Darbo tikslas - išanalizuoti, kaip Lietuvos studentai vertina smegenų veiklą stimuliuojančių medžiagų vartojimą. Susipažinta su neuroetinėmis smegenų veiklą stimuliuojančių medžiagų/preparatų (SVSP) vartojimo problemomis pasaulyje ir Lietuvoje, palygintos SVSP vartojimo bei vertinimo tendencijos Lietuvoje ir užsienio šalyse. Atlikta sociologinė apklausa, kurioje dalyvavo 531 studentas. Respondentams pateikta 44 klausimų anketa. Apklausti 7 universitetinių ir 3 neuniversitetinių aukštųjų mokyklų studentai. Rezultatai apdoroti SPSS programa. Nustatyta, jog SVSP yra aktuali tema Lietuvos studentams. 40,9 proc. respondentų vartoja arba yra vartoję SVSP. SVSP vartojimo problematika Lietuvoje ir pasaulyje skiriasi. Vakarų šalyse opiausios problemos: piktnaudžiavimas smegenų veiklą stimuliuojančiais vaistais, nekorektiška reklama, socialinė nelygybė tarp vartojančiųjų ir nevartojančiųjų SVSP. Lietuvoje nesilaikoma ženklinimo ir reklamos reikalavimų. Dauguma apklausos respondentų SVSP vartojime neįžvelgia socialinių, teisinių ar etinių problemų, tačiau tai gali sąlygoti išsamios ir teisingos informacijos trūkumas, nes 80,4 proc. respondentų apie juos sužinojo iš reklamos. Tikėtina, kad greitėjant gyvenimo tempui, smegenų veiklą stimuliuojančių preparatų vartojimas Lietuvoje didės, o vartotojams trūkstant išsamios ir korektiškos informacijos apie šiuos preparatus, teisinės, etinės ir socialinės problemos gali paaštrėti. / The aim of this research was to perfom an analysis on Lithuanian students evaluation of brain activity enhancing substances usage. Neuroethical problems araising while using brain activity enhancers were reviewed as well as tendencies of usage and evaluation were compared in Lithuania and other countries. Sociological survey has been performed in 7 universities and 3 non-university higher education institutions. 531 student filled-in a questionaire which contained 44 questions. Data has been analyzed using SPSS program. The usage of brain activity enhancing substances is a relevant topic for Lithuanian students. Brain enhancers are or have been used by 40,9 percent respondents. Although problems regarding brain activity enhancers usage are different in Lithuania and Western countries. Relevant problems in economical developed countries are misuse of brain enhancers, incorrect advertisement, social inequality. In Lithuania labelling and advertising requirement often are not followed. Majority of respondents do not think that using brain activity enhancers can cause any social, ethical or legal problems. Although such oppinion can be influenced by lack of comprehensive information. Information about brain activity enhancers was recieved through commercial means by 80,4 percent respondents. It can be predicted that usage of brain activity enhancers will grow together with rapid life-style. And since there is lack of comprehensive information regarding brain activity enhancers... [to full text]

This dissertation is a compendium of multiple research papers that, together, address two main objectives. The first objective and primary research question is to determine whether or not, through a procedure of independent component analysis (ICA)-based data mining, volume-domain validation, and source volume estimation, it is possible to construct a meaningful, objective, and informative model of brain activity from scalpacquired EEG data. Given that a methodology to construct such a model can be created, the secondary objective and research question investigated is whether or not the sources derived from the EEG data can be used to construct a model of complex brain function associated with the spatial navigation and the virtual Morris Water Task (vMWT). The assumptions of the signal and noise characteristics of scalp-acquired EEG data were discussed in the context of what is currently known about functional brain activity to identify appropriate characteristics by which to separate the activities comprising EEG data into parts. A new EEG analysis methodology was developed using both synthetic and real EEG data that encompasses novel algorithms for (1) data-mining of the EEG to obtain the activities of individual areas of the brain, (2) anatomical modeling of brain sources that provides information about the 3-dimensional volumes from which each of the activities separated from the EEG originates, and (3) validation of data mining results to determine if a source activity found via the data-mining step originates from a distinct modular unit inside the head or if it is an artefact. The methodology incorporating the algorithms developed was demonstrated for EEG data collected from study participants while they navigated a computer-based virtual maze environment. The brain activities of participants were meaningfully depicted via brain source volume estimation and representation of the activity relationships of multiple areas of the brain. A case study was used to demonstrate the analysis methodology as applied to the EEG of an individual person. In a second study, a group EEG dataset was investigated and activity relationships between areas of the brain for participants of the group study were individually depicted to show how brain activities of individuals can be compared to the group. The results presented in this dissertation support the conclusion that it is feasible to use ICA-based data mining to construct a physiological model of coordinated parts of the brain related to the vMWT from scalp-recorded EEG data. The methodology was successful in creating an objective and informative model of brain activity from EEG data. Furthermore, the evidence presented indicates that this methodology can be used to provide meaningful evaluation of the brain activities of individual persons and to make comparisons of individual persons against a group. In sum, the main contributions of this body of work are 5 fold. The technical contributions are: (1) a new data mining algorithm tailored for EEG, (2) an EEG component validation algorithm that identifies noise components via their poor representation in a head model, (3) a volume estimation algorithm that estimates the region in the brain from which each source waveform found via data mining originates, (4) a new procedure to study brain activities associated with spatial navigation. The main contribution of this work to the understanding of brain function is (5) evidence of specific functional systems within the brain that are used while persons participate in the vMWT paradigm (Livingstone and Skelton, 2007) examining spatial navigation. / Graduate / 0541 / 0622 / 0623

Optical recording of various brain signals is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. A major challenge in leveraging the technique is to identify and quantify the rich patterns embedded in the data. However, existing methods often struggle, either due to their limited signal analysis capabilities or poor performance. Here we present Activity Quantification and Analysis (AQuA2), an innovative analysis platform built upon machine learning theory. AQuA2 features a novel event detection pipeline for precise quantification of intricate brain signals and incorporates a Consensus Functional Unit (CFU) module to explore interactions among potential functional units driving repetitive signals. To enhance efficiency, we developed BIdirectional pushing with Linear Component Operations (BILCO) algorithm to handle propagation analysis, a time-consuming step using traditional algorithms. Furthermore, considering user-friendliness, AQuA2 is implemented as both a MATLAB package and a Fiji plugin, complete with a graphical interface for enhanced usability. AQuA2's validation through both simulation and real-world applications demonstrates its superior performance compared to its peers. Applied across various sensors (Calcium, NE, and ATP), cell types (astrocytes, oligodendrocytes, and neurons), animal models (zebrafish and mouse), and imaging modalities (two-photon, light sheet, and confocal), AQuA2 consistently delivers promising results and novel insights, showcasing its versatility in fluorescent imaging data analysis. / Doctor of Philosophy / Understanding and effectively treating brain diseases requires a deep insight into how the brain operates. A crucial aspect of this exploration involves directly visualizing different signals within the brain, allowing researchers to delve into the functions of brain cells and their interactions. However, as data collection expands rapidly, analyzing this wealth of information presents a significant challenge. Existing methods often fall short due to their limited capacity to analyze signals or their subpar performance, failing to keep pace with current demands. In this work, we introduce Activity Quantification and Analysis (AQuA2), an innovative platform rooted in machine learning principles. AQuA2 features a novel event detection pipeline for accurately quantifying intricate brain signals. Additionally, it incorporates a Consensus Functional Unit (CFU) module, which facilitates the exploration of interactions among potential functional units associated with repetitive signals. To enhance efficiency and usability, we have developed acceleration algorithms and released AQuA2 in two versions: a MATLAB package and a Fiji plugin, each designed to address unique user requirements. AQuA2 has demonstrated its efficacy through real-world applications, effectively quantifying and analyzing signals across various platforms such as biosensors, cell types, animal models, and imaging modalities, with promising outcomes. Furthermore, the utilization of AQuA2 has facilitated the discovery of new insights, thereby augmenting its value. These findings emphasize its versatility as software for comprehensive analysis of diverse fluorescent imaging data, enabling a wide range of scientific inquiries.

This thesis provides a novel dynamic large-scale network perspective on brain activity of human sleep based on the analysis of unique human neuroimaging data. Specifically, I provide new information based on integrating spatial and temporal aspects of brain activity both in the transitions between and during wakefulness and various stages of non-rapid-eye movement (NREM) sleep. This is achieved through investigations of inter-regional interactions, functional connectivity (FC), between activity timecourses throughout the brain. Overall, the presented findings provide new important whole-brain insights for our current understanding of sleep, and potentially also of sleep disorders and consciousness in general. In Chapter 2 I present a robust global increase in similarity between the structural connectivity (SC) and the FC in slow-wave sleep (SWS) in almost all of the participants of two independent fMRI datasets. This could point to a decreased state repertoire and more rigid brain dynamics during SWS. Chapter 2 further identifies the changes in FC strengths between wakefulness and individual stages of NREM sleep across the whole-brain fMRI network. I report connectivity in posterior parts of the brain as particularly strong during wakefulness, while connections between temporal and frontal cortices are increased in strength during N1 and N2 sleep. SWS is characterised by a global drop in FC. In Chapter 3 I take advantage of rare MEG recordings of NREM sleep to show, for the first time, the feasibility of constructing source-space FC networks of sleep using power envelope correlations. The increased temporal information of MEG signals allows me to identify the specific frequencies underlying the FC differences identified in Chapter 2 with fMRI. The beta band (16 – 30 Hz) thus stands out as important for the strong posterior connectivity of wakefulness, while a range of frequency bands from delta (0.25 – 4 Hz) to sigma (13 – 16 Hz) all appear to contribute to N2-specific FC increases. Consistent with the fMRI results, slow-wave sleep shows the lowest level of FC. Interestingly, however, the MEG signals suggest a fronto-temporal network of high connectivity in the alpha band, possibly reflecting memory processes. In Chapter 4 I expand the within-frequency FC analysis of Chapter 3 to explore potential cross-frequency interactions in the MEG FC networks. It is shown that N2 sleep involves an abundance of frequency cross-talk, while SWS includes very little. A multi-layer network approach shows that the gamma band (30 – 48 Hz) is particularly integrated in wakefulness. Chapter 5 addresses the identified MEG FC findings from the perspective of traditional spectral sleep staging. By correlating temporal changes in spectral power at the sensor level to fluctuations in average FC, a specific type of transient events is found to underlie the strong N2-specific coupling in static FC values. Lastly, in Chapter 6 I make the leap out of the constraints of traditional low-resolution sleep staging, and extract dynamic states of FC from fMRI timecourses in a completely unsupervised fashion. This provides a novel representation of whole-brain states of sleep and the dynamics governing them. I argue that data-driven approaches like this are necessary to fully characterise the spatiotemporal principles underlying wakefulness and sleep in the human brain.

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.

Parental responses to their children are crucially influenced by stress. However, brain-based mechanistic understanding of the adverse effects of parenting stress and benefits of therapeutic interventions is lacking. We studied maternal brain responses to salient child signals as a function of Mom Power (MP), an attachment-based parenting intervention established to decrease maternal distress. Twenty-nine mothers underwent two functional magnetic resonance imaging brain scans during a baby-cry task designed to solicit maternal responses to child's or self's distress signals. Between scans, mothers were pseudorandomly assigned to either MP (n = 14) or control (n = 15) with groups balanced for depression. Compared to control, MP decreased parenting stress and increased child-focused responses in social brain areas highlighted by the precuneus and its functional connectivity with subgenual anterior cingulate cortex, which are key components of reflective self-awareness and decision-making neurocircuitry. Furthermore, over 13 weeks, reduction in parenting stress was related to increasing child- versus self-focused baby-cry responses in amygdala–temporal pole functional connectivity, which may mediate maternal ability to take her child's perspective. Although replication in larger samples is needed, the results of this first parental-brain intervention study demonstrate robust stress-related brain circuits for maternal care that can be modulated by psychotherapy.

Abstract Brain activity fluctuations occur in multiple spatial and temporal scales. Functional magnetic resonance imaging (fMRI) has shown that infra slow fluctuations (ISF) of blood oxygen level-dependent signal (BOLD) are organized into well-defined areas called resting state networks (RSN). ISFs have also been detected in full-band EEG (fbEEG) data and in recent years, many have combined these two modalities to enable more accurate measurements of brain fluctuations. In simultaneous EEG-fMRI measurements the ISFs of BOLD signal have been found to be correlated with amplitude envelopes of faster electrophysiological data, suggesting the same underlying neuronal dynamics. Also direct correlations have been found in task related studies but not previously in resting state studies. Understanding the relation between EEG and BOLD signal in resting state might prove beneficial in the research of baseline activity fluctuations of the brain. Functional connectivity (FC) of the RSNs has been found to vary in different tasks and in some diseases, but also in resting state in healthy people. Despite numerous studies, no clear cause for these variations has yet been found. To research these open questions we performed simultaneous fbEEG-fMRI studies. The measurements from both modalities were analyzed with independent component analysis to improve the comparability of these results. Correlation analysis revealed that the EEG ISFs correlate with BOLD signal both temporally and spatially. These correlations showed spatiotemporal variability that was related to the strength of RSN functional connectivity. These results indicate that the ISFs of EEG and BOLD reflect a common source of fluctuations. The understanding of the correlations between ISFs in EEG and fMRI BOLD signals gives basic information of brain dynamics and of the variables that affect it. A better understanding of the background of brain activity helps in the development of more effective treatments for various neurological diseases as the knowledge of the mechanisms behind them grows. The ability to measure RSN activity with EEG more accurately can help in the development of new methods for early diagnosis of diseases. / Tiivistelmä Aivojen toiminta vaihtelee monissa avaruudellisissa ja ajallisissa mittakaavoissa. Toiminnallisissa magneettikuvauksissa (TMK) on havaittu, että veren happipitoisuudesta riippuvan (engl. BOLD) signaalin erittäin hitaat vaihtelut ovat järjestäytyneet hyvin määriteltyihin alueisiin, joita kutsutaan lepotilahermoverkostoiksi. Erittäin hitaita vaihteluita on havaittu myös täysikaistaisesta aivosähkökäyrästä (fbEEG). Viime vuosina nämä kaksi menetelmää on usein yhdistetty tarkemman mittaustuloksen aikaansaamiseksi. Samanaikaisissa EEG-TMK-mittauksissa BOLD signaalin erittäin hitaiden vaihteluiden on huomattu korreloivan nopeampien elektrofysiologisten värähtelyjen amplitudien verhokäyrien kanssa, mikä viittaa samaan perustana olevaan neuraaliseen dynamiikkaan. Myös suoria korrelaatioita on löydetty tehtäviin liittyvissä tutkimuksissa, mutta ei aiemmin lepotilatutkimuksissa. Lepotilan EEG:n ja BOLD-signaalin suhteen ymmärrys voi osoittautua hyödylliseksi aivojen perustilan aktiivisuuden vaihteluiden tutkimisessa. Hermoverkostojen toiminnallisen liittyvyyden on todettu huojuvan tietyissä tehtävissä ja joissain sairauksissa, mutta myös lepotilassa terveillä henkilöillä. Runsaasta tutkimuksesta huolimatta ei liittyvyyden huojunnalle ole vielä löytynyt selkeää aiheuttajaa. Näiden avoimien kysymysten tutkimiseksi suoritimme yhdenaikaisia fbEEG-TMK-mittauksia. Kummankin modaliteetin mittaustuloksia analysoitiin itsenäisten komponenttien analyysillä tulosten vertailtavuuden parantamiseksi. Korrelaatioanalyysit osoittivat, että EEG:n erittäin hitaat vaihtelut korreloivat ajallisesti ja avaruudellisesti TMK:n BOLD-signaalin kanssa. Näissä korrelaatioissa esiintyi sekä paikkaan että aikaan liittyvää huojuntaa, joka oli yhteydessä lepotilahermoverkostojen toiminnallisen liittyvyyden vahvuuteen. Nämä tulokset viittaavat siihen, että samat tekijät tuottavat EEG:n ja TMK:n BOLD-signaalien hitaat vaihtelut. EEG:n ja TMK:n signaalien erittäin hitaiden vaihteluiden välisen korrelaation ymmärtäminen antaa perustason tietoa aivojen toiminnan dynamiikasta sekä siihen vaikuttavista tekijöistä. Parempi ymmärrys aivotoiminnan taustoista auttaa kehittämään tehokkaampia hoitoja neurologisiin sairauksiin, kun tieto mekanismeista niiden takana tarkentuu. Mahdollisuus mitata lepotilahermoverkostojen toimintaa EEG:llä aiempaa tarkemmin voi auttaa kehittämään uusia menetelmiä sairauksien varhaiseen diagnostiikkaan.

La Tomographie par Émission de Positons (TEP) est une méthode d'imagerie médicale nucléaire permettant de mesurer l'activité métabolique d'un organe par la dégradation d'un radio-traceur injecté. Cette méthode d'imagerie peut être utilisée pour l'observation de l'activité métabolique cérébrale à l'aide d'un radio-traceur adéquat, tel que le 18F-Fluorodésoxyglucose. Dans le cadre d'une étude clinique, des patients cérébro-lésés ayant des troubles de la conscience ont eu une chirurgie d'implantation d'électrodes de Stimulation Cérébrale Profonde (SCP). Afin d'effectuer un suivi des patients avant et après la procédure de SCP, et parce qu'elle est compatible avec la présence d'électrode, l'imagerie TEP est utilisée. Nous nous posons la question suivante, comment caractériser les variations entre deux imageries TEP afin de mesurer précisément l'éffet d'un traitement ? Par construction les valeurs obtenues en imagerie TEP dépendent de nombreux facteurs. Si le poids du patient ainsi que la quantité injectée de radio-traceur marqué sont classiquement normalisés en utilisant la méthode des 'Standard Uptake Value' (SUV), la glycémie, entre autre ne l'est pas. Pour cette raison, calculer les variations d'activités entre deux imageries TEP est un problème délicat. Nous proposons une fonction pour calculer les cartes de variation métabolique de deux acquisitions TEP basée sur une approche voxel du ratio des imageries TEP. Nous l'appliquons à l'étude des patients stimulés (SCP) avec troubles de la conscience. Plus spéciffiquement, nous nous intéressons à la comparaison des imageries TEP intra-patient (avant versus après SCP), mais aussi à la comparaison interpatient (patient versus référence). Dans le processus de création des cartes intra-patient, les imageries TEP sont recalées rigidement avec une acquisition pondérée T1 d'Imagerie par Résonance Magnétique (IRM) structurelle. Du fait de déformations majeures liées aux lésions cérébrales, un masque cérébral précis est créé manuellement par un expert clinique. Dans le processus de création des cartes inter-patient, les imageries TEP des patients sont recalées de manière élastique à une imagerie de référence, un atlas (groupe témoin), que nous construisons. Dans ce cas, un masque semi-automatique de l'intérieur de la boîte crânienne est réalisé. Les résultats peuvent être affinés par l'application supplémentaire d'un masque manuel déformé. Un des points clefs de la méthode est de calculer une normalisation spécifique à chaque imagerie, les rendant comparables, afin de calculer une caractérisation quantitative des variations métaboliques cérébrales. Les cartes de variation métabolique cérébrale obtenues sont ensuite comparées aux évaluations et effets cliniques observés afin de juger de leur pertinence. / Positron Emission Tomography is a nuclear medicine imaging method, allowing measure of an organe metabolic activity through degradation of an injected radio-tracer. This methode can be used, with the appropriate radio-tracer, such as 18F-Fluorodeoxyglucose, for observation of cerebral metabolic activity. Through a clinical study, brain damaged patients with counciousness disorders had an implantation surgery of Deep Brain Stimulation (DBS) electrodes. To be able to do the follow up of the patient before and after the DBS procedure, and because it's compatible with electrodes, PET imaging is used. We ask ourself the following question, how to characterize variations between two PET images, to precisely mesure the impact of a treatment ? By construction, PET imaging obtained values depend of numerous factors. If patient weight and injected radio-tracer are classicaly normalized, using the `Standard Uptake Value' (SUV) method, glycemia for exemple is not. For this reason, compute activity variations between two PET images is a delicate problem. We propose a specific function to allow computation of metabolic variation maps for two PET acquisitions, based on a voxel approach of the PET imaging ratio. We apply it to the study of stimulated patients (DBS) with counciousness disorders. More specifically, we are interested in intra-patient PET imaging comparison (before versus after DBS), but also in inter-patient comparison (patient versus reference). During the intra-patient maps creation process, PET patient images are rigidly registered with a T1 weighted structural Magnetic Resonance Imaging (MRI) acquisition. Due to major deformation caused by cerebral injuries, a precise brain mask is created by a clinical expert. During the inter-patient maps creation process, PET patient imaging are non-rigidly registered to a reference imaging, an Atlas we build. In this case, a semi automatic mask of the inside skull is computed. Results can be further improved by the supplementary application of a deformed manual mask. One of the method key elements, is to estimate a specific normalization for each imaging, making them comparable, in order to calculate quantitative charaterisation of cerebral metabolic variations. Cerebral metabolic variation maps obtained are then compared to observed clinical assesments and effects to judge their relevance.

There is a growing understanding of the neural mechanisms of human maternal attachment. Human mothers’ neural responses to infants are associated with their behavioral sensitivity observed during interactions with infants. The current symposium aims to provide understanding of the core neural basis for mother-infant attachment, how prenatal and postnatal risk factors influence the maternal brain, and finally whether the negative changes in the maternal brain may be reversed by an intervention effort. The first paper presents converging evidence on neural, psychological and physiological responses to infants in new mothers across diverse cultural contexts. The paper highlights the common core neural processes of mother-infant attachment, which sets the foundation of understanding maternal brain’s successful and unsuccessful adaptation to parenthood. The second paper presents the role of prenatal risk factors, specifically prenatal maternal anxiety, in maternal brain adaptation to parenthood. This longitudinal study suggests that negative effects of maternal anxiety in mothers’ neural adaptation to parenthood may emerge during pregnancy. The third paper presents evidence that socioeconomic stress may also disrupt mothers’ neural adaptation to parenthood. Low family income is associated with dampened neural sensitivity to positive infant expressions and elevated neural sensitivity to negative infant expressions, which further influence disruptions in maternal behavioral responsiveness to own infants. The last presentation suggests that aberrant neural sensitivity to infants among distressed mothers may be improved via an intervention. Among depressed mothers, interventions to improve mental health reduced parental stress and strengthened neural functional connectivity in response to their infant.

Elektroencefalografický záznamový systém slouží k vyšetření mozkové aktivity. Na základě tohoto vyšetření lze stanovit diagnózu některých nemocí, například epilepsie. Účelem této práce bylo zpracování signálu z toho systému a vytvoření modelového signálu, který bude s reálným signálem porovnán. Uměle vytvořený signál vychází z Jansenova matematického modelu, který byl dále implementován v prostředí MATLAB a rozšířen ze základního modelu na komplexnější zahrnující nelinearity a model rozhraní elektroda – elektrolyt. Dále bylo provedeno měření signálů na EEG fantomu a následná identifikace parametrů naměřených signálu. V první fázi byly testovány jednoduché signály. Identifikace parametrů těchto signálů sloužila k validaci daného EEG fantomu. V druhé fázi bylo přistoupeno k testování EEG signálů navržených podle matematického Jansenova modelu. Analýza veškerých signálů zahrnuje mimo jiné časově frekvenční analýzu či ověření platnosti principu superpozice.