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EEG Biofeedback Application
Zapletal, Jakub ; Drahanský, Martin (referee) ; Tinka, Jan (advisor)
Tato práce je shrnutím existujících přístupů pro zpracování EEG signálu za účelem EEG biofeedbacku a dále popisuje návrh a implementaci vlastní aplikace pro EEG biofeedback se zaměřením na trénink pozornosti. Dále obsahuje případovou studii provedenou na neurotypickém studentovi a studentovi s ADHD, která zkoumá vliv implementované aplikace na měřený EEG signál subjektů.
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Analysis of electroencephalograms
Gajdoš, Martin ; Rozman, Jiří (referee) ; Kolářová, Jana (advisor)
Electroencephalography is sensitive diagnostic method for measuring electric potentials generated by brain. In this project are described the properties of the EEG signal and methods of EEG measuring, processing and evaluating. Also the noise sources and methods for noise removing are described. The project deals in the second part with detection of drowsiness and microsleep from driver’s EEG. At first theoretically, then is shown the practical measurement of EEG on volunteers. During the measurement was intention to induce drowsiness and microsleep. Finally is described the processing of measured EEG signals and the results are visualized.
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EEG Signal Analysis during the Stroop Test
Tolaszová, Eva ; Roman,, Robert (referee) ; Sekora, Jiří (advisor)
Master’s thesis deals with the measurement of biological signals for the effect of psychological burden. To monitor this effect was elected Stroop test, which is in the psychology used to detect disorders of attention and concentration. EEG and ECG signals during Stroop test were obtained using the EEG recording systém, in the context of research evoked potentials. As a part of the work it has been designed custom application for analyzing and interpreting data and statistical analysis by t-test.
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Data Analysis and Clasification from the Brain Activity Detector
Jileček, Jan ; Černocký, Jan (referee) ; Szőke, Igor (advisor)
This thesis aims to implement methods for recording EEG data obtained with the neural activity sensor OpenBCI Ultracortex IV headset. It also describes neurofeedback, methods of obtaining data from the motor cortex for further analysis and takes a look at the machine learning algorithms best suited for the presented problem. Multiple training and testing datasets are created, as well as a tool for recording the brain activity of a headset-wearing test subject, which is being visually presented with cognitive challenges on the screen in front of him. A neurofeedback demo app has been developed, presented and later used for calibration of new test subjects. Next part is data analysis, which aims to discriminate the left and right hand movement intention signatures in the brain motor cortex. Multiple classification methods are used and their utility reviewed.
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EEG signal measurement using acquisition PC card
Polák, Radek ; Kolářová, Jana (referee) ; Kolář, Radim (advisor)
The build preamplifier and the EEG Signal Measure Virtual Machine is the aim of this project. The abstract of actual clinical electroencefalography, the ways of measurement and processing of brain signals by clinical machines, and introducing my way of measure signal are placed in this work. The preamplifier has been designed for amplification of the brain signal, LabView virtual machine for maesurement and processing amplified signal.This project includes the measure output, the description of virtual machine, and the advice using of this product.
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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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Automatic measurement of characteristics of ampliers used for biosignals
Flídr, Karel ; Balogh, Jaroslav (referee) ; Harabiš, Vratislav (advisor)
This thesis is dedicated to operational amplifiers for biological signals. This work provides basic electrodiagnostic methods for scanning of biosignals. The work describes the processing of biological signals and types of sensors used for scanning of biological signals. Then there is described the development environment LabVIEW and NI ELVIS II development board. Finally, there is described an application developed in LabVIEW environment for measuring amplifiers attributes.
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Virtual Robot Control Using EEG
Drla, Michal ; Goldmann, Tomáš (referee) ; Tinka, Jan (advisor)
This bachelor thesis aimed to create an application where is user able to control the virtual robot with an EEG signal. The thesis contains a brief introduction that explains how BCI systems which are using EEG work. This introduction not only explains the basics of EEG analysis but also explains brain biology and shows different signals which are extractable from the brain. This thesis also explains the theory of neural networks which are used to implement the analysis. In implementation are shown scripts that were used to collect data and there is also shown the design of the neural network. Results of testing are good, the neural network was making correct decisions and the user was able to control the virtual robot.
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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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