Abstract:
Localization of the cognitive activity in the brain is one of the major problems inneuroscience. Current techniques for neuro-imaging are based on fMRI, PET, and ERPrecordings. The highest temporal resolution, which is crucial for temporal localization of activities, is achieved by ERP, but spatial resolution of scalp topography is low. To overcomethe limitation of scalp topography, several current-density estimation techniques weredeveloped whose goal is to find the locations of the three-dimensional (3D) intracerebral activities by solving an inverse problem. However, scalp topologies constituted by multiplesources which makes the inverse problem more complicated. The overall objective of thisthesis is to perform spatial analysis of scalp topography by 2-D wavelet transform and isolatespatial frequency components. This analysis could give us less complex scalp maps for sourcedetection. In this thesis, in order to see the topographic variations in neurocognitive processes, the ERP recordings were spatially enhanced by interpolation as a first step. At the secondstep, main topologies of ERP recordings were investigated by hierarchical clusteringalgorithm. Thirdly, different spatial frequencies of these main topologies were separated by 2-D wavelet transform. Finally, main topological maps and topographic maps of different spatial frequencies derived from them were used to find corresponding cortical activities(cortical activity maxima) by LORETA (Low Resolution Electromagnetic Tomography).Assessment of our spatial analyzing results was made according to the current densityestimation results.|Keywords: ERP, scalp topography, spatial analysis, 2-D wavelet, LORETA.
