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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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Brain wakefullness detection using frequency and time-frequency EEG signal analysis
Pohludka, Aleš ; Ronzhina, Marina (referee) ; Potočňák, Tomáš (advisor)
This work describes basics of electroencephalography, measuring methods of electroen- cephalographic signals, their processing and especially the interpretation of EEG signal in frequency and time-frequency domains for mental fatigue detection purposes. Mental fatigue, its sources, consequences and connection with sensory-cognitive system and link to memory is discussed. The most basic normalized international system for measuring EEG from the scalp as well as some of the experiments that ultimately lead to mental fatigue are described. With this knowledge in mind, an experiment was prepared for inducing such a state. Ten subjects participated in the test which was conducted in la- boratory with EEG machine GES 410MR by EGI. The data were analyzed mainly with S-transform and Hilbert-Huang transform. These two transforms represent two distinct state of the art time-frequency methods of spectral analysis. The result of this work lies in evaluating the relationship between mental fatigue, errors accumulated during the task and with time.
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Subjective perception of nurses fatigue in the ward with Covid-19 positive patients
Vosátková, Laura ; Jirkovský, Daniel (advisor) ; Haluzíková, Jana (referee)
The bachelor thesis deals with the workload of nurses caring for COVID-19 positive patients. The aim of the questionnaire survey was to determine the workload of these nurses and to determine whether the workload of the Covid-type ward exceeds the workload of the standard non-Covid ward. Data collection took place in the inpatient, ICU and ARO wards of the Rudolf and Stefanie Hospital in Benešov, which cared for COVID-19 positive patients. The questionnaire survey took place from November 2021 to January 2022. The target group of respondents consisted of practical and general nurses. A data sheet created by Microsoft Excel was used for data processing, and STATISTICA CZ 12 was used for basic statistical analyzes and frequency tables. From the results of the survey it was found that the share, resp. 73.44 % of respondents reported a rate of subjective experiences that matched congestion in Covid's department. 90.63 % of respondents reported a share of subjective experiences in the Covid department that corresponded to monotony. The share of the combination of subjective experiences of overload and monotony was stated by 67.19 % of respondents. 20.31% of respondents and 1.56% of respondents experienced an unfavorable workload in the ward with COVID-19 positive patients. It can be concluded that the...
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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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Brain wakefullness detection using frequency and time-frequency EEG signal analysis
Pohludka, Aleš ; Ronzhina, Marina (referee) ; Potočňák, Tomáš (advisor)
This work describes basics of electroencephalography, measuring methods of electroen- cephalographic signals, their processing and especially the interpretation of EEG signal in frequency and time-frequency domains for mental fatigue detection purposes. Mental fatigue, its sources, consequences and connection with sensory-cognitive system and link to memory is discussed. The most basic normalized international system for measuring EEG from the scalp as well as some of the experiments that ultimately lead to mental fatigue are described. With this knowledge in mind, an experiment was prepared for inducing such a state. Ten subjects participated in the test which was conducted in la- boratory with EEG machine GES 410MR by EGI. The data were analyzed mainly with S-transform and Hilbert-Huang transform. These two transforms represent two distinct state of the art time-frequency methods of spectral analysis. The result of this work lies in evaluating the relationship between mental fatigue, errors accumulated during the task and with time.
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