses exames são descritas em laudos médicos, que são armazenados com o objetivo de manter o histórico clínico do paciente e auxiliar os especialistas da área médica na realização de procedimentos futuros, como a identificação de padrões de determinadas enfermidades. Entretanto, o crescente aumento no armazenamento de dados médicos inviabiliza a análise manual dos mesmos. Outra dificuldade para a análise de EEG é a variabilidade de opiniões de especialistas sobre um mesmo padrão observado, podendo aumentar a dificuldade para o diagnóstico de enfermidades cerebrais. Também, os exames de EEG podem conter padrões relevantes difíceis de serem observados, mesmo por profissionais experientes. Da mesma forma, nos laudos podem faltar informações e/ou conter erros de digitação devido aos mesmos serem preenchidos apressadamente por especialistas. Assim, neste trabalho foi desenvolvido o método computacional de geração de laudos médicos (automatic generation of medical report AutoGenMR), que tem o propósito de auxiliar especialistas da área médica no diagnóstico de epilepsia e em tomadas de decisão. Esse processo é aplicado em duas fases: (1) construção de classificadores por meio de métodos de aprendizado de máquina e (2) geração automática de laudos textuais. O AutoGenMR foi avaliado experimentalmente em dois estudos de caso, para os quais, em cada um foi utilizada uma base de EEG disponibilizada publicamente e gratuitamente. Nessas avaliações foram utilizadas as mesmas configurações experimentais para a extração de características e construção de classificadores (desconsiderando que um dos problemas de classificação é multiclasse e o outro, binário). No primeiro estudo de caso, os modelos preditivos geraram, em média, 89% das expressões de laudos. Na segunda avaliação experimental, em média, 76% das sentenças de laudos foram geradas corretamente. Desse modo, os resultados de ambos estudos são considerados promissores, constatando que o AutoGenMR pode auxiliar especialistas na identificação de padrões relacionados a eventos epiléticos, na geração de laudos textuais padronizados e em processos de tomadas de decisão. / Epilepsy, which seizures are due to temporary electrical disturbances in the brain, is the fourth most common neurological disorder, affecting 50 million people, approximately. This disease can be diagnosed by electroencephalograms (EEG), which have great importance for the diagnosis of brain diseases. The information considered relevant in these tests is described in textual reports, which are stored in order to maintain the patients medical history and assist medical experts in performing such other procedures as the standard identification of certain diseases. However, the increasing medical data storage makes it unfeasible for manual analysis. Another challenge for the EEG analysis is the diversity of expert opinions on particular patterns observed and may increase the difficulty in diagnosing diseases of the brain. Moreover, the EEG may contain patterns difficult to be noticed even by experienced professionals. Similarly, the reports may not have information and/or include typographical errors due to its rushed filling by experts. Thereby, in this work, the automatic generation of medical report (AutoGenMR) method was developed in order to assist medical experts in the diagnosis of epilepsy and decision making. This method is applied in two phases: (1) classifier building by machine learning techniques and (2) automatic report generation. The AutoGenMR was computed in two case studies, for which, a public and freely available EEG database was used in each one. In both studies, the same experimental settings for feature extraction and classifier building were used. In the first study case, the classifiers correctly generated, on average, 89% of the report expressions. In the second experiment, on average, 76% of the report sentences were successfully generated. In this sense, the results of both studies are considered promising, noting that the AutoGenMR can assist medical experts in the identification of patterns related to epileptic events, standardized textual report generation, and in decision-making processes.

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.

Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2018-04-24T13:44:05Z No. of bitstreams: 1 Matheus Segalotto_.pdf: 8717126 bytes, checksum: 94fda4721d448e49b82be91aaa8057c7 (MD5) / Made available in DSpace on 2018-04-24T13:44:05Z (GMT). No. of bitstreams: 1 Matheus Segalotto_.pdf: 8717126 bytes, checksum: 94fda4721d448e49b82be91aaa8057c7 (MD5) Previous issue date: 2018-01-18 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / PROSUP - Programa de Suporte à Pós-Gradução de Instituições de Ensino Particulares / A compreensão de programa é um processo cognitivo realizado no cérebro dos desenvolvedores para entender o código-fonte. Este processo cognitivo pode ser influenciado por vários fatores, incluindo o nível de modularização do código-fonte e o nível de experiência dos desenvolvedores de software. A compreensão de programa é amplamente reconhecida como uma tarefa com problemas de erro e esforço. No entanto, pouco foi feito para medir o esforço cognitivo dos desenvolvedores para compreender o programa. Além disso, esses fatores influentes não são explorados no nível de esforço cognitivo na perspectiva dos desenvolvedores de software. Além disso, alguns modelos de cognição foram criados para detectar indicadores de atividade cerebral, bem como dispositivos de eletroencefalografia (EEG) para suportar essas detecções. Infelizmente, eles não são capazes de medir o esforço cognitivo. Este trabalho, portanto, propõe o ARNI, um modelo computacional baseado em EEG para medir a compreensão do programa. O modelo ARNI foi produzido com base em lacunas encontradas na literatura após um estudo de mapeamento sistemático (SMS), que analisou 1706 estudos, 12 dos quais foram escolhidos como estudos primários. Um experimento controlado com 35 desenvolvedores de software foi realizado para avaliar o modelo ARNI através de 350 cenários de compreensão de programa. Além disso, esse experimento também avaliou os efeitos da modularização e a experiência dos desenvolvedores no esforço cognitivo dos desenvolvedores. Os resultados obtidos sugerem que o modelo ARNI foi útil para medir o esforço cognitivo. O experimento controlado revelou que a compreensão do código fonte não modular exigia menos esforço temporal (34,11%) e produziu uma taxa de compreensão mais alta (33,65%) do que o código fonte modular. As principais contribuições são: (1) a execução de SMS no contexto estudado; (2) um modelo computacional para medir a compreensão do programa para medir o código-fonte; (3) conhecimento empírico sobre os efeitos da modularização no esforço cognitivo dos desenvolvedores. Finalmente, este trabalho pode ser visto como um primeiro passo para uma agenda ambiciosa na área de compreensão de programa. / Program comprehension is a cognitive process performed in the developers’ brain to understand source code. This cognitive process may be influenced by several factors, including the modularization level of source code and the experience level of software developers. The program comprehension is widely recognized as an error-prone and effort-consuming task. However, little has been done to measure developers’ cognitive effort to comprehend program. In addition, such influential factors are not explored at the cognitive effort level from the perspective of software developers. Additionally, some cognition models have been created to detect brain-activity indicators as well as wearable Electroencephalography (EEG) devices to support these detections. Unfortunately, they are not able to measure the cognitive effort. This work, therefore, proposes the ARNI, an EEG-Based computational model to measure program comprehension. The ARNI model was produced based on gaps found in the literature after a systematic mapping study (SMS), which reviewed 1706 studies, 12 of which were chosen as primary studies. A controlled experiment with 35 software developers was performed to evaluate the ARNI model through 350 scenarios of program comprehension. Moreover, this experiment also evaluated the effects of modularization and developers’ experience on the developers’ cognitive effort. The obtained results suggest that the ARNI model was useful to measure cognitive effort. The controlled experiment revealed that the comprehension of non-modular source code required less temporal effort (34.11%) and produced a higher correct comprehension rate (33.65%) than modular source code. The main contributions are: (1) the execution of SMS in the context studied; (2) a computational model to measure program comprehension to measure source code; (3) empirical knowledge about the effects of modularization on the developers’ cognitive effort. Finally, this work can be seen as a first step for an ambitious agenda in the area of program comprehension.

Orientador: Ione Corrêa / Resumo: INTRODUÇÃO: A Segurança do Paciente pode ser definida como um conjunto de ações que reduzem o risco de danos associados às infecções relacionadas à assistência à saúde até o mínimo aceitável, utilizando-se das melhores evidências disponíveis e visando promover uma assistência qualificada não só em instituições hospitalares. A falta de higienização das mãos é o principal veículo de transmissão de infecções e deve ser realizada antes e após de qualquer procedimento. Os exames de eletroencefalograma podem trazer riscos de contaminação e infecção do couro cabeludo, além dos utensílios utilizados para o exame. A falta de protocolo instituído no processo de trabalho ao paciente pode influenciar no que se refere na disseminação de microrganismos ou até mesmo infecção cruzada do couro cabeludo. OBJETIVO: Avaliar os fatores de risco de disseminação de microrganismos relacionados com as dermatoses do couro cabeludo para elaboração de medidas preventivas e controle de infecção cruzada em exames de eletroencefalograma. MÉTODO: Revisão Integrativa com estratégia de busca em bases de dados online: CINAHL, Embase, Pubmed, Scopus e Wos. Utilizou-se os seguintes descritores: Dermatoses do Couro Cabeludo, Eletroencefalograma, Desinfecção, Instituição em Saúde e Infecção Cruzada. O levantamento foi realizado durante os meses de janeiro a fevereiro de 2018. Como critérios de inclusão utilizaram-se estudos que abordassem a temática da correlação entre exame de eletroencefalograma e infecção cruza... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: INTRODUCTION: Patient Safety can be defined as a set of actions that reduce the risk of injury associated with health care-associated infections to the lowest acceptable level, using the best evidence available and to promote qualified care not only in hospital institutions. The lack of hand wash is the primary way of transmission of infections and should be performed before and after any procedure. Electroencephalogram tests can bring scalp contamination and infection risks, as well as the utensils used for the exam. The lack of protocol instituted in the patient work process can influence in the dissemination of microorganisms or even cross-infection of the scalp. OBJECTIVE: To evaluate risk factors to microorganisms' dissemination related to dermatoses of the scalp. To perform measures of prevention and control of cross infection in electroencephalogram exams. METHOD: Integrative revision with search strategy in online databases: CINAHL, Embase, PubMEd, Scopus and Wos. The descriptors were used: Scalp Dermatoses, Electroencephalogram, Disinfection, Health Institution and Cross Infection. The search was carried out from January to February 2018. As inclusion criteria were researches by topic electroencephalogram and cross-infection, in national and international journals and texts available in Portuguese, Spanish or English indexed and without temporal delimitation. RESULTS: A total of 936 articles were identified. From the inclusion criteria, four articles were inserted fo... (Complete abstract click electronic access below) / Mestre

Το ηλεκτροεγκεφαλογράφημα αποτελεί μία μέθοδο καταγραφής των ηλεκτρικών σημάτων που παράγονται στο εσωτερικό του εγκεφάλου. Τα ηλεκτρικά αυτά σήματα διαχέονται από το σημείο που παράγονται προς την εξωτερική δερματική επιφάνεια του κρανίου όπου μετρώνται ως διαφορές δυναμικού. Αντικείμενο της διπλωματικής εργασίας είναι η σχεδίαση και η υλοποίηση μίας πειραματικής συνθήκης και της αντίστοιχης πειραματικής διάταξης, η οποία θα επιτρέπει την καταγραφή των Προκλητών δυναμικών του ατόμου που θα εκτελεί το πείραμα. Το κλινικό πρωτόκολλο που χρησιμοποιήθηκε εξετάζει δύο βασικά θέματα. Το πρώτο αφορά την αντίληψη του ανθρώπου για το χρόνο και το δεύτερο το πώς επηρεάζουν τα υποσυνείδητα μηνύματα τη λήψη αποφάσεων και την εγκεφαλική λειτουργία. Αρχικά, στο πρώτο κεφάλαιο της παρούσας διπλωματικής εργασίας, αναφέρονται οι βασικές αρχές του ηλεκτροεγκεφαλογραφήματος και το σύστημα διάδοσης της πληροφορίας στον ανθρώπινο εγκέφαλο. Επιπλέον περιγράφεται ο τρόπος με τον οποίο γίνεται η καταγραφή του σήματος του εγκεφαλογραφήματος και των Προκλητών Δυναμικών. Στη συνέχεια, στο δεύτερο κεφάλαιο παρουσιάζεται η θεωρία στην οποία βασίστηκε ο σχεδιασμός του κλινικού πρωτοκόλλου και η οποία αφορά δύο θέματα, την αντίληψη του ανθρώπου για το χρόνο και την επιρροή των υποσυνείδητων μηνυμάτων στη λήψη αποφάσεων. Στα επόμενα κεφάλαια περιγράφεται το κλινικό πρωτόκολλο που χρησιμοποιήθηκε και οι πειραματικές συνθήκες που εξετάστηκαν κατά τη διεξαγωγή των πειραμάτων. Ακόμα, γίνεται περιγραφή τόσο του υλικού όσο και του λογισμικού μέρους της πειραματικής διάταξης που αναπτύχθηκε για την υλοποίηση του κλινικού πρωτοκόλλου. Η πειραματική διάταξη που αναπτύχθηκε στα πλαίσια της διπλωματικής επιτρέπει τη σύνδεση με Ηλεκτροεγκεφαλογράφο με αποτέλεσμα τον συγχρονισμό της πειραματικής ακολουθίας και της καταγραφής του ηλεκτροεγκεφαλογραφήματος και των Προκλητών Δυναμικών. Με την πειραματική διάταξη που αναπτύχθηκε έγιναν δύο πειράματα. Η καταγραφή της εγκεφαλικής δραστηριότητας του κάθε εξεταζόμενου (ΗΕΓ) ήταν συνεχής για όλη τη διάρκεια της δοκιμασίας. Η εξαγωγή των Προκλητών Δυναμικών έγινε μετά το τέλος της καταγραφής με το πρόγραμμα EEGLAB, με το οποίο έγινε και η επεξεργασία τους. Τα Προκλητά Δυναμικά απεικονίστηκαν τόσο σε δισδιάστατα όσο και σε τρισδιάστατα μοντέλα κεφαλιών ενώ εξετάστηκε και το φασματικό περιεχόμενο του σήματος του ηλετροεγκεφαλογραφήματος για τις διάφορες πειραματικές συνθήκες. Από την ανάλυση των καταγραφών παρατηρήθηκαν κάποιες διαφορές μεταξύ των συνθηκών του πειράματος οι οποίες περιείχαν υποσυνείδητα μηνύματα και αυτών που δεν περιείχαν. Οι κορυφώσεις του δυναμικού εντοπίστηκαν τις ίδιες χρονικές στιγμές για όλες τις συνθήκες ενώ το πλάτος τους ήταν διαφορετικό μεταξύ των συνθηκών στις οποίες δινόταν στους εξεταζόμενους η σωστή απάντηση με υποσυνείδητο μήνυμα και σε αυτές που δεν δινόταν. / The electroencephalogram constitutes a method for recording electrical signals produced in the interior of the brain. These electric signals are diffused from the point of the brain where they are produced to the exterior dermal surface of the skull where they are measured as potential differences. The object of this diploma thesis is the design and the development of an experiment and the corresponding experimental setup, which allows the recording of the Event Related Potentials of the person who executes the experiment. The clinical protocol that is used examines two fundamental issues. The first is related to time perception while the second one examines if and how subliminal messages influence the decision making and the cerebral operation. Initially, in the first chapter of the present diploma thesis, the fundamentals of the Electroencephalogram and the system that is responsible for the distribution of the information inside the human brain is described. Following, Electroencephalogram and Event Related Potentials (ERPs) recordings are described. In the second chapter the theory on which the planning of the clinical protocol was based is presented. It concerns two research fields of psychophysiology; time perception and the influence of subliminal messages in decision-making. In the next chapters the clinical protocol and the experimental conditions that were examined during the experiments are described. Furthermore, there is a description of both the software and the hardware modules of the developed system. The experimental setup that was developed in the framework of the diploma thesis allows the connection to an Electroencephalograph and appropriate trigger signals are used in order to synchronize stimuli and recordings of EEG and ERPs. Two experiments were conducted. The recording of the brain activity was continuous for the whole duration of the experimental procedure. The Event Related Potentials were extracted post-hoc, after the end of the recording, using the EEGLAB software. The Event Relates Potentials were mapped both on two-dimensional and on three-dimensional head models. The spectrum of the electroencephalogram was also examined for the various experimental conditions. Analysis of the recordings revealed differences between the experimental conditions that contained subliminal messages in the EEG and ERPs. The ERPs’ peaks were detected at the same time delays for all the conditions. However, the amplitude of the peaks differed between the conditions where the right answers were given with subliminal messages and those that did not contain any subliminal messages.

Thesis (M.S.)--Linköping University, 2004. / Includes bibliographical references (p. 63-64). Also available online via the VTI web site (www.vti.se).

Η παρούσα διπλωματική εργασία ασχολείται με τον προεγχειρητικό έλεγχο ασθενών με κροταφική επιληψία που είναι υποψήφιοι να χειρουργηθούν. Συγκεκριμένα εστιάζει στην αξιολόγηση της χρήσης του εξωκρανιακού ΗΕΓ (ηλεκτροεγκεφαλογραφήματος) ως προεγχειρητικού συνεπικουρικού εργαλείου για την εντόπιση και χαρτογράφηση της βλάβης του κροταφικού λοβου για ένα άριστο μετεγχειρητικό αποτέλεσμα. / This study focuses on the preoperative evaluation of extracranial EEG of patients with temporal lobe epilepsy who are candidates for surgery. More precisely, it focuses on the efficiency of the extracranial EEG to map the intracranial lesion that causes the epileptic syndrome.

abstract: In this research work, a novel control system strategy for the robust control of an unmanned ground vehicle is proposed. This strategy is motivated by efforts to mitigate the problem for scenarios in which the human operator is unable to properly communicate with the vehicle. This novel control system strategy consisted of three major components: I.) Two independent intelligent controllers, II.) An intelligent navigation system, and III.) An intelligent controller tuning unit. The inner workings of the first two components are based off the Brain Emotional Learning (BEL), which is a mathematical model of the Amygdala-Orbitofrontal, a region in mammalians brain known to be responsible for emotional learning. Simulation results demonstrated the implementation of the BEL model to be very robust, efficient, and adaptable to dynamical changes in its application as controller and as a sensor fusion filter for an unmanned ground vehicle. These results were obtained with significantly less computational cost when compared to traditional methods for control and sensor fusion. For the intelligent controller tuning unit, the implementation of a human emotion recognition system was investigated. This system was utilized for the classification of driving behavior. Results from experiments showed that the affective states of the driver are accurately captured. However, the driver's affective state is not a good indicator of the driver's driving behavior. As a result, an alternative method for classifying driving behavior from the driver's brain activity was explored. This method proved to be successful at classifying the driver's behavior. It obtained results comparable to the common approach through vehicle parameters. This alternative approach has the advantage of directly classifying driving behavior from the driver, which is of particular use in UGV domain because the operator's information is readily available. The classified driving mode was used tune the controllers' performance to a desired mode of operation. Such qualities are required for a contingency control system that would allow the vehicle to operate with no operator inputs. / Dissertation/Thesis / Doctoral Dissertation Engineering 2015

Submitted by (edna.saturno@ufrpe.br) on 2017-03-30T14:56:43Z No. of bitstreams: 1 RohgiToshio Meneses Chikushi.pdf: 6493487 bytes, checksum: b95c0c692d050783c78c20f7a212f0e6 (MD5) / Made available in DSpace on 2017-03-30T14:56:43Z (GMT). No. of bitstreams: 1 RohgiToshio Meneses Chikushi.pdf: 6493487 bytes, checksum: b95c0c692d050783c78c20f7a212f0e6 (MD5) Previous issue date: 2014-08-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The electroencephalogram (EEG) is still an important tool in the diagnosis of neurodiseases. As recording technique offers an excellent temporal resolution, instantly capturing brain electrical activity. Recent studies suggest that non-linear dynamic time series as EEG can be transformed into complex networks by the methods of visibility graph and the recurrence network. The builded complex network allows many parameters or network metrics to characterize normal and epleptics. In this work, we transform EEG signals to complex networks and identify the metrics to find statistical diferences between normal and epleptical groups. We show that exist significant statistical differences in the network metrics from the normals and epileptics conditions. We conclude that the transformation of the EEG signal in complex networks provide a helpful tool to diagnostic the brain states. / O eletroencefalograma (EEG) ainda é uma ferramenta importante no diagnóstico de desordens neurológicas. Como técnica de registro, oferece uma excelente resolução temporal, capturando instantaneamente a atividade cerebral. Estudos recentes em dinâmica não linear sugerem que séries temporais como o EEG podem ser transformadas em redes complexas por meio de mapeamentos como o método de visibilidade e o de recorrência. Essas redes, em analogia às rede neuronais, representam as características de complexidade dinâmica do sistema nervoso. Neste trabalho, transformamos sinais de EEG em redes complexas derivadas da reconstrução dos espaços de fase, com base no conceito de recorrência. A aplicação de redes complexas na análise não linear da dinâmica da atividade cerebral, possibilitou diferenciar estados normais e epilépticos por meio da comparação das medidas topológicas dessas redes. Identificamos diferenças significativas ao compararmos os registros de EEG em condições normais e epilépticas usando as métricas das redes e concluímos que a transformação do EEG em redes complexas fornece um grande número de parâmetros úteis para caracterização e possível diagnóstico dos estados do comportamento cerebral normal e epiléptico.