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Evolutionary Optimization of the EEG Classifier Feature Extractor
Ovesná, Anna ; Hurta, Martin (referee) ; Mrázek, Vojtěch (advisor)
This work focuses on the optimisation of EEG signal classification of alcoholics and control subjects using evolutionary algorithms with a multi-objective approach. The main goal is to maximise the accuracy, sensitivity and specificity of the classification algorithm and minimise the number of features used. Four different classifiers are used, namely Support Vector Machine, k-nearest neighbors, Naive Bayes and AdaBoost. The selection of the best features is optimised using three different evolutionary approaches, two of which convert multi-objective optimisation to single-objective using weighted summation or restricting the maximum number of features. The Pareto optimal solutions are found by the NSGA-II algorithm. Results show that the evolutionary algorithms, combined with appropriate classifiers, reliably distinguish a person with a tendency to alcoholism from one with a healthy relationship towards alcohol.
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Methods for electroencephalogram records comparison
Kliment, Juraj ; Ronzhina, Marina (referee) ; Janoušek, Oto (advisor)
The bachelor thesis deals with the comparison of EEG signals from the human brain. The aim of this work is to find suitable nonlinear parameters, based on which it is possible to compare EEG records created in different conditions. The selected parameters are then tested on data from a publicly available database using Matlab software, the results are statistically processed and compared with the results of existing scientific studies. The comparison was based on the parameters Approximate entropy, Correlation dimension, Hurst exponent and Lyapunov exponent.
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Methods for electroencephalogram records comparison
Kliment, Juraj ; Ronzhina, Marina (referee) ; Janoušek, Oto (advisor)
The bachelor thesis deals with the comparison of EEG signals from the human brain. The aim of this work is to find suitable nonlinear parameters, based on which it is possible to compare EEG records created in different conditions. The selected parameters are then tested on data from a publicly available database using Matlab software, the results are statistically processed and compared with the results of existing scientific studies. The comparison was based on the parameters Approximate entropy, Correlation dimension, Hurst exponent and Lyapunov exponent.
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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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The influence of the nucleus accumbens on perception of facial expressions
VALUCHOVÁ, Kateřina
The bachelor thesis deals with the research of the stimulation of nucleus accumbens with laughter and the influence of this stimulation on the perception of facial expressions. The theoretical part is focused on an anatomical and functional description of nucleus accumbens and related researches. The information on laughter, facial expressions as well as electroencephalography (EEG) and event related potentials (ERP) are processed in the theoretical part. The empirical part introduces a research methodology that aims to determine the influence of laughter and associated nucleus accumbens stimulation on the perception and evaluation of neutral and positive facial expressions (obtained from the KDEF database). The experiment was attended by 16 subjects. The funny videos were showed to the subjects and each of them had the task to evaluate the presented facial expression. Neural data were obtained using an electroencephalogram and processed through the Matlab program, specifically in its EEGlab toolbox. Behavioral data were processed in the Statistica program and a paired T-test was used for statistical analysis. The final analysis of neural and behavioral data included 15 subjects. The resulting analysis showed that despite the stimulation with a humorous stimulus there was a different treatment and evaluation of neutral and positive facial expressions at both neural and behavioral level, which was in contradiction with the established hypotheses. The results did not show that the humorous stimulus used to stimulate nucleus accumbens had a statistically significant effect on the evaluation of facial expressions.
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General use of EEG sensors for mind controlled devices
Blažej, Svätopluk ; Sekora, Jiří (referee) ; Marcoň, Petr (advisor)
This bachelor thesis deals with various types of sensors for collection of EEG data and their application in mind-controlled devices. This work also deals with the issue of EEG signal measurement and its further analysis, how to choose the right sensor, and right design and construction of the device for data collection, amplification and filtration of the signal obtained by the selected sensor. Further, this project aims to develop software for translation (transformation) of the obtained data in order to enable communication and control of external devices.
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The influence of deep brain stimulation on the brain connectivity
Horváthová, Ľubica ; Výtvarová, Eva (referee) ; Klimeš, Petr (advisor)
Hĺbková mozgová stimulácia (DBS) predstavuje účinnú liečbu pre pacientov s Parkinsonovou chorobou (PD) alebo farmakorezistentnou epilepsiou. Avšak mechanizmy, ktorými znižuje počet záchvatov a zlepšuje pohyb, zostávajú ešte do značnej miery neznáme. Pre lepšie pochopenie a určenie, v ktorých frekvenčných pásmach je zmena najdôležitejšia, boli urobené porovnania medzi vypnutou a zapnutou DBS pomocou korelačnej metódy a indexu fázového posunu. Jedenásť pacientov s PD a naimplantovanými neurostimulátormi z firiem Medtronic a St.Jude Medical bolo predmetom nahraných dát použitých v tejto práci. Výsledky dokazujú, že zmena konektivity počas DBS nastane a zároveň, že najviac ovplyvňuje najvyššie frekvencie ako beta, nízka gama a vysoká gama. Zmeny v týchto frekvenciách, zodpovedné za motorickú aktivitu, sústredenie a spracovanie informácií, sú v súlade s klinickou teóriou o PD. Počas tejto choroby je patologická beta aktivita hypersynchronizovaná a gama aktivita je znížená práve v motorických oblastiach. Ak sa gama aktivita počas zapnutej stimulácie zvyšuje, fyziologický stav pacientov sa čiastočne znovuobnovuje a tým zlepšuje ich hybnosť. Metódy a výsledky tejto práce budú použité pre ďalší výskum pacientov s PD a epilepsiou.
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Sleep stages classification
Nováková, Kateřina ; Ronzhina, Marina (referee) ; Potočňák, Tomáš (advisor)
This work deals with the basic description of polysomnography, sleep morphology and sleep stages. Furtherly, some methods to process electroencephalographic signals are mentioned. Those processing methods are mainly focused on sleep stage classification. The practical part deals with the realization of three classification algorithms using artificial neural networks and verifying the functionality of these methods. All algorithms are designed in Matlab. Feature vectors for individual methods are obtained using energy values, Welch's spectral analysis and Hilbert-Huang Transform. For classification three types of artificial neural networks were used - layer recurrent network, feedforward network and pattern recognition network. On the basis of feature vectors, the sleep is divided into three stages - wakefulness (W), sleep without rapid eye movements (NREM) and sleep with rapid eye movements (REM).
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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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Electrical Activity Brain Mapping
Dobeš, Petr ; Drahanský, Martin (referee) ; Kupková, Karolína (advisor)
Electrical activity of human brain is one of the most significant signals of biological origin. In order to understand and interpret electroencephalogram (EEG signal) correctly, it is often necessary to perform its visualization. This bachelor thesis deals with EEG signal and its visualization using topographic mapping. The work includes the basics of theory and processing of EEG signal. Moreover, this work consists of design proposal and implementation of an application for topographic mapping of EEG signal obtained using Emotiv Epoc Headset device. Visualization is performed in real time (at the time of measurement). Visualized quantities are amplitude and frequency domain with the possibility to select frequency bands. Implemented application represents an alternative to the procedure when EEG signal has to be recorded and stored in order to perform its visualization.
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