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Spectral Analysis of EEG Signal
Miklenda, Petr ; Orság, Filip (referee) ; Kupková, Karolína (advisor)
This bachelor thesis is focused at the EEG signal analysis using the Fast Fourier Transform. The theoretical part consist of EEG signal description, its creation and selected method for signal analysis. The practical part presents program for recording data from EEG headset and displaying the Fourier spectrum.
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Spectral analysis of the EEG signal
Blatný, Michal ; Kozumplík, Jiří (referee) ; Hrozek, Jan (advisor)
Master’s thesis deal about electroencefalography, measurement EEG signals and analysis measuermed signals. Project contains two basis practical parts. Firts part contain two PC’s programs that’s are used to fundamental analysis to frequence-domain and visual display of brain mapping created with Matlab. Second chapter of practical parts includes two PC’s programs created with LabView. First of them is the EEG biofeedback making use for advanced analyses and second program is used to detection segment of stacionarity.
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Electroencephalography
Jarošová, Veronika ; Ronzhina, Marina (referee) ; Chmelař, Milan (advisor)
This semestral paper has its aim to describe the electroencephalography. The beginning of paper is devoted to the history of electroencephalography, then there is both spontaneous and evoked brain activity described. Further, the paper deal with the electroencephalography. At the conclusion of teoretical part is described the methodology and processing of electroencephalography signal. The practical part of the work is focused on the creation of three various exercises on the epilepsy theme, reflexes and memory. Then the exercises were verified practically at fourteen volunteers. Further, in the work there is mentioned the instruction for particular measures, valuation results and its discussion.
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Biofeedback and its practical use
Dvořák, Jiří ; Hrozek, Jan (referee) ; Čmiel, Vratislav (advisor)
The aim of this work is describe common methods of biological feedback therapy that is used to treat some psychosomatic diseases. Subsequently, the description is focused on minimal brain dysfunction treatment by the help of EEG biofeedback. Properties and technical requirements for this therapy are concretized. The last part of this thesis is dedicated to the design and realization of practical software tool for EEG biofeedback therapy which is made in LabView 7.1. The M535 acquisition unit and NI USB-6221 measuring device are used for hardware solution.
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EEG Analysis During Anaesthesia
Hodulíková, Tereza ; Kolářová, Jana (referee) ; Sadovský, Petr (advisor)
This master's thesis deals with the method of functional examination of brain electric activity. In the first part is description of central nervous system, method of electroencephalography and possible connections. Furthermor the project involves characteristic of EEG signal and its artifacts. It also includes signal processing and list of symptoms, which will be used for an analysis of the EEG during anesthesia. The second part of thesis involves development of application, which allow viewing and proccesing of EEG signal. In conclusion of thesis is carried out unequal segmentation and statistical processing.
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Effect of emotive stimulation in EEG signal
Vaněčková, Tereza ; Ronzhina, Marina (referee) ; Bubník, Karel (advisor)
This thesis deals with emotions and their effect on EEG signal. Firstly, method of electroencephalography, the method of scanning EEG signal, its properties, frequency bands and signal affecting factors are described. The following is an explanation of emotions, its expression, theories of emotion origin, dimensions, classification and lateralization of emotional experience. Furthermore, review of studies that have influenced this work is provided. The practical part consists of the experimental measurement description, principle of stimuli selection, signal EEG recording using the Emotiv EPOC device and the Self-Assessment Manikin evaluation. There are also clarified methods of data processing and selection of emotion related features of EEG signal. The final section summarizes the achieved results and outlines possible continuation of emotional states recognizing.
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Data Analysis and Clasification from the Brain Activity Detector
Ullrich, Petr ; Šůstek, Martin (referee) ; Szőke, Igor (advisor)
This thesis deals with the issue of recording brain activity, implementation of its processing, analysis and classification. The OpenBCI hardware is used for recording. I have studied and described necessary information about recording brain activity and OpenBCI project. Design for data set, data processing and thoughts classification was created. Created system allows classification based on recorded brain activity. The neural network was used for classfication, but the success of the recognition of designed classes was not high.
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Wheelchair control using EEG signal classification
Malý, Lukáš ; Sadovský, Petr (referee) ; Žalud, Luděk (advisor)
Tato diplomová práce představuje koncept elektrického invalidního vozíku ovládaného lidskou myslí. Tento koncept je určen pro osoby, které elektrický invalidní vozík nemohou ovládat klasickými způsoby, jakým je například joystick. V práci jsou popsány čtyři hlavní komponenty konceptu: elektroencefalograf, brain-computer interface (rozhraní mozek-počítač), systém sdílené kontroly a samotný elektrický invalidní vozík. V textu je představena použitá metodologie a výsledky provedených experimentů. V závěru jsou nastíněna doporučení pro budoucí vývoj.
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Person Identification and Verification Using EEG
Žitný, Roland ; Orság, Filip (referee) ; Tinka, Jan (advisor)
The aim of this work was to create a brain-computer interface that reliably identifies and verifies a person using his electroencephalographic signals. Creating a user profile and verifying it is based on processing reactions to his own face, and the face of strangers or acquaintances. Algorithms such as bandpass and noise removal using wavelet transformation are user to filter signals. The classification of reactions is performed using a convolutional neural network or linear discriminant analysis. The average accuracy of the linear discriminant analysis is 66.2 % and of the convolutional neural network is 58.7 %. The maximum achieved accuracy was with linear discriminant analysis and at 93.7 %.
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Acoustic generator for evoked potentials stimulation
Škutková, Helena ; Rampl, Ivan (referee) ; Chmelař, Milan (advisor)
Evoked potentials are electric brain response to external stimulus. They are important diagnostic no visual method in neurology. For their excitation use of different of kinds stimulation, most often: visual, auditory, somatosenzory, olfactory and gustatory. Evoked potentials are objective method for measurement sense perception. This master’s thesis is specialized to auditory evoked potentials and design acoustic generator for their stimulation. Auditory evoked potentials are primary used for objective audiometry, but they have another usage. In the first place, application is specialized on health sector. The aim of this master’s thesis is compact specified medical requirements with available technical resources.
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