A probabilistic algorithm integrating source localization and noise suppression for MEG and EEG data.

JM Zumer, HT Attias, K Sekihara, SS Nagarajan

Research output: Contribution to journalArticle

Abstract

We have developed a novel probabilistic model that estimates neural source activity measured by MEG and EEG data while suppressing the effect of interference and noise sources. The model estimates contributions to sensor data from evoked sources, interference sources and sensor noise using Bayesian methods and by exploiting knowledge about their timing and spatial covariance properties. Full posterior distributions are computed rather than just the MAP estimates. In simulation, the algorithm can accurately localize and estimate the time courses of several simultaneously active dipoles, with rotating or fixed orientation, at noise levels typical for averaged MEG data. The algorithm even performs reasonably at noise levels typical of an average of just a few trials. The algorithm is superior to beamforming techniques, which we show to be an approximation to our graphical model, in estimation of temporally correlated sources. Success of this algorithm using MEG data for localizing bilateral auditory cortex, low-SNR somatosensory activations, and for localizing an epileptic spike source are also demonstrated.
Original languageEnglish
Pages (from-to)102-115
JournalNeuroImage
Volume37
Issue number1
DOIs
Publication statusPublished - 1 Aug 2007

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