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

by: Srikantan S Nagarajan, Hagai T Attias, Hild, Kensuke Sekihara

NeuroImage, Vol. 30, No. 2. (1 April 2006), pp. 400-416.

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Abstract

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.

@article{citeulike:2110198, abstract = {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.}, author = {Nagarajan, Srikantan S. and Attias, Hagai T. and Hild and Sekihara, Kensuke }, citeulike-article-id = {2110198}, doi = {http://dx.doi.org/10.1016/j.neuroimage.2005.09.055}, journal = {NeuroImage}, keywords = {eeg, events, graphical-models}, month = {April}, number = {2}, pages = {400--416}, posted-at = {2007-12-14 02:44:26}, priority = {5}, title = {A graphical model for estimating stimulus-evoked brain responses from magnetoencephalography data with large background brain activity}, url = {http://dx.doi.org/10.1016/j.neuroimage.2005.09.055}, volume = {30}, year = {2006} }

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TY - JOUR ID - citeulike:2110198 L3 - citeulike-article-id:2110198 TI - A graphical model for estimating stimulus-evoked brain responses from magnetoencephalography data with large background brain activity JF - NeuroImage VL - 30 IS - 2 SP - 400 EP - 416 N2 - 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. KW - eeg KW - events KW - graphical-models AU - Nagarajan, Srikantan AU - Attias, Hagai AU - Hild AU - Sekihara, Kensuke PY - 2006/04/01/ UR - http://dx.doi.org/10.1016/j.neuroimage.2005.09.055 ER -

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