Contexto: Epilepsia não é uma única doença, mas uma família de síndromes que compartilham a recorrência de crises. Estima-se que 3% da população em geral terá epilepsia em algum momento em suas vidas. A detecção de crises epiléticas é frequentemente feita através da análise de exames de eletroencefalografia. Há várias dificuldades na detecção de crises, variabilidade entre pessoas, localização do conteúdo espectral, interferências, dentre outras. Motivação: Há um crescente uso com bons resultados de redes complexas para análise de séries temporais, mas poucos destes são voltados à análise de sinais de epilepsia. Os trabalhos que analisam epilepsia, em geral, negligenciam uma análise estatística rigorosa. Ainda há dúvida quanto à utilização de algoritmos prospectivos para predição de crises. Métodos: As séries temporais são analisadas utilizando 7 tamanhos diferentes de janelas, 256, 303, 512, 910, 1.024, 2.048, e 2.730 pontos. São utilizados 6 algoritmos de conversão de série temporal em rede complexa, redes de k vizinhos mais próximos, redes de k vizinhos mais próximos adaptativos, redes de epsilon vizinhança, redes cíclicas, redes de transição, e grafos de visibilidade. Cada um desses algoritmos têm seus parâmetros, e no total são realizadas 75 conversões. Para cada rede complexa gerada, são extraídas 21 medidas que as caracterizam. Com a extração dessas medidas, um novo conjunto de dados é formado e utilizado para treinar 37 classificadores diferentes, divididos em 4 classes, análise de discriminante linear, árvore de decisão, k vizinhos mais próximos, e máquina de vetores de suporte. É utilizada uma validação cruzada com 10-folds numa parte do conjunto de dados separada para o treino dos classificadores, e apenas o melhor classificador dentre os 37 foi selecionado em cada conversão realizada. No conjunto de teste, é feita a estimativa de desempenho do melhor classificador, que é então comparado à um preditor aleatório e ao estado da arte. Resultados: A rede de epsilon vizinhança obteve o melhor resultado, com 100% de acurácia no conjunto de teste em quase todos os cenários, com janelas de tamanho pequeno e com a análise de discriminante linear. As outras redes também tiveram bons resultados, comparáveis ao estado da arte, exceto a rede de transição cujo desempenho foi ruim. Conclusão: Foi possível desenvolver um algoritmo prospectivo com classificador linear utilizando a rede de epsilon vizinhança, com desempenho comparável ao estado da arte e com rigorosa avaliação estatística, e não apenas utilizando a acurácia como medida de desempenho. / Context: Epilepsy is not a single disease, but a family of syndromes that share recurrent seizures. It is estimated that 3% of the population will have epilepsy at some moment of their life. Seizure detection is frequently done through EEG analysis. There are several difficulties in seizure detection, people variability, the location of the spectral content, interferences, among other things. Motivation: There is a growing usage with good results of the complex networks to analyze time series, but few studies focusing on epilepsy. The works that have analyzed epilepsy, in general, have neglected a strict statistical analysis. There is still doubts regarding the usage of prospective algorithms to predict seizures. Methods: The time series were analyzed on 7 different window sizes, 256, 303, 512, 910, 1024, 2048, and 2730 points. We used 6 different algorithms to convert the time series into complex networks, k nearest neighbors network, adaptive k nearest neighbors network, epsilon neighborhood network, cycle network, transition network, visibility graph. Each algorithm has its parameters, and in total, we performed 75 conversions. For each conversion, the network extracted 21 measures. A new dataset is formed with these measures, and it was used to train 37 classifiers, divided into 4 classes, linear discriminant analysis, decision tree, k nearest neighbors, support vector machine. We used 10-fold cross-validation in a training set, separated from the whole dataset, and only the best classifier between the 37 was selected for each conversion. In the test set, we estimated the performance of the best classifiers, and then they were compared with a random predictor and with the state-of-the-art. Results: The epsilon neighborhood network presented the best result with 100% accuracy over almost all scenarios in the test set, with small window sizes and the linear discriminant analysis. The other networks also had good results, comparable to the state-of-the-art, except the transition network which had poor performance. Conclusion: We were able to develop a prospective algorithm with a linear classifier using the epsilon neighborhood network, with a performance comparable to the state-of-the-art and with rigorous statistical analysis, and not only using the accuracy as our performance measure.

A cafeína é um potente estimulante do sistema nervoso central dos animais e vem sendo usada para melhorar o desempenho de cavalos atletas devido a sua propriedade de estímulo da atividade motora e redução da fadiga muscular. O objetivo do presente estudo foi avaliar o perfil eletroencefalográfico de equinos submetidos a cafeína comercial utilizando eletrodos de superfície e biotelemetria. Foram utilizados dois protocolos experimentais. No primeiro protocolo dois equinos (A e B) foram submetidos a cafeína comercial e no segundo protocolo dois animais controle (C e D) foram submetidos a um placebo com solução fisiológica. O EEG obtido dessas situações foi analisado no ambiente Matlab® onde se avaliou os espectros de potência. Os dados foram analisados por One-way ANOVA valores de p < 0,05 usando vários testes estatísticos. A análise do espectro resultante mostrou predominância de frequências nas faixas de 20 Hz e 35 Hz para o animal A; 15 Hz, 20 Hz e 25 Hz para o animal B, essas frequências foram verificadas nos animais antes de serem submetidos à cafeína; quando foram submetidos à cafeína foi observado um pico predominante em 10 Hz em ambos indivíduos. Para os animais controle, a frequência observada foi de 15 Hz e 25 Hz para o animal C e para o animal D as frequências foram 15 Hz, 20 Hz, 30 Hz e 35 Hz. Para ambos os animais submetidos à cafeína os resultados estatísticos comprovaram que houve diferenças entre as médias da densidade espectral de potência dos sinais adquiridos. Para os animais que foram submetidos ao placebo os testes estatísticos demonstraram que não houve diferenças das médias dos espectros constatando que a aplicação do placebo não teve efeito na atividade elétrica cerebral nos equin os estudados. Conclui-se que o EEG registrou um padrão diferenciado para os animais que foram submetidos à cafeína. / Caffeine is a powerful stimulant of the central nervous system of animals and has been used to improve performance in athletic horses due to its property of stimulating motor activity and reduced muscle fatigue. The aim of this study was to evaluate the electroencephalographic pattern of horses undergoing caffeine by the use of surface electrodes and biotelemetry system. Two experimental protocols were carried out. In the first protocol two horses (A and B) were submitted to commercial caffeine and in the second protocol two control horses (C and D) were submitted to a saline placebo. The EEG was obtained and analyzed in Matlab® by evaluating the power spectra. Data were analyzed by one-way ANOVA with p-value <0.05 using several statistical tests. The results of spectrum analysis showed predominance of frequency bands from 20 Hz to 35 Hz for animal A and 15 Hz, 20 Hz and 25 Hz for animal B; these frequencies were observed in the animals before being subjected to caffeine; when they were submitted to caffeine it was observed a predominant peak at 10 Hz in both individuals. For animals used as control the observed frequency was 15 Hz and 25 Hz for animal C; for animal D frequencies were 15 Hz, 20 Hz, 30 Hz and 35 Hz. For both animals submitted to caffeine statistical results showed that there were differences between the means of the power spectral density of signals acquired. For animals that underwent placebo saline statistical tests showed no differences of mean spectra stating that the application of placebo had no effect on brain electrical activity studied in horses. Overall results had shown that the EEG pattern was influenced by caffeine.

This is a study of quantitative electroencephalographic (QEEG) comodulation analysis, which is used to assist in identifying regional brain patterns associated with Chronic Fatigue Syndrome (CFS) compared to an EEG normative database. Further, this study investigates EEG patterns in depression which is found to be a highly comorbid condition to CFS. The QEEG comodulation analysis examines spatial-temporal cross-correlation of spectral estimates in the individual resting dominant frequency band. A pattern shown by Sterman and Kaiser (2001) and referred to as the Anterior Posterior Dissociation (APD) discloses a significant reduction in shared functional modulation between frontal and centro-parietal areas of the cortex. Conversely, depressed patients have not shown this pattern of activity but have disclosed a pattern of frontal Hypercomodulation localized to bilateral pre-frontal and frontal cortex. This research investigates these comodulation patterns to determine whether they exist reliably in these populations of interest and whether a clear distinction between two highly comorbid conditions can be made using this metric. Sixteen CFS sufferers and 16 depressed participants, diagnosed by physicians and a psychiatrist respectively were involved in QEEG data collection procedures. Nineteen-channel cap recordings were collected in five conditions: eyes-closed, eyes open, reading task-one, math computations task-two, and a second eyes-closed baseline. Five of the 16 CFS patients showed a clear Anterior Posterior Dissociation pattern for the eyes-closed resting dominant frequency. However, 11 participants did not show this pattern of dysregulation. Examination of the mean 8-12 Hz band spectral magnitudes across three cortical regions (frontal, central and parietal) indicated a trend of higher overall alpha levels in the parietal region in CFS patients who showed the APD pattern compared to those who did not show this pattern. All participants who showed the APD pattern were free of medication, while the majority of those absent of this pattern were using antidepressant medications. For the depressed group, all of which were medication free, 100 % of the depressed group showed a frontal Hypercomodulation pattern. Furthermore, examination of the mean 8-12 Hz band spectral magnitudes across three cortical regions disclosed a trend of high frontal alpha and a left/right asymmetry of greater voltages in the left frontal cortex. Although these samples are small, it is suggested that this method of evaluating the disorder of CFS holds promise. The fact that this pattern is not consistently represented in the CFS sample could be explained by the possibility of subtypes of CFS, or perhaps comorbid conditions. Further, the use of antidepressant medications may mask the pattern by altering the temporal characteristics of the EEG. This study, however, was able to demonstrate that the QEEG was able to parse out the regional cerebral brain differences between CFS and depressed group.

A cafeína é um potente estimulante do sistema nervoso central dos animais e vem sendo usada para melhorar o desempenho de cavalos atletas devido a sua propriedade de estímulo da atividade motora e redução da fadiga muscular. O objetivo do presente estudo foi avaliar o perfil eletroencefalográfico de equinos submetidos a cafeína comercial utilizando eletrodos de superfície e biotelemetria. Foram utilizados dois protocolos experimentais. No primeiro protocolo dois equinos (A e B) foram submetidos a cafeína comercial e no segundo protocolo dois animais controle (C e D) foram submetidos a um placebo com solução fisiológica. O EEG obtido dessas situações foi analisado no ambiente Matlab® onde se avaliou os espectros de potência. Os dados foram analisados por One-way ANOVA valores de p < 0,05 usando vários testes estatísticos. A análise do espectro resultante mostrou predominância de frequências nas faixas de 20 Hz e 35 Hz para o animal A; 15 Hz, 20 Hz e 25 Hz para o animal B, essas frequências foram verificadas nos animais antes de serem submetidos à cafeína; quando foram submetidos à cafeína foi observado um pico predominante em 10 Hz em ambos indivíduos. Para os animais controle, a frequência observada foi de 15 Hz e 25 Hz para o animal C e para o animal D as frequências foram 15 Hz, 20 Hz, 30 Hz e 35 Hz. Para ambos os animais submetidos à cafeína os resultados estatísticos comprovaram que houve diferenças entre as médias da densidade espectral de potência dos sinais adquiridos. Para os animais que foram submetidos ao placebo os testes estatísticos demonstraram que não houve diferenças das médias dos espectros constatando que a aplicação do placebo não teve efeito na atividade elétrica cerebral nos equin os estudados. Conclui-se que o EEG registrou um padrão diferenciado para os animais que foram submetidos à cafeína. / Caffeine is a powerful stimulant of the central nervous system of animals and has been used to improve performance in athletic horses due to its property of stimulating motor activity and reduced muscle fatigue. The aim of this study was to evaluate the electroencephalographic pattern of horses undergoing caffeine by the use of surface electrodes and biotelemetry system. Two experimental protocols were carried out. In the first protocol two horses (A and B) were submitted to commercial caffeine and in the second protocol two control horses (C and D) were submitted to a saline placebo. The EEG was obtained and analyzed in Matlab® by evaluating the power spectra. Data were analyzed by one-way ANOVA with p-value <0.05 using several statistical tests. The results of spectrum analysis showed predominance of frequency bands from 20 Hz to 35 Hz for animal A and 15 Hz, 20 Hz and 25 Hz for animal B; these frequencies were observed in the animals before being subjected to caffeine; when they were submitted to caffeine it was observed a predominant peak at 10 Hz in both individuals. For animals used as control the observed frequency was 15 Hz and 25 Hz for animal C; for animal D frequencies were 15 Hz, 20 Hz, 30 Hz and 35 Hz. For both animals submitted to caffeine statistical results showed that there were differences between the means of the power spectral density of signals acquired. For animals that underwent placebo saline statistical tests showed no differences of mean spectra stating that the application of placebo had no effect on brain electrical activity studied in horses. Overall results had shown that the EEG pattern was influenced by caffeine.

Contexto: Epilepsia não é uma única doença, mas uma família de síndromes que compartilham a recorrência de crises. Estima-se que 3% da população em geral terá epilepsia em algum momento em suas vidas. A detecção de crises epiléticas é frequentemente feita através da análise de exames de eletroencefalografia. Há várias dificuldades na detecção de crises, variabilidade entre pessoas, localização do conteúdo espectral, interferências, dentre outras. Motivação: Há um crescente uso com bons resultados de redes complexas para análise de séries temporais, mas poucos destes são voltados à análise de sinais de epilepsia. Os trabalhos que analisam epilepsia, em geral, negligenciam uma análise estatística rigorosa. Ainda há dúvida quanto à utilização de algoritmos prospectivos para predição de crises. Métodos: As séries temporais são analisadas utilizando 7 tamanhos diferentes de janelas, 256, 303, 512, 910, 1.024, 2.048, e 2.730 pontos. São utilizados 6 algoritmos de conversão de série temporal em rede complexa, redes de k vizinhos mais próximos, redes de k vizinhos mais próximos adaptativos, redes de epsilon vizinhança, redes cíclicas, redes de transição, e grafos de visibilidade. Cada um desses algoritmos têm seus parâmetros, e no total são realizadas 75 conversões. Para cada rede complexa gerada, são extraídas 21 medidas que as caracterizam. Com a extração dessas medidas, um novo conjunto de dados é formado e utilizado para treinar 37 classificadores diferentes, divididos em 4 classes, análise de discriminante linear, árvore de decisão, k vizinhos mais próximos, e máquina de vetores de suporte. É utilizada uma validação cruzada com 10-folds numa parte do conjunto de dados separada para o treino dos classificadores, e apenas o melhor classificador dentre os 37 foi selecionado em cada conversão realizada. No conjunto de teste, é feita a estimativa de desempenho do melhor classificador, que é então comparado à um preditor aleatório e ao estado da arte. Resultados: A rede de epsilon vizinhança obteve o melhor resultado, com 100% de acurácia no conjunto de teste em quase todos os cenários, com janelas de tamanho pequeno e com a análise de discriminante linear. As outras redes também tiveram bons resultados, comparáveis ao estado da arte, exceto a rede de transição cujo desempenho foi ruim. Conclusão: Foi possível desenvolver um algoritmo prospectivo com classificador linear utilizando a rede de epsilon vizinhança, com desempenho comparável ao estado da arte e com rigorosa avaliação estatística, e não apenas utilizando a acurácia como medida de desempenho. / Context: Epilepsy is not a single disease, but a family of syndromes that share recurrent seizures. It is estimated that 3% of the population will have epilepsy at some moment of their life. Seizure detection is frequently done through EEG analysis. There are several difficulties in seizure detection, people variability, the location of the spectral content, interferences, among other things. Motivation: There is a growing usage with good results of the complex networks to analyze time series, but few studies focusing on epilepsy. The works that have analyzed epilepsy, in general, have neglected a strict statistical analysis. There is still doubts regarding the usage of prospective algorithms to predict seizures. Methods: The time series were analyzed on 7 different window sizes, 256, 303, 512, 910, 1024, 2048, and 2730 points. We used 6 different algorithms to convert the time series into complex networks, k nearest neighbors network, adaptive k nearest neighbors network, epsilon neighborhood network, cycle network, transition network, visibility graph. Each algorithm has its parameters, and in total, we performed 75 conversions. For each conversion, the network extracted 21 measures. A new dataset is formed with these measures, and it was used to train 37 classifiers, divided into 4 classes, linear discriminant analysis, decision tree, k nearest neighbors, support vector machine. We used 10-fold cross-validation in a training set, separated from the whole dataset, and only the best classifier between the 37 was selected for each conversion. In the test set, we estimated the performance of the best classifiers, and then they were compared with a random predictor and with the state-of-the-art. Results: The epsilon neighborhood network presented the best result with 100% accuracy over almost all scenarios in the test set, with small window sizes and the linear discriminant analysis. The other networks also had good results, comparable to the state-of-the-art, except the transition network which had poor performance. Conclusion: We were able to develop a prospective algorithm with a linear classifier using the epsilon neighborhood network, with a performance comparable to the state-of-the-art and with rigorous statistical analysis, and not only using the accuracy as our performance measure.

Dans le domaine aéronautique, comme dans de nombreux autres domaines de la vie courante ou professionnelle, les erreurs ont souvent été associées à des défaillances attentionnelles. Nos travaux s’insèrent dans cette problématique, et sont plus particulièrement focalisés sur les variations de la capacité à allouer son attention sur une tâche donnée à la suite d’une interruption. Dans un premier temps, nous avons mis en place des expérimentations qui permettent d’évaluer l’étendue des variations de performances obtenues dans une tâche à la suite d’une interruption, en fonction des composantes attentionnelles sollicitées dans la tâche à exécuter. Dans un second temps, nous avons mis en place un indicateur fiable et objectif mettant en évidence des différences dans le fonctionnement physiologique cérébral en fonction de ces composantes attentionnelles. Dans un troisième temps, nous avons été amenés à vérifier l’effet de diverses interruptions dans des situations aéronautiques réalistes. Nos travaux permettent de conclure à l’existence de fluctuations de l’attention à la suite d’une interruption, fluctuations dont la conséquence peut se traduire par des variations de performances et par différentes stratégies de gestion des erreurs et des activités. / In the aviation field, as in many other areas of personal or professional life, errors have often been associated with attentional failures. Our work is related to this issue, and is more particularly focused on variations of attention following an interruption. In a first step, we have set up experiments to measure changes in performance obtained in a task after an interruption. These variations are studied systematically according to various attentional components requested in the task at hand. In a second step, we have set up an indicator showing differences in the physiological functioning of the brain depending on these attentional components. Thirdly, we have tested the effect of various interruptions in realistic aeronautical situations. From our work, we conclude that there is a variation in attention after an interruption, the consequences of which can result in errors, performance variations, and differences in the management of errors and activities.

Les problèmes inverses de localisation de sources en électroencéphalographie (EEG) consistent à retrouver le lieu d'origine dans le cerveau des signaux mesurés sur le scalp. La qualité du résultat de localisation dépend des modèles géométriques et de conductivité électrique utilisés pour la résolution du problème. Parmi les tissus composant la tête, le crâne est celui dont la conductivité est la plus influente, en particulier à cause de sa faible valeur. De plus, le crâne humain est un tissu osseux comportant des parties dures et spongieuses, d'épaisseurs variables. Sa composition est très variable selon les individus, en termes de géométrie et de valeurs des conductivités, d'où la nécessité de développer des technique d'estimation de conductivités inconnues dans le crâne. Le but de cette thèse est de réduire l'incertitude sur la conductivité du crâne, pour des géométries sphériques et réalistes, en particulier en vue d’améliorer les résultats d'estimation des sources dans le problème inverse EEG. Dans le cas d'un domaine sphérique à 3 couches, l'existence, l'unicité et la stabilité de la conductivité dans la couche intermédiaire (crâne) sont discutées, et une procédure de reconstruction est proposée. Puis deux modèles plus réalistes de tête sont étudiés, l'un pour lequel le crâne est modelisé par un seul compartiment, l'autre dans lequel les parties spongieuses et dure sont distinguées. Des simulations numériques mettent en évidence le rôle de la structure interne du crâne pour la détermination de sa conductivité. / One of the major issues related to electroencephalography (EEG) is to localize where in the brain signals are generated, this is so called inverse problem of source localization. The quality of the source localization depends on the accuracy of the geometry and the electrical conductivity model used to solve the problem. Among the head tissues, the skull conductivity is the one that influences most the accuracy of the source localization, due to its low value. Moreover, the human skull is a bony tissue consisting of compact and spongy bone layers, whose thickness vary across the skull. As the skull tissue composition has strong inter-individual variability both in terms of geometry and of individual conductivity, conductivity estimation techniques are required in order to determine the unknown skull conductivity. The aim of this thesis is to reduce the uncertainty on the skull conductivity both in spherical and realistic head geometries in order to increase the quality of the inverse source localization problem. Therefore, conductivity estimation is first performed on a 3-layered spherical head model. Existence, uniqueness and stability of the conductivity in the intermediate skull layer are discussed, together with a constructive recovery scheme. Then a simulation study is performed comparing two realistic head models, a bulk model where the skull is modelled as a single compartment and a detailed one accounting for the compact and spongy bone layers, in order to determine the importance of the internal skull structure for conductivity estimation in EEG.

L'examen EEG est un examen fonctionnel essentiel de la médecine moderne. Il consiste dans l'acquisition de signaux physiologique d'un patient afin de diagnostiquer ses troubles neurologiques. À cause d'une mauvaise répartition des experts et d'une diversification des spécialités de neurophysiologie, la téléexpertise s'impose comme une solution pour apporter des soins pour tous. Cette thèse aborde la problématique de téléexpertise d'un examen EEG tout en repoussant les limitations des équipements d'EEG actuelle. Ces limitations, observées par les médecins, sont l'imprécision de la synchronisation entre les signaux physiologiques et l'enregistrement vidéo et la faible qualité de cet enregistrement vidéo. La première limitation nous a conduit à développer un mécanisme de synchronisation matérielle déterministe ainsi que de définir un nouveau format de fichier capable de stocker les signaux physiologiques ainsi que la vidéo de manière uniforme. La seconde limitation a mené en la définition d'un nouvel algorithme de compression nommé ROI-Waaves. Cet algorithme, utilisant la transformée en ondelettes et le codeur entropique HENUC, est capable d'encoder des zones de l'image avec une qualité supérieure afin de conserver les détails des mains et du visage du patient. Finalement, nous avons développé deux implémentations mettant en place les solutions proposées et permettant de réaliser un examen EEG synchronisé et compressé. De plus, nous avons proposé une architecture matérielle compressant un flux vidéo à 100 images par seconde en temps réel en utilisant ROI-Waaves. / The EEG exam is an essential functional exam in modern medicine. It involves the acquisition of biological signals of a patient to diagnose his neurological disorders. Because of a poor spread of experts and diversification of neurophysiological specialties, remote expertise imposes itself as a solution to bring health care to most people. The thesis approaches the problematic of remote diagnosis of EEG exams while pushing back the current EEG device limitations. Those limitations, observed by doctors, are the imprecise synchronization between biological signals and the video recording and the low quality of this video recording. The first limitation has led us to develop a hardware determinist synchronization as well as to define a novel file format to store biological signals alongside the video. Thanks to those two mechanisms, we guarantee the correct synchronization when reviewing a EEG file. The second limitation has driven us to define a new compression algorithm named ROI-Waaves. The algorithm uses wavelet transform and the HENUC coding scheme and encodes one or many regions of an image with higher quality to preserve details. Those regions are defined as the hands and head of the patient and help doctors to perform correct diagnoses. Lastly, we have developed two implementations which use the proposed solutions and allowing to carry out a synchronized and compressed EEG exam. The produced exams by these devices are complete and can be used for review or long-time storage. In addition, we have proposed a hardware architecture that compresses 100 frames per second video stream in real time using ROI-Waaves.

There are several commercial electroencephalography (EEG) devices on the market; however, affordable devices are not versatile for diverse research applications. The purpose of this project was to investigate how to develop a low-cost, portable, single-channel EEG system for a research institute that could be used for neurofeedback-related applications in homecare. A device comparison was intended to examine what system requirements such a system would need to achieve the secondary objective of developing a neurofeedback application that demonstrates the functionalities of the new device. A portable, single-channel EEG device prototype was realized that consisted of an amplifier module called EEG Click, a single-board microcontroller, an electrode cable, some disposable wet electrode pads, and a custom 3D-printed headband. Three pieces of software were developed: firmware for the prototype, two supporting computer applications for data recording, and visual neurofeedback. The neurofeedback application replayed a first-person view roller coaster video at a varying frame rate based on the theta band's mean power spectral density (PSD). The prototype was compared against a commercial device, InteraXon MUSE 2 (Muse). Technical measurements included determining the amplitude-frequency characteristics and signal quality, such as signal-to-noise ratio (SNR), spurious-free dynamic range (SFDR), and total harmonic distortion (THD). Furthermore, four physiological measurements were performed on six human test subjects, aged between 21-31 (mean: 26.0, std: 3.11), to compare the altered brain activity and induced artifacts between the two devices. The four tests were respiratory exercise, head movement exercise, eye movement exercise, and paced auditory serial addition test (PASAT), where each measurement included several epochs with various stimuli. After the recordings, PSD was calculated for each bandpass filtered epoch, then the spectra were split into theta (4-8 Hz), alpha (8-12 Hz), and beta bands (12-30 Hz). The PSD values were averaged within each frequency band, and then these baseline-corrected mean values were the input for the repeated measures ANOVA statistical analysis. Results revealed that the amplitude-frequency characteristic of the prototype was low-pass filter-like and had a smaller slope than Muse's. The prototype's SNR, including and excluding the first five harmonics, was 6 dB higher, while SFDR and THD for the first five harmonics were roughly the same as Muse's. The two devices were comparable in detecting changes in most physiological measurements. Some differences between the two devices were that Muse was able to detect changes in respiratory activity in the beta band (F(8,16) = 2.510, p = .056), while the prototype was more sensitive to eye movement, especially lateral and circular eye movement in theta (F(2,8) = 9.144, p = .009) and alpha (F(2,8) = 6.095, p = .025) bands. A low-cost, portable EEG prototype was successfully realized and validated. The prototype was capable of performing homecare neurofeedback in the theta band. The results indicated it is worth exploring further the capabilities of the prototype. Since the sample size was too small, more complex physiological measurements with more test subjects would be more conclusive. Nevertheless, the findings are promising; the prototype may become a product once. / Det finns flera kommersiella EEG-apparater (elektroencefalografi) på marknaden; däremot är de prismässigt överkomliga apparaterna inte mångsidiga nog för olika forskningsapplikationer. Syftet med detta projekt var att undersöka hur man kan utveckla en billigt, portabelt, enkanaligt EEG-system för ett forskningsinstitut som skulle kunna användas för neurofeedbackrelaterade tillämpningar inom hemsjukvård. En apparatjämförelse var tänkt att undersöka vilka systemkrav ett sådant system skulle behöva uppnå för att realisera det sekundära målet att utveckla en neurofeedback-applikation för att demonstrera den nya apparatens funktioner. En prototyp av en bärbar, enkanalig EEG-apparat skapades som bestod av en förstärkarmodul kallad EEG Click, en enkortsmikrokontroller, en elektrodkabel, några utbytbara våta elektrodkuddar och ett 3D-tryckt specialpannband. Tre mjukvarodelar utvecklades: en firmware för prototypen och två stödjande datorapplikationer, en för datainspelning och en för visuell neurofeedback. Applikationen för neurofeedback spelade upp en berg-och-dalbana för förstapersonsvisning med en varierande bildhastighet baserat på thetabandets effektspektrumet (eng. power spectral density, PSD). Prototypen jämfördes mot en kommersiell apparat, InteraXon MUSE 2 (Muse). Tekniska mätningar inkluderade fastställande av amplitud-frekvensegenskaper och signalkvalitet, såsom signal-brusförhållande (eng. signal-to-noise ratio, SNR), spuriosfritt dynamiskt område (eng. spurious free dynamic range, SFDR) och total harmonisk distorsion (eng. total harmonic distortion, THD). Vidare utfördes fyra fysiologiska mätningar på sex mänskliga deltagare (medelålder: 26,0, std: 3,11) för att jämföra de två apparaterna med avseende på mätningar av den förändrade hjärnaktiviteten och inducerade artefakter. De fyra testerna var andningsövningar, huvudrörelseövningar, ögonrörelseövningar, och paced auditory serial addition test (PASAT), där varje mätning innehöll flera epoker med olika stimuli. Efter inspelningarna beräknades PSD för varje bandpassfiltrerad epok, sedan delades spektrumet upp i theta-, alpha- och beta-band. Medelvärdet för PSD-värdena kalkylerades för varje frekvensband och dessa baseline-korrigerade medelvärden var indata till den beroende ANOVA statistisk analysen. Resultaten avslöjade att amplitud-frekvenskarakteristiken för prototypen var lågpassfilterliknande och hade en mindre lutning än Muses. Prototypens SNR, inklusive och exklusive de första fem harmonik, var 6 dB högre, medan SFDR och THD för de första fem övertonerna var ungefär desamma som Muses. De två apparaterna var jämförbara när det gäller att upptäcka förändringar i de flesta fysiologiska mätningar. Vissa skillnader mellan de två apparaterna var att Muse kunde upptäcka förändringar i andningsaktivitet i beta-bandet (F(8,16) = 2,510, p = 0,056), medan prototypen var mer känslig för ögonrörelser, särskilt de laterala och cirkulära ögonrörelser, i theta-bandet (F(2,8) = 9,144, p = 0,009) och alfa-bandet (F(2,8) = 6,095, p = 0,025). Prototypen var generellt mer känslig för grundläggande hjärnaktivitet, buller från omgivningen och artefakter. Sammanfattningvis konstruerades en billig, bärbar EEG-prototyp, vilketvaliderades framgångsrikt. Den anpassade enheten kunde utföra neurofeedback för hemsjukvård. Resultaten visade att det är värt att utforska prototypens möjligheter ytterligare. Eftersom stickprovet var relativt litet skulle mer komplexa fysiologiska mätningar med flera testpersoner krävas för att fastställa framtida användningsområden. Icke desto mindre är resultaten lovande; prototypen kan bli en produkt en gång.

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