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A probabilistic algorithm for robust interference suppression in bioelectromagnetic sensor data
Author(s) -
Nagarajan Srikantan S.,
Attias Hagai T.,
Hild Kenneth E.,
Sekihara Kensuke
Publication year - 2007
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2941
Subject(s) - computer science , magnetoencephalography , probabilistic logic , algorithm , expectation–maximization algorithm , noise (video) , interference (communication) , bayes' theorem , maximization , inference , electroencephalography , pattern recognition (psychology) , robustness (evolution) , bayesian probability , artificial intelligence , mathematics , statistics , mathematical optimization , maximum likelihood , psychology , computer network , channel (broadcasting) , psychiatry , image (mathematics) , biochemistry , chemistry , gene
Abstract 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 baselineperiods, 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. Copyright © 2007 John Wiley & Sons, Ltd.