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11
The spatiotemporal MEG covariance matrix modeled as a sum of Kronecker products
 NeuroImage
, 2005
"... The single Kronecker product (KP) model for the spatiotemporal covariance of MEG residuals is extended to a sum of Kronecker products. This sum of KP is estimated such that it approximates the spatiotemporal sample covariance best in matrix norm. Contrary to the single KP, this extension allows for ..."
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The single Kronecker product (KP) model for the spatiotemporal covariance of MEG residuals is extended to a sum of Kronecker products. This sum of KP is estimated such that it approximates the spatiotemporal sample covariance best in matrix norm. Contrary to the single KP, this extension allows for describing multiple, independent phenomena in the ongoing background activity. Whereas the single KP model can be interpreted by assuming that background activity is generated by randomly distributed dipoles with certain spatial and temporal characteristics, the sum model can be physiologically interpreted by assuming a composite of such processes. Taking enough terms into account, the spatiotemporal sample covariance matrix can be described exactly by this extended model. In the estimation of the sum of KP model, it appears that the sum of the first 2 KP describes between 67 % and 93%. Moreover, these first two terms describe two physiological processes in the background activity: focal, frequencyspecific alpha activity, and more widespread nonfrequencyspecific activity. Furthermore, temporal nonstationarities due to trialtotrial variations are not clearly visible in the first two terms, and, hence, play only a minor role in the sample covariance matrix in terms of matrix power. Considering the dipole localization, the single KP model appears to describe around 80 % of the noise and seems therefore adequate. The emphasis of further improvement of localization accuracy should be on improving the source model rather than the covariance model.
A Graphical Model for Estimating StimulusEvoked Brain Responses from Magnetoencephalography Data with Large Background Brain Activity Abstract
, 2005
"... This paper formulates a novel probabilistic graphical model for noisy stimulusevoked 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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This paper formulates a novel probabilistic graphical model for noisy stimulusevoked 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 ExpectationMaximization (EM) algorithm that estimates the model parameters from data. Using the model, the algorithm cleans the stimulusevoked 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.
A maximumlikelihood estimator for trialtotrial variations in noisy MEG/EEG data sets
 IEEE Trans Biomed Eng
"... Abstract—The standard procedure to determine the brain response from a multitrial evoked magnetoencephalography (MEG) or electroencephalography (EEG) data set is to average the individual trials of these data, time locked to the stimulus onset. When the brain responses vary from trialtotrial thi ..."
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Abstract—The standard procedure to determine the brain response from a multitrial evoked magnetoencephalography (MEG) or electroencephalography (EEG) data set is to average the individual trials of these data, time locked to the stimulus onset. When the brain responses vary from trialtotrial this approach is false. In this paper, a maximumlikelihood estimator is derived for the case that the recorded data contain amplitude variations. The estimator accounts for spatially and temporally correlated background noise that is superimposed on the brain response. The model is applied to a series of 17 MEG data sets of normal subjects, obtained during median nerve stimulation. It appears that the amplitude of late component (30–120 ms) shows a systematic negative trend indicating a weakening response during stimulation time. For the early components (20–35 ms) no such a systematic effect was found. The model is furthermore applied on a MEG data set consisting of epileptic spikes of constant spatial distribution but varying polarity. For these data, the advantage of applying the model is that positive and negative spikes can be processed with a single model, thereby reducing the number of degrees of freedom and increasing the signaltonoise ratio. Index Terms—Covariance, habituation, maximumlikelihood, MEG noise.
A statistical model and simulator for cardiovascular pressure signals
 Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine 222
, 2008
"... signals ..."
Stochastic maximum likelihood mean and crossspectrum structure estimation: analytic and neuromagnetic Monte Carlo results
, 2004
"... In [1] we proposed to analyze crossspectrum matrices obtained from electro or magnetoencephalographic (EEG/MEG) signals, to obtain estimates of the EEG/MEG sources and their coherence. In this paper we extend this method in two ways. First, by modelling such interactions as linear filters, and se ..."
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Cited by 1 (1 self)
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In [1] we proposed to analyze crossspectrum matrices obtained from electro or magnetoencephalographic (EEG/MEG) signals, to obtain estimates of the EEG/MEG sources and their coherence. In this paper we extend this method in two ways. First, by modelling such interactions as linear filters, and second, by taking the mean of the signals across different trials into account. To obtain estimates we propose a stochastic maximum likelihood (SML) method, and obtain the concentrated likelihood that includes the trial means.
unknown title
, 2005
"... A graphical model for estimating stimulusevoked brain responses from magnetoencephalography data with large background brain activity ..."
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A graphical model for estimating stimulusevoked brain responses from magnetoencephalography data with large background brain activity
A three domain covariance framework for EEG/MEG data
"... In this paper we introduce a covariance framework for the analysis of EEG and MEG data that takes into account observed temporal stationarity on small time scales and trialtotrial variations. We formulate a model for the covariance matrix, which is a Kronecker product of three components that cor ..."
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In this paper we introduce a covariance framework for the analysis of EEG and MEG data that takes into account observed temporal stationarity on small time scales and trialtotrial variations. We formulate a model for the covariance matrix, which is a Kronecker product of three components that correspond to space, time and epochs/trials, and consider maximum likelihood estimation of the unknown parameter values. An iterative algorithm that finds approximations of the maximum likelihood estimates is proposed. We perform a simulation study to assess the performance of the estimator and investigate the influence of different assumptions about the covariance factors on the estimated covariance matrix and on its components. Apart from that, we illustrate our method on real EEG and MEG data sets. The proposed covariance model is applicable in a variety of cases where spontaneous EEG or MEG acts as source of noise and realistic noise covariance estimates are needed for accurate dipole localization, such as in evoked activity studies, or where the properties of spontaneous EEG or MEG are themselves the topic of interest, such as in combined EEG/fMRI experiments in which the correlation between EEG and fMRI signals is investigated.