A physiologically motivated sparse, compact, and smooth (SCS) approach to EEG source localization.
Here, we introduce a novel approach to the EEG inverse problem based on the assumption that principal cortical sources of multi-channel EEG recordings may be assumed to be spatially sparse, compact, and smooth (SCS). To enforce these characteristics of solutions to the EEG inverse problem, we propose a correlation-variance model which factors a cortical source space covariance matrix into the multiplication of a pre-given correlation coefficient matrix and the square root of the diagonal variance matrix learned from the data under a Bayesian learning framework. We tested the SCS method using simulated EEG data with various SNR and applied it to a real ECOG data set. We compare the results of SCS to those of an established SBL algorithm.
Swartz Center of Computational Neuroscience, Univ of California San Diego, CA, USA.
Cheng
C
Cao
Zeynep
Z
Akalin Acar
Kenneth
K
Kreutz-Delgado
Scott
S
Makeig
D001499:Bayes Theorem; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008962:Models, Theoretical; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio