Development of a realistic head model for EEG event-detection and source localization in newborn infants.
In this work we present an integrated method for electroencephalography (EEG) source localization in newborn infants, based on a realistic head model. To build a realistic head model we propose an interactive hybrid segmentation method for T1 magnetic resonance images (MRI), consisting of active contours, fuzzy c-means (FCM) clustering and mathematical morphology. Subsequently, we solve the localization problem using a spike train detection algorithm and an algorithm that deals with the forward and inverse problem. The performance of this fused method indicates that our realistic head model is suitable for the accurate localization of the EEG activity. We will present both initial qualitative and quantitative results.
Despotovic
Ivana
I
Faculty of Electrical Engineering, Ghent University, MEDISIP-IPI-IBBT,Ghent, Belgium. ivana.despotovic@telin.ugent.be
Deburchgraeve
Wouter
W
Hallez
Hans
H
Vansteenkiste
Ewout
E
Philips
Wilfried
W
D000465:Algorithms; D001921:Brain / Q000473:pathology; D001931:Brain Mapping / Q000379:methods; D016000:Cluster Analysis; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods*; D017143:Fuzzy Logic; D006257:Head / Q000473:pathology*; D006801:Humans; D007231:Infant, Newborn; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D008962:Models, Theoretical; D019047:Phantoms, Imaging