Stimulus Evoked Independent Factor Analysis of MEG Data with Large Background Activity

Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)

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Kenneth Hild, Kensuke Sekihara, Hagai Attias, Srikantan Nagarajan


This paper presents a novel technique for analyzing electromagnetic imaging data obtained using the stimulus evoked experimental paradigm. The technique is based on a probabilistic graphical model, which describes the data in terms of underlying evoked and interference sources, and explicitly models the stimulus evoked paradigm. A variational Bayesian EM algorithm infers the model from data, suppresses interference sources, and reconstructs the activity of separated individual brain sources. The new algorithm outperforms existing techniques on two real datasets, as well as on simulated data.