Probabilistic algorithms for MEG/EEG source reconstruction using temporal basis functions learned from data.
We present two related probabilistic methods for neural source reconstruction from MEG/EEG data that reduce effects of interference, noise, and correlated sources. Both methods localize source activity using a linear mixture of temporal basis functions (TBFs) learned from the data. In contrast to existing methods that use predetermined TBFs, we compute TBFs from data using a graphical factor analysis based model [Nagarajan, S.S., Attias, H.T., Hild, K.E., Sekihara, K., 2007a. A probabilistic algorithm for robust interference suppression in bioelectromagnetic sensor data. Stat Med 26, 3886-3910], which separates evoked or event-related source activity from ongoing spontaneous background brain activity. Both algorithms compute an optimal weighting of these TBFs at each voxel to provide a spatiotemporal map of activity across the brain and a source image map from the likelihood of a dipole source at each voxel. We explicitly model, with two different robust parameterizations, the contribution from signals outside a voxel of interest. The two models differ in a trade-off of computational speed versus accuracy of learning the unknown interference contributions. Performance in simulations and real data, both with large noise and interference and/or correlated sources, demonstrates significant improvement over existing source localization methods.
Zumer
Johanna M
JM
Biomagnetic Imaging Lab, Department of Radiology, University of California, San Francisco, San Francisco, CA 94143-0628, USA.
Attias
Hagai T
HT
Sekihara
Kensuke
K
Nagarajan
Srikantan S
SS
D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000502:physiology; D004569:Electroencephalography; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015225:Magnetoencephalography; D008962:Models, Theoretical