Abstract:
Electroencephalography (EEG) is a common technique for studying and understanding the functioning of the brain. In addition, functional Magnetic Resonance Imaging (fMRI), in the recent years has been a very conventional method for neuroimaging. The most important property of the EEG, which makes it superior to other neuroimaging modalities is its very high temporal resolution. EEG reflects functional activities in the range of milliseconds. However, due to limited number of electrode measurements and some modeling failures, it can provide limited spatial resolution. fMRI provides satisfactory spatial resolution for imaging of these processes but it lacks good temporal resolution. In this thesis, the steady state human visual evoked potentials and their corresponding fMRI scans are processed using EEG source reconstruction and fMRI statistical parametric mapping methods. The visual stimulations are ranging from 2 to 10 Hz. The fMRI voxels which proved significantly active were correlated with their associated EEG neuroelectric power which was determined on the same geometric head with Low Resolution Electromagnetic Tomography (LORETA). Spatially averaged positive BOLD, post-stimulus undershoot and LORETA amplitudes are determined across the frequencies as well as the spatial correlations between the positive BOLD and LORETA amplitudes over an activation mask. Finally, the correlation between the standardized regression parameter due to the steady state visual effect and the LORETA amplitudes were also computed over the frequencies. The most consistent observation for all these analyses is the significant activation increase at 8 Hz together with a strong correlation between the two imaging modalities.|Keywords: fMRI, EEG, Source Localization, Statistical Parametric Mapping, General Linear Model, Statistical Inference, T Test, Forward Problem, Inverse Problem, LORETA.
