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Analysis of connections between simultaneous EEG and fMRI data
Labounek, René ; Kremláček,, Jan (referee) ; Lamoš, Martin (advisor)
Electroencephalography and functional magnetic resonance are two different methods for measuring of neural activity. EEG signals have excellent time resolution, fMRI scans capture records of brain activity in excellent spatial resolution. It is assumed that the joint analysis can take advantage of both methods simultaneously. Statistical Parametric Mapping (SPM8) is freely available software which serves to automatic analysis of fMRI data estimated with general linear model. It is not possible to estimate automatic EEG–fMRI analysis with it. Therefore software EEG Regressor Builder was created during master thesis. It preprocesses EEG signals into EEG regressors which are loaded with program SPM8 where joint EEG–fMRI analysis is estimated in general linear model. EEG regressors consist of vectors of temporal changes in absolute or relative power values of EEG signal in the specified frequency bands from selected electrodes due to periods of fMRI acquisition of individual images. The software is tested on the simultaneous EEG-fMRI data of a visual oddball experiment. EEG regressors are calculated for temporal changes in absolute and relative EEG power values in three frequency bands of interest ( 8-12Hz, 12-20Hz a 20-30Hz) from the occipital electrodes (O1, O2 and Oz). Three types of test analyzes is performed. Data from three individuals is examined in the first. Accuracy of results is evaluated due to the possibilities of setting of calculation method of regressor. Group analysis of data from twenty-two healthy patients is performed in the second. Group EEG regressors analysis is realized in the third through the correlation matrix due to the specified type of power and frequency band outside of the general linear model.
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Joint EEG-fMRI analysis based on heuristic model
Janeček, David ; Kremláček, Jan (referee) ; Labounek, René (advisor)
The master thesis deals with the joint EEG-fMRI analysis based on a heuristic model that describes the relationship between changes in blood flow in active brain areas and in the electrical activity of neurons. This work also discusses various methods of extracting of useful information from the EEG and their influence on the final result of joined analysis. There were tested averaging methods of electrodes interest, decomposition by principal components analysis and decomposition by independent component analysis. Methods of averaging and decomposition by PCA give similar results, but information about a stimulus vector can not be extracted. Using ICA decomposition, we are able to obtain information relating to the certain stimulation, but there is the problem in the final interpretation and selection of the right components in a blind search for variability coupled with the experiment. It was found out that although components calculated from the time sequence EEG are independent for each to other, their spectrum shifts are correlated. This spectral dependence was eliminated by PCA / ICA decomposition from vectors of spectrum shifts. For this method, each component brings new information about brain activity. The results of the heuristic approach were compared with the results of the joined analysis based on the relative and absolute power approach from frequency bands of interest. And the similarity between activation maps was founded, especially for the heuristic model and the relative power from the gamma band (20-40Hz).
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Inverse values of EEG signal power in joint EEG-fMRI analysis
Sanetrníková, Dominika ; Kolář, Radim (referee) ; Labounek, René (advisor)
The first part of this thesis summarizes the basic theory of brain activity measurement using the BOLD signal and scalp EEG, the effect of noise phenomena in the data and its suppression, the merger of the fusion of the measured data using the general linear model and the current implementation of computational algorithms in the software library EEG Regressor Builder 1.0. Within the own solution of this thesis, the changes of the software library to version 1.1 were realized according to the requirements of the bachelor thesis. The hypothesis that temporal changes of the EEG relative band power (20 - 40Hz) has the same spatial correlates with the BOLD signal as the inverse power in the frequency range 0-12Hz. The hypothesis was rejected based on the calculation of similarity criterions between 3D activation maps for different parameter settings of the joint analysis calculations. As an appropriate criterions were chosen the correlation coefficient and the cosine criterion. The Euclidean distance was proved to be unfit. Also it was proved the inverse power value of EEG signal in the given frequency band brings to the common EEG-fMRI analysis an anti-correlated signal to the normal absolute power in the same frequency band. Furthermore the influence of regressors describing motion artifacts reduces the number of supra-thresholded voxels.
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Inverse values of EEG signal power in joint EEG-fMRI analysis
Sanetrníková, Dominika ; Kolář, Radim (referee) ; Labounek, René (advisor)
The first part of this thesis summarizes the basic theory of brain activity measurement using the BOLD signal and scalp EEG, the effect of noise phenomena in the data and its suppression, the merger of the fusion of the measured data using the general linear model and the current implementation of computational algorithms in the software library EEG Regressor Builder 1.0. Within the own solution of this thesis, the changes of the software library to version 1.1 were realized according to the requirements of the bachelor thesis. The hypothesis that temporal changes of the EEG relative band power (20 - 40Hz) has the same spatial correlates with the BOLD signal as the inverse power in the frequency range 0-12Hz. The hypothesis was rejected based on the calculation of similarity criterions between 3D activation maps for different parameter settings of the joint analysis calculations. As an appropriate criterions were chosen the correlation coefficient and the cosine criterion. The Euclidean distance was proved to be unfit. Also it was proved the inverse power value of EEG signal in the given frequency band brings to the common EEG-fMRI analysis an anti-correlated signal to the normal absolute power in the same frequency band. Furthermore the influence of regressors describing motion artifacts reduces the number of supra-thresholded voxels.
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Joint EEG-fMRI analysis based on heuristic model
Janeček, David ; Kremláček, Jan (referee) ; Labounek, René (advisor)
The master thesis deals with the joint EEG-fMRI analysis based on a heuristic model that describes the relationship between changes in blood flow in active brain areas and in the electrical activity of neurons. This work also discusses various methods of extracting of useful information from the EEG and their influence on the final result of joined analysis. There were tested averaging methods of electrodes interest, decomposition by principal components analysis and decomposition by independent component analysis. Methods of averaging and decomposition by PCA give similar results, but information about a stimulus vector can not be extracted. Using ICA decomposition, we are able to obtain information relating to the certain stimulation, but there is the problem in the final interpretation and selection of the right components in a blind search for variability coupled with the experiment. It was found out that although components calculated from the time sequence EEG are independent for each to other, their spectrum shifts are correlated. This spectral dependence was eliminated by PCA / ICA decomposition from vectors of spectrum shifts. For this method, each component brings new information about brain activity. The results of the heuristic approach were compared with the results of the joined analysis based on the relative and absolute power approach from frequency bands of interest. And the similarity between activation maps was founded, especially for the heuristic model and the relative power from the gamma band (20-40Hz).
|
|
|
Analysis of connections between simultaneous EEG and fMRI data
Labounek, René ; Kremláček,, Jan (referee) ; Lamoš, Martin (advisor)
Electroencephalography and functional magnetic resonance are two different methods for measuring of neural activity. EEG signals have excellent time resolution, fMRI scans capture records of brain activity in excellent spatial resolution. It is assumed that the joint analysis can take advantage of both methods simultaneously. Statistical Parametric Mapping (SPM8) is freely available software which serves to automatic analysis of fMRI data estimated with general linear model. It is not possible to estimate automatic EEG–fMRI analysis with it. Therefore software EEG Regressor Builder was created during master thesis. It preprocesses EEG signals into EEG regressors which are loaded with program SPM8 where joint EEG–fMRI analysis is estimated in general linear model. EEG regressors consist of vectors of temporal changes in absolute or relative power values of EEG signal in the specified frequency bands from selected electrodes due to periods of fMRI acquisition of individual images. The software is tested on the simultaneous EEG-fMRI data of a visual oddball experiment. EEG regressors are calculated for temporal changes in absolute and relative EEG power values in three frequency bands of interest ( 8-12Hz, 12-20Hz a 20-30Hz) from the occipital electrodes (O1, O2 and Oz). Three types of test analyzes is performed. Data from three individuals is examined in the first. Accuracy of results is evaluated due to the possibilities of setting of calculation method of regressor. Group analysis of data from twenty-two healthy patients is performed in the second. Group EEG regressors analysis is realized in the third through the correlation matrix due to the specified type of power and frequency band outside of the general linear model.
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