A graphical model for estimating stimulus-evoked brain responses from magnetoencephalography data with large background brain activity

This paper formulates a novel probabilistic graphical model for noisy stimulus-evoked MEG and EEG sensor data obtained in the presence of large background brain activity. The model describes the observed data in terms of unobserved evoked and background factors with additive sensor noise. We present...

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Bibliographic Details
Published in:NeuroImage (Orlando, Fla.) Fla.), 2006-04, Vol.30 (2), p.400-416
Main Authors: Nagarajan, Srikantan S., Attias, Hagai T., Hild, Kenneth E., Sekihara, Kensuke
Format: Article
Language:English
Subjects:
Algorithms
Bayes Theorem
Brain - physiology
Computer Simulation
Data Interpretation, Statistical
Economic models
Electroencephalography
Electroencephalography - statistics & numerical data
EM algorithm
Evoked Potentials - physiology
Factor Analysis, Statistical
Graphical models
Humans
Magnetoencephalography
Magnetoencephalography - statistics & numerical data
Models, Statistical
Noise
Principal components analysis
Probability distribution
Variables
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Summary:This paper formulates a novel probabilistic graphical model for noisy stimulus-evoked MEG and EEG sensor data obtained in the presence of large background brain activity. The model describes the observed data in terms of unobserved evoked and background factors with additive sensor noise. We present an expectation maximization (EM) algorithm that estimates the model parameters from data. Using the model, the algorithm cleans the stimulus-evoked data by removing interference from background factors and noise artifacts and separates those data into contributions from independent factors. We demonstrate on real and simulated data that the algorithm outperforms benchmark methods for denoising and separation. We also show that the algorithm improves the performance of localization with beamforming algorithms.
ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2005.09.055