ABSTRACT
Electroencephalography (EEG) and magnetic resonance imaging (MRI) are noninvasive neuro-imaging modalities largely used in neurology explorations. MRI is considered as a static modality and could be so important for anatomy by its high spatial resolution. EEG, on the other hand, is an important tool permitting to image temporal dynamic activities of the human brain. Fusion of these two essential modalities would be hence a so emerging research domain targeting to explore brain activities with the MRI static modality. Our present research investigates a sophisticated approach for localization of the cerebral activity that could be involved by the dynamic EEG modality and carefully illustrated within MRI static modality. Such careful cerebral activity localization would be first based on an advanced methodology yielding therefore a singular value decomposition-based lead field weighting to sLORETA method formalism, for solving in fact the inverse problem in the EEG. The conceived method for source localization, carried out on different cases of simulated dipoles experiments, showed satisfactory results. Different cases of simulated dipoles experiments and metrics were used to confirm the reliability of the proposed method. The experimental results confirm that our method presents a flexible and robust tool for EEG source imaging.
Subject(s)
Brain Mapping/methods , Brain/physiology , Electroencephalography/methods , Image Interpretation, Computer-Assisted/methods , Imaging, Three-Dimensional/methods , Models, Neurological , Algorithms , Computer Simulation , Humans , Models, Statistical , Nerve Net/physiology , Reproducibility of Results , Sensitivity and Specificity , Spatio-Temporal AnalysisABSTRACT
In this paper we present a new approach which combines the two methods of cerebral electric activity's localization: "Weighted Minimum Norm" (WMN) and the iterative method "FOCal Underdetermined System Solver" (FOCUSS). Our idea is to use the current density distribution estimated by the WMN method in order to initialize the weighting matrix necessary for the localization with FOCUSS method. We compare the found results with those of the traditional WMN and FOCUSS methods in term of computing time and resolution matrix. The presented results show that our approach gives a good localization of the active sources in the brain.
