An evaluation of EEG scanner's dependence on the imaging technique, forward model computation method, and array dimensionality.
EEG source reconstruction involves solving an inverse problem that is highly ill-posed and dependent on a generally fixed forward propagation model. In this contribution we compare a low and high density EEG setup's dependence on correct forward modeling. Specifically, we examine how different forward models affect the source estimates obtained using four inverse solvers Minimum-Norm, LORETA, Minimum-Variance Adaptive Beamformer, and Sparse Bayesian Learning.
DTU Informatics, Technical University of Denmark, DK-2800 Kgs. Lyngby. C. Stahlhut. cs@imm.dtu.dk
Carsten
C
Stahlhut
Hagai Thomas
HT
Attias
Arkadiusz
A
Stopczynski
Michael Kai
MK
Petersen
Jakob Eg
JE
Larsen
Lars Kai
LK
Hansen
D001499:Bayes Theorem; D003198:Computer Simulation; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods*; D004574:Electromagnetic Fields; D006257:Head / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D013702:Temporal Lobe / Q000502:physiology