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.

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.

This paper addresses the problem of detecting K-complexes in sleep EEG. The study of sleep has become very essential to diagnose the brain disorders and analysis of brain activities. Since Kcomplex can have a wide variety of shapes it is very difficult to detect the K-complexes manually. In this paper, I present an automatic method for K-complexes detection based wavelet transform,TKEO and method for classification using feedforward multilayer neural network designed in Matlab. Detection performance reached the value approx. from 52,9 to 83,6 %.

This master thesis dwells on EEGbiofeedback (also called Neurofeedback) interface of human brain and the computer and its concrete realization in Java programming language. This system is founded on the basis of the computer, which is accomplishing biological feedback (biofeedback) and the electroencephalography (EEG) by helping that state's scanning of user's brain is realized. By this way is possible to practise the human brain effectively to achieve better concentration, the elimination of sleeping and learning deficiency. Hereafter is the suggestion of direction control of computer mouse by EEG device incorporated, which makes it possible for the man to regulate the direction of the cursor's movement on the screen by the frequency of brain's oscillation. The motivation for solution of this problem is the effort to help to handicapped people to communicate with surrounding world. The introduction of this paper contains the basic facts about human brain, electroencephalography and EEG biofeedback. The following chapters dwell on the specification of claims to developed application, its suggestion and description of actual realization. The final part relates to the BCI (Brain-Computer Interface) systems and suggestion of computer's control by EEGappliance with evaluation of attained results.

This master thesis deals with classification of sleep stages on the base of polysomnographic signals. On several signals was performed analysis and feature extraxtion in time domain and in frequency domain as well. For feature extraxtion was used EEG, EOG and EMG signals. For classification was selected classification models K-NN, SVM and artifical neural network. Accuracy of classifation is different depending on used method and spleep stages split. The best results achieved classification among stages Wake, REM, and N3, with neural network usage. In this case the succes was 93,1 %.

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.

This thesis deals with wavelet analysis of sleep electroencephalogram to sleep stages scoring. The theoretical part of the thesis deals with the theory of EEG signal creation and analysis. The polysomnography (PSG) is also described. This is the method for simultaneous measuring the different electrical signals; main of them are electroencephalogram (EEG), electromyogram (EMG) and electrooculogram (EOG). This method is used to diagnose sleep failure. Therefore sleep, sleep stages and sleep disorders are also described in the present study. In practical part, some results of application of discrete wavelet transform (DWT) for decomposing the sleep EEGs using mother wavelet Daubechies 2 „db2“ are shown and the level of the seven. The classification of the resulting data was used feedforward neural network with backpropagation errors.

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.

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.

Diploma thesis – the Biological feedback in therapy deals with electromyography (EMG), investigative techniques based on electromyography, myofeedback applications and intention controlled myofeedback (IMF therapy). Electromyography is described on principal and there are used electrodes presented. In investigative methods are mentioned their basic progress at investigation by the help of elektromyography. Next are present artifacts, which in the elektromyography signal values. For myofeedback I worked up basic study. Myofeedback is based principle in IMF therapy. The effect of IMF therapy was verified with synchronized signal electroencephalograph (EEG) and EMG. In concrete I worked with movement-related cortical potentials (MRCP) components and Bereitschaftspotential (BP). BP presents highlight component from MRCP and alone BP precluding fulfillment movement. It was tested on metering, that BP occur before executed movement. Idea IMF therapy is correct.