Abstract
Functional neuroimaging methods give the opportunity of investigating human brain functioning. Mostly used functional neuroimaging techniques include Electroencephalogram (EEG), functional magnetic resonance imaging (fMRI), positron emission tomography (PET) and optical imaging. Among these techniques EEG has the best time resolution, while fMRI has the best spatial resolution. High temporal resolution of EEG is an attractive property for neuroimaging studies. EEG inverse problem is needed to be solved in order to identify the locations and the strength of the electrical sources forming EEG/ERP topographies. Low spatial resolution of the scalp topography causes this localization problem more complicated. In this paper, a spatial preprocessing method, which separates a topography into two or more subtopographies is proposed. The decomposition procedure is based on defining a spatial map with radial basis functions which forms the subtopographies. A simulated data is used to exhibit the advantage of using this decomposition technique prior to EEG source localization. It is shown that the accuracy of the source localization problem is improved by using the subtopographies instead of using the raw topography.
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Duru, A.D., Ademoglu, A. (2008). Source Localization of Subtopographies Decomposed by Radial Basis Functions. In: Dohi, T., Sakuma, I., Liao, H. (eds) Medical Imaging and Augmented Reality. MIAR 2008. Lecture Notes in Computer Science, vol 5128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79982-5_12
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DOI: https://doi.org/10.1007/978-3-540-79982-5_12
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