Title :
An evaluation of EEG scanner´s dependence on the imaging technique, forward model computation method, and array dimensionality
Author :
Stahlhut, C. ; Attias, H.T. ; Stopczynski, Arkadiusz ; Petersen, M.K. ; Larsen, Jakob Eg ; Hansen, Lars Kai
Author_Institution :
DTU Inf., Tech. Univ. of Denmark, Lyngby, Denmark
fDate :
Aug. 28 2012-Sept. 1 2012
Abstract :
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.
Keywords :
Bayes methods; array signal processing; electroencephalography; estimation theory; inverse problems; learning (artificial intelligence); medical signal processing; signal reconstruction; EEG scanner dependence evaluation; EEG source reconstruction; LORETA; array dimensionality; fixed forward propagation model; forward model computation method; high density EEG setup; imaging technique; inverse problem; inverse solvers minimum-norm; low density EEG setup; minimum variance adaptive beamformer; source estimates; sparse Bayesian learning; Biological system modeling; Brain modeling; Computational modeling; Electroencephalography; Image reconstruction; Imaging; Sensors; Bayes Theorem; Computer Simulation; Electroencephalography; Electromagnetic Fields; Head; Humans; Image Processing, Computer-Assisted; Models, Theoretical; Temporal Lobe;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4244-4119-8
Electronic_ISBN :
1557-170X
DOI :
10.1109/EMBC.2012.6346235
