Neuroimaging of brain activity using spatio-temporal signal modelling

Functional neuroimaging enables us to obtain information about how the brain responds to cognitive and/or emotional tasks.Neuroimaging of brain activity requires spatio-temporal modelling of measured electrical and/or hemodynamic data and integration of the measurements obtained at different spatial and or temporal scales. In this thesis, new techniques are employed for the investigation of spatio-temporal dynamics of different functional data as the EEG-ERP, the invasive/non-nonivasive recordings of epileptic EEG, and simultaneously recorded steady state EEG-fMRI. Spatio-temporal wavelet decompositions using realistic head models are applied in order to produce simple stationary input subtopographies for the source localization. Besides, a spatial decomposition method based on radial basis functions is used. The usage of the subtopographies facilitate the inverse solution and it is shown that even the temporally correlated EEG sources can be localized by this approach.Integration of the data obtained from different spatial scales is an important problem in epileptic EEG. To assess their reliability, the spatial performance of the scalp EEG based inverse solutions are compared with deep or cortical measurements and their simultaneously measured datasets. The multimodal functional information integration is proposed to compare the dynamics deduced by the simultaneously recorded SSVEP and fMRI. The temporal correlation between the time series of EEG and fMRI is calculated via the GLM. It is observed that the SSVEP source maps are the spatial subsets of the fMRI activity.The study demonstrates the applicability and potential of new spatio-temporal methods in EEG research which can be used to study cognition, attention, memory, and perception. Proposed methods can also be used as tools in more practical areas like brain computer interfacing, neurosurgical planning and neuro-psychological assessment of certain disorders.