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Dectection of brain wakefulness from scalp EEG data with higher order statistics
Semeráková, Nikola ; Ronzhina, Marina (referee) ; Labounek, René (advisor)
Presented master's thesis deals with detection of brain wakefulness from scalp EEG data with higher order statistics. Part of the thesis is a description of electroencephalography, from the method of signal generation, sensing, electroencephraphy, EEG signal artifacts, frequency bands of EEG signal to its possible processing. Furthermore, the concept of mental fatigue and the possibility of its detection in the EEG signal is described. Subsequently, the principles of higher statistical methods of PCA and ICA and the specific possibilities of decomposition of EEG signal are described using these methods, from which the method of group spatial-frequency ICA was chosen as a suitable method for selection of partial oscillatory sources in EEG signal. In the next part there is described a method of acquisition of data, a the suggestion of solution with selected method and a description of the implemented algorithm, that was applied to real 256-lead scalp EEG data captured during a block task focused on subject allertnes. The absolute and relative power of the EEG signal was decomposed. From the achieved results, we observe that the fluctuations of the spatial frequency patterns of relative power (especially for theta and alpha bands) significantly more closely correspond with the change of reaction time and the error of the subjects performing the task. These observations appear to be relatively consistent with previously published literature, and the current study shows that spatial frequency ICA is able to blindly isolate space-frequency patterns whose fluctuations are statistically significantly correlated with parameters (reaction time, error rate) directly flowing from the given task.
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Dectection of brain wakefulness from scalp EEG data with higher order statistics
Semeráková, Nikola ; Ronzhina, Marina (referee) ; Labounek, René (advisor)
Presented master's thesis deals with detection of brain wakefulness from scalp EEG data with higher order statistics. Part of the thesis is a description of electroencephalography, from the method of signal generation, sensing, electroencephraphy, EEG signal artifacts, frequency bands of EEG signal to its possible processing. Furthermore, the concept of mental fatigue and the possibility of its detection in the EEG signal is described. Subsequently, the principles of higher statistical methods of PCA and ICA and the specific possibilities of decomposition of EEG signal are described using these methods, from which the method of group spatial-frequency ICA was chosen as a suitable method for selection of partial oscillatory sources in EEG signal. In the next part there is described a method of acquisition of data, a the suggestion of solution with selected method and a description of the implemented algorithm, that was applied to real 256-lead scalp EEG data captured during a block task focused on subject allertnes. The absolute and relative power of the EEG signal was decomposed. From the achieved results, we observe that the fluctuations of the spatial frequency patterns of relative power (especially for theta and alpha bands) significantly more closely correspond with the change of reaction time and the error of the subjects performing the task. These observations appear to be relatively consistent with previously published literature, and the current study shows that spatial frequency ICA is able to blindly isolate space-frequency patterns whose fluctuations are statistically significantly correlated with parameters (reaction time, error rate) directly flowing from the given task.
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