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Simultanneous EEG-FMRI Data Fusion with Generalized Spectral Patterns
Labounek, René ; Havlíček, Martin (referee) ; Hlinka, Jaroslav (referee) ; Jan, Jiří (advisor)
Mnoho rozdílných strategií fúze bylo vyvinuto během posledních 15 let výzkumu simultánního EEG-fMRI. Aktuální dizertační práce shrnuje aktuální současný stav v oblasti výzkumu fúze simultánních EEG-fMRI dat a pokládá si za cíl vylepšit vizualizaci úkolem evokovaných mozkových sítí slepou analýzou přímo z nasnímaných dat. Dva rozdílné modely, které by to měly vylepšit, byly navrhnuty v předložené práci (tj. zobecněný spektrální heuristický model a zobecněný prostorovo-frekvenční heuristický model). Zobecněný frekvenční heuristický model využívá fluktuace relativního EEG výkonu v určitých frekvenčních pásmech zprůměrovaných přes elektrody zájmu a srovnává je se zpožděnými fluktuacemi BOLD signálů pomocí obecného lineárního modelu. Získané výsledky ukazují, že model zobrazuje několik na frekvenci závislých rozdílných úkolem evokovaných EEG-fMRI sítí. Model překonává přístup fluktuací absolutního EEG výkonu i klasický (povodní) heuristický přístup. Absolutní výkon vizualizoval s úkolem nesouvisející širokospektrální EEG-fMRI komponentu a klasický heuristický přístup nebyl senzitivní k vizualizaci s úkolem spřažené vizuální sítě, která byla pozorována pro relativní pásmo pro data vizuálního oddball experimentu. Pro EEG-fMRI data s úkolem sémantického rozhodování, frekvenční závislost nebyla ve finálních výsledcích tak evidentní, neboť všechna pásma zobrazily vizuální síť a nezobrazily aktivace v řečových centrech. Tyto výsledky byly pravděpodobně poškozeny artefaktem mrkání v EEG datech. Koeficienty vzájemné informace mezi rozdílnými EEG-fMRI statistickými parametrickými mapami ukázaly, že podobnosti napříč různými frekvenčními pásmy jsou obdobné napříč různými úkoly (tj. vizuální oddball a sémantické rozhodování). Navíc, koeficienty prokázaly, že průměrování napříč různými elektrodami zájmu nepřináší žádnou novou informaci do společné analýzy, tj. signál na jednom svodu je velmi rozmazaný signál z celého skalpu. Z těchto důvodů začalo být třeba lépe zakomponovat informace ze svodů do EEG-fMRI analýzy, a proto jsme navrhli více obecný prostorovo-frekvenční heuristický model a také jak ho odhadnout za pomoci prostorovo-frekvenční skupinové analýzy nezávislých komponent relativního výkonu EEG spektra. Získané výsledky ukazují, že prostorovo-frekvenční heuristický model vizualizuje statisticky nejvíce signifikantní s úkolem spřažené mozkové sítě (srovnáno s výsledky prostorovo-frekvenčních vzorů absolutního výkonu a s výsledky zobecněného frekvenčního heuristického modelu). Prostorovo-frekvenční heuristický model byl jediný, který zaznamenal s úkolem spřažené aktivace v řečových centrech na datech sémantického rozhodování. Mimo fúzi prostorovo-frekvenčních vzorů s fMRI daty, jsme testovali stabilitu odhadů prostorovo-frekvenčních vzorů napříč různými paradigmaty (tj. vizuální oddball, semantické rozhodování a resting-state) za pomoci k-means shlukovacího algoritmu. Dostali jsme 14 stabilních vzorů pro absolutní EEG výkon a 12 stabilních vzorů pro relativní EEG výkon. Ačkoliv 10 z těchto vzorů vypadají podobně napříč výkonovými typy, prostorovo-frekvenční vzory relativního výkonu (tj. vzory prostorovo-frekvenčního heuristického modelu) mají vyšší evidenci k úkolům.
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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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Underdetermined Blind Audio Signal Separation
Čermák, Jan ; Smékal, Zdeněk (advisor)
We often have to face the fact that several signals are mixed together in unknown environment. The signals must be first extracted from the mixture in order to interpret them correctly. This problem is in signal processing society called blind source separation. This dissertation thesis deals with multi-channel separation of audio signals in real environment, when the source signals outnumber the sensors. An introduction to blind source separation is presented in the first part of the thesis. The present state of separation methods is then analyzed. Based on this knowledge, the separation systems implementing fuzzy time-frequency mask are introduced. However these methods are still introducing nonlinear changes in the signal spectra, which can yield in musical noise. In order to reduce musical noise, novel methods combining time-frequency binary masking and beamforming are introduced. The new separation system performs linear spatial filtering even if the source signals outnumber the sensors. Finally, the separation systems are evaluated by objective and subjective tests in the last part of the thesis.
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Human Sleep EEG Analysis
Sadovský, Petr ; Rozman, Jiří (advisor)
This thesis deals with analysis and processing of the Sleep Electroencephalogram (EEG) signals. The scope of this thesis can be split into several areas. The first area is application of the Independent Component Analysis (ICA) method for EEG signal analysis. A model of EEG signal formation is proposed and conditions under which this model is valid are examined. It is shown that ICA can be used to remove non-deterministic artifacts contained in the EEG signals. The second area of interest is analysis of stationarity of the Sleep EEG signal. Methods to identify stationary signal segments and to analyze statistical properties of these stationary segments are presented. The third area of interest focuses on spectral analysis of the Sleep EEG signals. Analyses are performed that shows the processes that form particular parts of EEG signals spectrum. Also, random signals that are an integral part of the EEG signals analysis are performed. The last area of interest focuses on elimination of the transition processes that are caused by the filtering of the short EEG signal segments.
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Joint EEG-fMRI analysis based on heuristic model
Janeček, David ; Kremláček, Jan (referee) ; Labounek, René (advisor)
The master thesis deals with the joint EEG-fMRI analysis based on a heuristic model that describes the relationship between changes in blood flow in active brain areas and in the electrical activity of neurons. This work also discusses various methods of extracting of useful information from the EEG and their influence on the final result of joined analysis. There were tested averaging methods of electrodes interest, decomposition by principal components analysis and decomposition by independent component analysis. Methods of averaging and decomposition by PCA give similar results, but information about a stimulus vector can not be extracted. Using ICA decomposition, we are able to obtain information relating to the certain stimulation, but there is the problem in the final interpretation and selection of the right components in a blind search for variability coupled with the experiment. It was found out that although components calculated from the time sequence EEG are independent for each to other, their spectrum shifts are correlated. This spectral dependence was eliminated by PCA / ICA decomposition from vectors of spectrum shifts. For this method, each component brings new information about brain activity. The results of the heuristic approach were compared with the results of the joined analysis based on the relative and absolute power approach from frequency bands of interest. And the similarity between activation maps was founded, especially for the heuristic model and the relative power from the gamma band (20-40Hz).
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Non-contact detection of physiological parameters from image sequences
Bršlicová, Tereza ; Janoušek, Oto (referee) ; Kolář, Radim (advisor)
This thesis deals with the study of contactless and non-invasive methods for estimating heart and respiratory rate. Non-contact measurement involves sensing persons by using camera and the values of the physiological parameters are then assessed from the sets of image sequences by using suitable approaches. The theoretical part is devoted to description of the various methods and their implementation. The practical part describes the design and realization of the experiment for contactless detection of heart and respiratory rate. The experiment was carried out on 10 volunteers with a known heart and respiratory rate, which was covered by using of a sophisticated system BIOPAC. Processing and analysis of the measured data was conducted in software environment Matlab. Finally, results from contactless detection were compared with the reference from measurement system BIOPAC. Experiment results are statistically evaluated and discussed.
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A comparison of effective and functional connectivity methods in fMRI
Gajdoš, Martin ; Schwarz, Daniel (referee) ; Jan, Jiří (advisor)
Functional magnetic resonance imaging (fMRI) is recent important method, used in neuroimaging. The aim of this thesis is to develop software tool for comparison of two methods for functional and effective connectivity estimation. In this thesis are described the basics of magnetic resonance imaging, fMRI, basic terms of fMRI experiments and generally are described methods of functional and effective connectivity. Then are more detailed mentioned methods of dynamic causal modeling (DCM), Granger causal modeling (GCM) and independent component analysis (ICA). Practical implementation of DCM in toolbox SMP and ICA in toolbox GIFT is also mentioned. In purpose to describe behavior of DCM and GCM in dependence on several parameters are performed Monte Carlo simulations. Then the concept and realization of software tool for simulating connectivity and comparison of DCM and GCM are described. Finally results of DCM and GCM comparison and results of Monte Carlo simulations are discussed.
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Implemetation of algorithms for blind source separation in C/C++ language
Funderák, Marcel ; Malý, Jan (referee) ; Míča, Ivan (advisor)
This thesis is describing one of the methods of Blind Source Separation (BSS) which is Independent Component Analysis. There is shown some brief introduction to the theory behind in which there are explained some basic findings. These findings are important for understanding the theory behind algorithms of ICA. These theoretical findings include primarily explanations of basic knowledge of statistics science. In next part there are described methods which are advisable for preprocessing of input signals – mainly Principal Component Analysis (PCA) and whitening of signals. Mainly whitening is very important part of solution of ICA algorithms. Then there are described different ICA algorithm solutions and especially introduction in this problematic. FastICA algorithm description is mainly depicted because it is very good for computer processing since it is strong and it is less computer demanding than other algorithms. After that follows implementation of one of the ICA algorithm in C++ programming language. FastICA algorithm for complex valued signal was chosen.
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Multi-channel Methods of Speech Enhancement
Zitka, Adam ; Balík, Miroslav (referee) ; Smékal, Zdeněk (advisor)
This thesis deals with multi-channel methods of speech enhancement. Multichannel methods of speech enhancement use a few microphones for recording signals. From mixtures of signals, for example, individual speakers can be separated, noise should be reduced etc. with using neural networks. The task of separating speakers is known as a cocktail-party effect. The main method of solving this problem is called independent component analysis. At first there are described its theoretical foundation and presented conditions and requirements for its application. Methods of ICA try to separate the mixtures with help of searching the minimal gaussian properties of signals. For the analysis of independent components are used different mathematical properties of signals such as kurtosis and entropy. Signals, which were mixed artificially on a computer, can be relatively well separated using, for example, FastICA algorithm or ICA gradient ascent. However, difficult is situation, if we want to separate the signals created in the real recording enviroment, because the separation of speech people speaking at the same time in the real environment affects other various factors such as acoustic properties of the room, noise, delays, reflections from the walls, the position or the type of microphones, etc. Work presents aproach of independent component analysis in the frequency domain, which can successfully separate also recordings made in the real environment.
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Comparison of success rate of multi-channel methods of speech signal separation
Přikryl, Petr ; Zezula, Radek (referee) ; Míča, Ivan (advisor)
The separation of independent sources from mixed observed data is a fundamental problem in many practical situations. A typical example is speech recordings made in an acoustic environment in the presence of background noise or other speakers. Problems of signal separation are explored by a group of methods called Blind Source Separation. Blind Source Separation (BSS) consists on estimating a set of N unknown sources from P observations resulting from the mixture of these sources and unknown background. Some existing solutions for instantaneous mixtures are reviewed and in Matlab implemented , i.e Independent Componnent Analysis (ICA) and Time-Frequency Analysis (TF). The acoustic signals recorded in real environment are not instantaneous, but convolutive mixtures. In this case, an ICA algorithm for separation of convolutive mixtures in frequency domain is introduced and in Matlab implemented. This diploma thesis examines the useability and comparisn of proposed separation algorithms.
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