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Measurement of biological signals with Bipack system
Brudík, Vladislav ; Hlaváček, Antonín (referee) ; Valla, Martin (advisor)
This bachelor's thesis deals with questions of sensing electrical brain activity by electroencephalograph, and the data processing. Theoretical part is focused on brain, it's anatomy and functions. It also deals with biological signals, types of curves and systems used for measuring EEG curves. For purpose of signal processing it's been designed application software with GUI interface. This application software displays EEG curves, significant frequency ranges, frequency spectrum and filtered areas of characteristic waves.
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Alpha monitor
Svobodová, Eva ; Kolářová, Jana (referee) ; Chmelař, Milan (advisor)
The master´s thesis presents the problems of EEG biofeedback and its application to relax people. The first part discusses the properties of EEG signal , the requirements of the standard EEG and also distribusion signal into different frequency bands. The main essence of the work is the design and realization of Alfa Monitor – a device for relaxation , that for implamanting EEG biofeedback uses acoustic form and sensing of electrical activity of brain in the region of alpha waves. The second half of the work is t focused on circuit design, using integrated circuits with component values of relevant calculations . Further, it analyzes the practical implementation of alfa monitor. The last chapter is devoted to the testing the functionality of this device.
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Time Frequency Analysis of ERP Signals
Bartůšek, Jan ; Provazník, Ivo (referee) ; Černocký, Jan (advisor)
Tato práce se zabývá vylepšením algoritmu pro sdružování (clustering) ERP signálů pomocí analýzy časových a prostorových vlastností pseudo-signálů získaných za pomocí metody analýzy nezávislých komponent (Independent Component Analysis). Naším zájmem je nalezení nových vlastností, které by zlepšily stávající výsledky. Tato práce se zabývá použitím Fourierovy transformace (Fourier Transform), FIR filtru a krátkodobé Fourierovy transformace ke zkvalitnění informace pro sdružovací algoritmy. Princip a použitelnost metody jsou popsány a demonstrovány ukázkovým algoritmem. Výsledky ukázaly, že pomocí dané metody je možné získat ze vstupních dat zajímavé informace, které mohou být úspěšně použity ke zlepšení výsledků.
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Typing Using Brain Signals
Wagner, Lukáš ; Malinka, Kamil (referee) ; Tinka, Jan (advisor)
This bachelor thesis focusses on the implementation of a brain-computer interface, programmed in Python language, that would enable to communicate using EEG. The thesis investigates and evaluates existing brain-computer interface technologies for this purpose. The thesis also explores the use of machine learning applied to the technology, in particular neural networks, which have proven to be one of the most accurate methods of EEG signal processing. Following that, 3 different systems are proposed and implemented, each on different paradigm of visually evoking EEG potential changes. These systems were tested with different signal classification approaches. Unfortunately, none of the systems proved to be useful in communication.
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The adaptive EEG segmentation
Balcarová, Anežka ; Kozumplík, Jiří (referee) ; Kubicová, Vladimíra (advisor)
This project is aimed at EEG signal, especially at segmentation of signal and next at processing signal, witch this segmentation go before. Problem with signals stacionarity and stress importance of adaptive segmentation are outlined here. Principle of basic methods of adaptive segmentation are explained and two of them are processed by Matlab to segmentation channel. Parameters (limiting value and window length) influence segmentation. The limiting value is assessed with help of white noise.
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Analysis of connections between simultaneous EEG and fMRI data
Labounek, René ; Kremláček,, Jan (referee) ; Lamoš, Martin (advisor)
Electroencephalography and functional magnetic resonance are two different methods for measuring of neural activity. EEG signals have excellent time resolution, fMRI scans capture records of brain activity in excellent spatial resolution. It is assumed that the joint analysis can take advantage of both methods simultaneously. Statistical Parametric Mapping (SPM8) is freely available software which serves to automatic analysis of fMRI data estimated with general linear model. It is not possible to estimate automatic EEG–fMRI analysis with it. Therefore software EEG Regressor Builder was created during master thesis. It preprocesses EEG signals into EEG regressors which are loaded with program SPM8 where joint EEG–fMRI analysis is estimated in general linear model. EEG regressors consist of vectors of temporal changes in absolute or relative power values of EEG signal in the specified frequency bands from selected electrodes due to periods of fMRI acquisition of individual images. The software is tested on the simultaneous EEG-fMRI data of a visual oddball experiment. EEG regressors are calculated for temporal changes in absolute and relative EEG power values in three frequency bands of interest ( 8-12Hz, 12-20Hz a 20-30Hz) from the occipital electrodes (O1, O2 and Oz). Three types of test analyzes is performed. Data from three individuals is examined in the first. Accuracy of results is evaluated due to the possibilities of setting of calculation method of regressor. Group analysis of data from twenty-two healthy patients is performed in the second. Group EEG regressors analysis is realized in the third through the correlation matrix due to the specified type of power and frequency band outside of the general linear model.
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EEG Signal Processing and Analysis
Uhliarik, Michal ; Drahanský, Martin (referee) ; Kupková, Karolína (advisor)
Tato práce se zabývá oblastí elektroencefalografie, zpracováním EEG signálů a jejich analýzou. Jsou vysvětleny základní principy vzniku biologických signálů v mozku, charakteristické mozkové vlny a jejich klasifikace. Dále práce ilustruje základní metodologie měření a záznamu těchto signálů, chyby měření, vliv a zdroje signálových artefaktů. Následně je rozebrána problematika předzpracování signálu, nejrozšířenější metodologie, jejich primární určení a teoretické podklady. Zároveň je obsažen i přehled metod pro analýzu EEG signálu v časové, frekvenční a časově-frekvenční oblasti. Jádrem práce jsou metody analýzy EEG signálu ve frekvenční oblasti, jsou uvedeny jejich teoretické podklady, omezení, odchylky a zaměření, jako i vhodné matematické aparáty pro kompenzaci uvedených nedostatků. Praktická část popisuje architekturu a implementaci aplikace Easy EEG Player, která vznikla jako součást téhle práce. Jsou popsány metody reprezentace, zpracováni a analýzy EEG dat za použití zvolených metodologií.
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Numerosity in musicians
VOTAVOVÁ, Jana
This bachelor thesis focuses on a description of a connection between music and numerosity which is related to non-symbolic mathematics. It deals with the possibilities of how early intensive music training can affect mathematical skills, especially the approximal numerical system (ANS). Approximal numerical system ranks among three basic mathematical systems which form the basis of symbolic mathematics. In addition to the possibilities of connecting music and mathematics, the bachelor thesis also deals with mathematical anxiety which most often arises during the first contact with school mathematics and significantly affects the development of mathematical skills in gifted individuals. This thesis presents indisputable evidence that music training has an effect on mathematical skills and it could be used to train an approximate numerical system and thereby improve mathematical performance and as the case may be reduce mathematical anxiety. However, the main goal of this thesis is not only to provide evidence of the connection between music and mathematics but also to be used as an impulse for the further research
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Typing Using Brain Signals
Wagner, Lukáš ; Malinka, Kamil (referee) ; Tinka, Jan (advisor)
This bachelor thesis focusses on the implementation of a brain-computer interface, programmed in Python language, that would enable to communicate using EEG. The thesis investigates and evaluates existing brain-computer interface technologies for this purpose. The thesis also explores the use of machine learning applied to the technology, in particular neural networks, which have proven to be one of the most accurate methods of EEG signal processing. Following that, 3 different systems are proposed and implemented, each on different paradigm of visually evoking EEG potential changes. These systems were tested with different signal classification approaches. Unfortunately, none of the systems proved to be useful in communication.
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Time Frequency Analysis of ERP Signals
Bartůšek, Jan ; Provazník, Ivo (referee) ; Černocký, Jan (advisor)
Tato práce se zabývá vylepšením algoritmu pro sdružování (clustering) ERP signálů pomocí analýzy časových a prostorových vlastností pseudo-signálů získaných za pomocí metody analýzy nezávislých komponent (Independent Component Analysis). Naším zájmem je nalezení nových vlastností, které by zlepšily stávající výsledky. Tato práce se zabývá použitím Fourierovy transformace (Fourier Transform), FIR filtru a krátkodobé Fourierovy transformace ke zkvalitnění informace pro sdružovací algoritmy. Princip a použitelnost metody jsou popsány a demonstrovány ukázkovým algoritmem. Výsledky ukázaly, že pomocí dané metody je možné získat ze vstupních dat zajímavé informace, které mohou být úspěšně použity ke zlepšení výsledků.
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