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Spatiotemporal EEG/MEG source analysis based on a parametric noise covariance model
 IEEE Transactions on Biomedical Engineering
, 2002
"... c○2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other w ..."
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Cited by 22 (4 self)
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c○2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Estimating Evoked Dipole Responses in Unknown Spatially Correlated Noise with EEG/MEG Arrays
, 2000
"... We present maximum likelihood (ML) methods for estimating evoked dipole responses using electroencephalography (EEG) and magnetoencephalography (MEG) arrays, which allow for spatially correlated noise between sensors with unknown covariance. The electric source is modeled as a collection of current ..."
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Cited by 17 (1 self)
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We present maximum likelihood (ML) methods for estimating evoked dipole responses using electroencephalography (EEG) and magnetoencephalography (MEG) arrays, which allow for spatially correlated noise between sensors with unknown covariance. The electric source is modeled as a collection of current dipoles at fixed locations and the head as a spherical conductor. We permit the dipoles' moments to vary with time by modeling them as linear combinations of parametric or nonparametric basis functions. We estimate the dipoles' locations and moments and derive the CramrRao bound for the unknown parameters. We also propose an MLbased method for scanning the brain response data, which can be used to initialize the multidimensional search required to obtain the true dipole location estimates. Numerical simulations demonstrate the performance of the proposed methods. Index TermsCramrRao bound, dipole source, EEG, evoked responses, maximum likelihood parameter estimation, MEG, sensor arr...
A mathematical approach to the temporal stationarity of background noise in MEG/EEG measurements
 NeuroImage
, 2003
"... The general spatiotemporal covariance matrix of the background noise in MEG/EEG signals is huge. To reduce the dimensionality of this matrix it is modeled as a Kronecker product of a spatial and a temporal covariance matrix. When the number of time samples is larger than, say, J 500, the iterative ..."
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Cited by 11 (3 self)
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The general spatiotemporal covariance matrix of the background noise in MEG/EEG signals is huge. To reduce the dimensionality of this matrix it is modeled as a Kronecker product of a spatial and a temporal covariance matrix. When the number of time samples is larger than, say, J 500, the iterative Maximum Likelihood estimation of these two matrices is still too timeconsuming to be useful on a routine basis. In this study we looked for methods to circumvent this computationally expensive procedure by using a parametric model with subjectdependent parameters. Such a model would additionally help with interpreting MEG/EEG signals. For the spatial covariance, models have been derived already and it has been shown that measured MEG/EEG signals can be understood spatially as random processes, generated by random dipoles. The temporal covariance, however, has not been modeled yet, therefore we studied the temporal covariance matrix in several subjects. For all subjects the temporal covariance shows an alpha oscillation and vanishes for large time lag. This gives rise to a temporal noise model consisting of two components: alpha activity and additional random noise. The alpha activity is modeled as randomly occurring waves with random phase and the covariance of the additional noise decreases exponentially with lag. This model requires only six parameters instead of 12J(J 1). Theoretically, this model is stationary but in practice the stationarity of the matrix is highly influenced by the baseline correction. It appears that very good agreement between the data and the parametric model can be obtained when the baseline correction window is taken into account properly. This finding implies that the background noise is in principle a stationary process and that nonstationarities are mainly caused by the nature of the preprocessing method. When analyzing events at a fixed sample
K and SeitherPreisler A. Studies of tonotopy based on wave N100 of the auditory evoked field are problematic. NeuroImage 2003b
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Efficient dipole parameter estimation in EEG systems with nearML performance
 IEEE Trans. Biomed. Eng
"... Abstract—Source signals that have strong temporal correlation can pose a challenge for highresolution EEG source localization algorithms. In this paper, we present two methods that are able to accurately locate highly correlated sources in situations where other highresolution methods such as mult ..."
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Abstract—Source signals that have strong temporal correlation can pose a challenge for highresolution EEG source localization algorithms. In this paper, we present two methods that are able to accurately locate highly correlated sources in situations where other highresolution methods such as multiple signal classification and linearly constrained minimum variance beamforming fail. These methods are based on approximations to the optimal maximum likelihood (ML) approach, but offer significant computational advantages over ML when estimates of the equivalent EEG dipole orientation and moment are required in addition to the source location. The first method uses a twostage approach in which localization is performed assuming an unstructured dipole moment model, and then the dipole orientation is obtained by using these estimates in a second step. The second method is based on the use of the noise subspace fitting concept, and has been shown to provide performance that is asymptotically equivalent to the direct ML method. Both techniques lead to a considerably simpler optimization than ML since the estimation of the source locations and dipole moments is decoupled. Examples using data from simulations and auditory experiments are presented to illustrate the performance of the algorithms. Index Terms—Electroencephalography (EEG), magnetoencephalography (MEG), sensor array processing, source localization.
The neurochemical basis of human cortical auditory processing: combining proton magnetic resonance spectroscopy and magnetoencephalography
 BMC Biology
, 2006
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BioMed Central
, 2007
"... Research article Pure phaselocking of beta/gamma oscillation contributes to the N30 frontal component of somatosensory evoked potentials ..."
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Research article Pure phaselocking of beta/gamma oscillation contributes to the N30 frontal component of somatosensory evoked potentials
Auditory temporal processing in healthy aging: a magnetoencephalographic study
"... Impaired speech perception is one of the major sequelae of aging. In addition to peripheral hearing loss, central deficits of auditory processing are supposed to contribute to the deterioration of speech perception in older individuals. To test the hypothesis that auditory temporal processing is com ..."
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Impaired speech perception is one of the major sequelae of aging. In addition to peripheral hearing loss, central deficits of auditory processing are supposed to contribute to the deterioration of speech perception in older individuals. To test the hypothesis that auditory temporal processing is compromised in aging, auditory evoked magnetic fields were recorded during stimulation with sequences of 4 rapidly recurring speech sounds in 28 healthy individuals aged 20 78 years. Results The decrement of the N1m amplitude during rapid auditory stimulation was not significantly different between older and younger adults. The amplitudes of the middlelatency P1m wave and of the longlatency N1m, however, were significantly larger in older than in younger participants. Conclusions The results of the present study do not provide evidence for the hypothesis that auditory temporal processing, as measured by the decrement (shortterm habituation) of the major auditory evoked component, the N1m wave, is impaired in aging. The differences between these
Maximum likelihood spatiotemporal EEG/MEG source analysis
"... Introduction EEG/MEG noise has an unequal variance and is correlated, both in space and in time. Noise variance may differ greatly between samples or sensors, and correlations between samples or sensors can be very high [14]. If these noise characteristics are neglected, then an EEG/MEG source an ..."
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Introduction EEG/MEG noise has an unequal variance and is correlated, both in space and in time. Noise variance may differ greatly between samples or sensors, and correlations between samples or sensors can be very high [14]. If these noise characteristics are neglected, then an EEG/MEG source analysis will yield unreliable results [e.g. 5, 6]. First, source parameter estimates will be inefficient. That is, their standard errors will be too high. Second, the estimated covariance matrix of the parameter estimates will be inaccurate. In general it will give a too optimistic impression of precision. Third, goodness of fit measures will be unreliable, which may result in over or undermodeling of the data. For these reasons, it is very beneficial to incorporate the spatiotemporal noise covariance in the analysis. Although the spatial covariance is incorporated quite often [611], the temporal covariance is disregarded up to now. Therefore, we are developing a method to incorporate the
evoked by lateralized clicks
"... Dipole source analysis of auditory brain stem responses ..."
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