A probabilistic algorithm for robust interference suppression in bioelectromagnetic sensor data.
Magnetoencephalography (MEG) and electroencephalography (EEG) sensor measurements are often contaminated by several interferences such as background activity from outside the regions of interest, by biological and non-biological artifacts, and by sensor noise. Here, we introduce a probabilistic graphical model and inference algorithm based on variational-Bayes expectation-maximization for estimation of activity of interest through interference suppression. The algorithm exploits the fact that electromagnetic recording data can often be partitioned into baseline periods, when only interferences are present, and active time periods, when activity of interest is present in addition to interferences. This algorithm is found to be robust and efficient and significantly superior to many other existing approaches on real and simulated data.
Nagarajan
Srikantan S
SS
Department of Radiology, University of California at San Francisco, 513 Parnassus Avenue 5362, San Francisco, CA 94122, USA. srikantan.nagarajan@radiology.ucsf.edu
Attias
Hagai T
HT
Hild
Kenneth E
KE
Sekihara
Kensuke
K
D000465:Algorithms; D001499:Bayes Theorem; D001931:Brain Mapping; D004569:Electroencephalography; D005071:Evoked Potentials / Q000502:physiology; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D015225:Magnetoencephalography; D014481:United States