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Toolbox for automatic EEG data quality assessment
Meloun, Jan ; Gajdoš, Martin (referee) ; Lamoš, Martin (advisor)
This thesis deals with the design of a tool for the automatic evaluation of EEG data quality. The theoretical part of the thesis contains a description of the formation and propagation of the action potential through the nervous system. Furthermore, a theoretical description of the EEG recording and its artifacts. The following is a description of the methods used to detect artifacts. In the practical part of the thesis, there is a description of the design of the tool for automatic EEG quality assessment, including a discussion of the results based on the provided data.
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Cortical-subcortical interactions in EEG data of patients with pharmacoresistant epilepsy
Šíma, Jan ; Králík, Martin (referee) ; Lamoš, Martin (advisor)
This bachelor's thesis deals with the elaboration of a literature search on epilepsy and electroencephalography signals with a focus on patients with drug-resistant epilepsy and the analysis of cortico-subcortical relationships. The theoretical part describes the chapters of epilepsy, electroencephalography, the possibility of pre-processing EEG data and analytical methods, which describe the cortico-subcortical interactions. The practical part contains pre-processing of EEG data, analysis of methods used, data analysis, results, discussion, and conclusion. The data analysis itself is performed by the Phase-amplitude coupling method. The discussion discusses the results, limitations, and other possible connections. The conclusion summarizes the whole bachelor thesis.
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Spatial-temporal analysis of HD-EEG data in pacients with nerodegenerative disease
Jordánek, Tomáš ; Kozumplík, Jiří (referee) ; Lamoš, Martin (advisor)
This master’s thesis deals with diagnostics of prodromal stage of Lewy body disease using microstate analysis. First part of the thesis includes theoretical background which is needed for understanding discussed topics and presented results. This part consists of description of the disease, diagnostic options, electroencephalography, pre-processing of the EEG record and the microstate analysis process. Theoretical background is followed by a practical part of the thesis. In the beginning, there is a chapter about a dataset, used EEG device, and own solution of the pre-processing. Microstate analysis is discussed next, its output parameters were compared between groups with statistical methods. Comparison of the subjects in prodromal stage of Lewy body disease and healthy controls brought significant differences in three parameters of microstates, in rate of unlabelled time frames and also for some counts of transitions between each map or unlabelled sections. Comparison of the subjects in prodromal stage of Lewy body disease and healthy controls brought significant differences in three parameters of microstates, in rate of unlabelled time frames and also for some counts of transitions between each map or unlabelled sections.
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Microstates analysis in EEG data of sleep-deprived subjects
Křápková, Monika ; Koudelka, Vlastimil (referee) ; Lamoš, Martin (advisor)
This bachelor’s thesis deals with the processing and analysis of EEG data in sleep deprived subjects. In the theoretical part, the electroencephalography method is presented first. Further, there are possibilities of preprocessing and analysis of EEG data, introduction to statistics, and the last one is a research on the influence of sleep deprivation on human electrophysiology. The practical part consists of the preprocessing of EEG data, EEG microstates analysis and statistical evaluation of the results from the study of sleep deprivation. Finally, the results of this part are discussed in a separate chapter.
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Data processing in real-time fMRI neurofeedback
Bečička, Martin ; Slavíček, Tomáš (referee) ; Lamoš, Martin (advisor)
Tato práce se zabývá digitálním filtrováním dat získaných z fMRI neurofeedbacku v reálném čase. Práce analyzuje dosavadní řešení používané v CEITEC MU, se zaměřením na zkrácení prodlení na začátku každého neurofeedback bloku, které je způsobeno digitálním filtrováním. Dosavadní řešení používá, hlavně pro jeho online a vyhlazovací vlastnosti, nelineární Kalmánův filtr. Analýzou 150 průběhu fMRI neurofeedback sezení byla zjištěna dolní hranice, kterou nelineární Kalmánův filtr potřebuje k naučení. Počet potřebných vzorků je významně menši než je nastaveno v dosavadním řešení. Další možnosti zkrácení prodlení byly prozkoumány a klouzavý průměrovací filtr byl vybrán jako optimální kompromis mezi dobou prodlení, zpoždění filtru a jeho vyhlazovacími vlastnostmi.
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Influence of parcellation atlas on quality of classification in patients with neurodegenerative dissease
Montilla, Michaela ; Lamoš, Martin (referee) ; Gajdoš, Martin (advisor)
The aim of the thesis is to define the dependency of the classification of patients affected by neurodegenerative diseases on the choice of the parcellation atlas. Part of this thesis is the application of the functional connectivity analysis and the calculation of graph metrics according to the method published by Olaf Sporns and Mikail Rubinov [1] on fMRI data measured at CEITEC MU. The application is preceded by the theoretical research of parcellation atlases for brain segmentation from fMRI frames and the research of mathematical methods for classification as well as classifiers of neurodegenerative diseases. The first chapters of the thesis brings a theoretical basis of knowledge from the field of magnetic and functional magnetic resonance imaging. The physical principles of the method, the conditions and the course of acquisition of image data are defined. The third chapter summarizes the graph metrics used in the diploma thesis for analyzing and classifying graphs. The paper presents a brief overview of the brain segmentation methods, with the focuse on the atlas-based segmentation. After a theoretical research of functional connectivity methods and mathematical classification methods, the findings were used for segmentation, calculation of graph metrics and for classification of fMRI images obtained from 96 subjects into the one of two classes using Binary classifications by support vector machines and linear discriminatory analysis. The data classified in this study was measured on patiens with Parkinson’s disease (PD), Alzheimer’s disease (AD), Mild cognitive impairment (MCI), a combination of PD and MCI and subjects belonging to the control group of healthy individuals. For pre-processing and analysis, the MATLAB environment, the SPM12 toolbox and The Brain Connectivity Toolbox were used.
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Real-Time fMRI neurofeedback of amygdala activity
Sobotková, Marika ; Sekora, Jiří (referee) ; Lamoš, Martin (advisor)
The aim of this diploma thesis is real-time fMRI neurofeedback. In this case, the activity of amygdala is monitored and controled by an emotional regulatory visual task. A procedure to process measured data online and to incorporate it into the stimulus protocol has been proposed. A pilot study was carried out. Offline analysis of measured data was performed, including evaluation of the results of the analysis. The data is processed in MATLAB using the functions of the SPM library.
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Visualisation of tissue surface from volume OCT data
Kostiha, Marek ; Lamoš, Martin (referee) ; Čmiel, Vratislav (advisor)
Display surface is almost commonplace for a man. It uses the difference in brightness of individual objects and surface objects. Use in science, however, is considerably more complicated. Modern computer technologies till not its computing power even the human brain. The maximum achievable resolution with the technology but the human eye cannot cope. Although science and other technical fields are significantly burdened by noise that is needed to suppress and some technologies are very challenging, in many ways surpassed human performance and has become indispensable.
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Relationship between Electrophysiological Activity and Dynamic Functional Connectivity of Large-scale Brain Networks in fMRI Data
Lamoš, Martin ; Hlinka, Jaroslav (referee) ; Kremláček, Jan (referee) ; Jan, Jiří (advisor)
Functional brain connectivity is a marker of the brain state. Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during EEG data analysis may leave part of the neural activity unrecognized. A proposed approach blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. The blind decomposition of EEG spectrogram by Parallel Factor Analysis has been shown to be a useful technique for uncovering patterns of neural activity where each pattern contains three signatures (spatial, temporal, and spectral). The decomposition takes into account the trilinear structure of EEG data, as compared to the standard approaches of electrode averaging, electrode subset selection or using standard frequency bands. The simultaneously acquired BOLD fMRI data were decomposed by Independent Component Analysis. Dynamic functional connectivity was computed on the component’s time series using a sliding window correlation, and functional connectivity network states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of functional connectivity network states and the fluctuations of EEG spectral patterns. Three patterns related to the dynamics of functional connectivity network states were found. Previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. This work suggests that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral constraints are applied on the EEG data.
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Segmentation of blood-vessel tree in whole-body MRI data
Guricová, Karolína ; Lamoš, Martin (referee) ; Kolář, Radim (advisor)
Práce popisuje anatomii a vlastnosti cévního eit s charakteristickými znaky, na kterých je zaloena jeho segmentace. Nejprve jsou uvedeny metody segmentace 3D CT a MRI sken. Více detailn jsou popsány základy segmentace zaloené na druhých derivacích a Hessov matici. K urení podobnosti cévám v pvodním obraze jsou spoítány vlastní ísla Hessovy matice kadého voxelu. K vytvoení výsledného segmentovaného obrazu je navreno více metod pro zpracování tchto vlastních ísel. Metoda je prakticky implementována v MATLABu. Vysegmentované arteriální eit je visualizováno pomoc í knihovny VTK kódované v Pythonu. Dále je navreno GUI, které umouje mení v zpracovaném objemu. Délky artérií jsou aproximovány lineárními úseky kopírujícími jejich cesty a výsledky tohoto mení jsou v práci prezentovány. Limitace této metody a návrh na poloautomatické mení jsou rozebrány na konci této práce.
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