The thesis deals with EEG signals, their description, methods of quantitative analysis and the processes in time-frequency domains, or power spectrums. The relationsheep between brain electrical activity and acustic stimuli (Mozart´s “Sonata K448”) was studied using EEG analysis in relation to sound impulses from replayed extracts of. The proposed experiment protocol included recording of EEG of volunteers. In order to visualize and analyze the data, the software with the graphic user interface was created, which enables topological mapping of brain activity and its vizualization in the time-frequency domain.

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.