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61
Independent component approach to the analysis of EEG and MEG recordings
 IEEE Transactions on Biomedical Engineering
, 2000
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Abstract

Cited by 57 (8 self)
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This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Helsinki University of Technology's products or services. Internal or 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 must be obtained from the IEEE by writing to
Independent Components of Magnetoencephalography: Localization
, 2002
"... We applied secondorder blind identification (SOBI), an independent component analysis (ICA) method, to MEG data collected during cognitive tasks. We explored SOBI's ability to help isolate underlying neuronal sources with relatively poor signaltonoise ratios, allowing their identification and ..."
Abstract

Cited by 27 (10 self)
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We applied secondorder blind identification (SOBI), an independent component analysis (ICA) method, to MEG data collected during cognitive tasks. We explored SOBI's ability to help isolate underlying neuronal sources with relatively poor signaltonoise ratios, allowing their identification and localization. We compare localization of the SOBIseparated components to localization from unprocessed sensor signals, using an equivalent current dipole (ECD) modeling method. For visual and somatosensory modalities, SOBI preprocessing resulted in components that can be localized to physiologically and anatomically meaningful locations.
Hierarchical Bayesian estimation for MEG inverse problem
 NeuroImage
"... Source current estimation from MEG measurement is an illposed problem that requires prior assumptions about brain activity and an efficient estimation algorithm. In this article, we propose a new hierarchical Bayesian method introducing a hierarchical prior that can effectively incorporate both str ..."
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Cited by 27 (0 self)
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Source current estimation from MEG measurement is an illposed problem that requires prior assumptions about brain activity and an efficient estimation algorithm. In this article, we propose a new hierarchical Bayesian method introducing a hierarchical prior that can effectively incorporate both structural and functional MRI data. In our method, the variance of the source current at each source location is considered an unknown parameter and estimated from the observed MEG data and prior information by using the Variational Bayesian method. The fMRI information can be imposed as prior information on the variance distribution rather than the variance itself so that it gives a soft constraint on the variance. A spatial smoothness constraint, that the neural activity within a few millimeter radius tends to be similar due to the neural connections, can also be implemented as a hierarchical prior. The proposed method provides a unified theory to deal with the following three situations: (1) MEG with no other data,
Anatomically informed basis functions for EEG source localization: Combining functional and anatomical constraints
 NeuroImage
, 2002
"... Distributed linear solutions have frequently been used to solve the source localization problem in EEG. Here we introduce an approach based on the weighted minimum norm (WMN) method that imposes constraints using anatomical and physiological information derived from other imaging modalities. The ana ..."
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Cited by 20 (3 self)
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Distributed linear solutions have frequently been used to solve the source localization problem in EEG. Here we introduce an approach based on the weighted minimum norm (WMN) method that imposes constraints using anatomical and physiological information derived from other imaging modalities. The anatomical constraints are used to reduce the solution space a priori by modeling the spatial source distribution with a set of basis functions. These spatial basis functions are chosen in a principled way using information theory. The reduced problem is then solved with a classical WMN method. Further (functional) constraints can be introduced in the weighting of the solution using fMRI brain responses to augment spatial priors. We used simulated data to explore the behavior of the approach over a range of the model’s hyperparameters. To assess the construct validity of our method we compared it with two established approaches to the source localization problem, a simple weighted minimum norm and a maximum smoothness (Loretalike) solution. This involved simulations, using single and multiple sources that were analyzed under different levels of confidence in the priors. © 2002 Elsevier Science (USA) Key Words: EEG; source localization; distributed linear solution; informed basis functions; anatomical constraints; functional constraints.
Electrical neuroimaging based on biophysical constraints
 NeuroImage
, 2004
"... This paper proposes and implements biophysical constraints to select a unique solution to the bioelectromagnetic inverse problem. It first shows that the brain’s electric fields and potentials are predominantly due to ohmic currents. This serves to reformulate the inverse problem in terms of a restr ..."
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Cited by 20 (2 self)
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This paper proposes and implements biophysical constraints to select a unique solution to the bioelectromagnetic inverse problem. It first shows that the brain’s electric fields and potentials are predominantly due to ohmic currents. This serves to reformulate the inverse problem in terms of a restricted source model permitting noninvasive estimations of Local Field Potentials (LFPs) in depth from scalprecorded data. Uniqueness in the solution is achieved by a physically derived regularization strategy that imposes a spatial structure on the solution based upon the physical laws that describe electromagnetic fields in biological media. The regularization strategy and the source model emulate the properties of brain activity’s actual generators. This added information is independent of both the recorded data and head model and suffices for obtaining a unique solution compatible with and aimed at analyzing experimental data. The inverse solution’s features are evaluated with eventrelated potentials (ERPs) from a healthy
Mapping human brain function with MEG and EEG: methods and validation
 NeuroImage
, 2004
"... We survey the field of magnetoencephalography (MEG) and electroencephalography (EEG) source estimation. These modalities offer the potential for functional brain mapping with temporal resolution in the millisecond range. However, the limited number of spatial measurements and the illposedness of th ..."
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Cited by 18 (0 self)
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We survey the field of magnetoencephalography (MEG) and electroencephalography (EEG) source estimation. These modalities offer the potential for functional brain mapping with temporal resolution in the millisecond range. However, the limited number of spatial measurements and the illposedness of the inverse problem present significant limits to our ability to produce accurate spatial maps from these data without imposing major restrictions on the form of the inverse solution. Here we describe approaches to solving the forward problem of computing the mapping from putative inverse solutions into the data space. We then describe the inverse problem in terms of low dimensional solutions, based on the equivalent current dipole (ECD), and high dimensional solutions, in which images of neural activation are constrained to the cerebral cortex. We also address the issue of objective assessment of the relative performance of inverse procedures by the freeresponse receiver operating characteristic (FROC) curve. We conclude with a discussion of methods for assessing statistical significance of experimental results through use of the bootstrap for determining confidence regions in dipolefitting methods, and random field (RF) and permutation methods for detecting significant activation in cortically constrained imaging studies.
On the relationship of synaptic activity to macroscopic measurements: does coregistration of EEG with fMRI make sense?
 BRAIN TOPOGR
, 2000
"... A twoscale theoretical description outlines relationships between brain current sources and the resulting extracranial electric field, recorded as EEG. Finding unknown sources of EEG, the socalled "inverse problem", is discussed in general terms, with emphasis on the fundamental nonuniqueness of ..."
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Cited by 15 (0 self)
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A twoscale theoretical description outlines relationships between brain current sources and the resulting extracranial electric field, recorded as EEG. Finding unknown sources of EEG, the socalled "inverse problem", is discussed in general terms, with emphasis on the fundamental nonuniqueness of inverse solutions. Hemodynamic signatures, measured with fMRI, are expressed as voxel integrals to facilitate comparisons with EEG. Two generally distinct cell groups (1 and 2), generating EEG and fMRI signals respectively, are embedded within the much broader class of synaptic action fields. Cell groups 1 and 2 may or may not overlap in specific experiments. Implications of this incomplete overlap for coregistration studies are considered. Each experimental measure of brain function is generally sensitive to a different kind of source activity and to different spatial and temporal scales. Failure to appreciate such distinctions can exacerbate conflicting views of brain function that emphasize either global integration or functional localization.
Blind Source Separation Techniques for Decomposing Event Related Brain Signals
, 2002
"... Recently blind source separation (BSS) methods have been highly successfully applied to biomedical data. This paper reviews the concept of BSS and demonstrates its usefulness in the context of eventrelated MEG measurements. In a rst experiment we apply BSS to artifact identi cation of raw MEG data ..."
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Cited by 13 (4 self)
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Recently blind source separation (BSS) methods have been highly successfully applied to biomedical data. This paper reviews the concept of BSS and demonstrates its usefulness in the context of eventrelated MEG measurements. In a rst experiment we apply BSS to artifact identi cation of raw MEG data and discuss how the quality of the resulting independent component projections can be evaluated. The second part of our study considers averaged data of event related magnetic elds. Here it is particularly important to monitor and thus avoid possible over tting due to limited sample size. A stability assessment of the BSS decomposition allows to solve this task and an additional grouping of the BSS components reveals interesting structure, that could ultimately be used for gaining a better physiological modeling of the data.
Dynamical Factor Analysis Of Rhythmic Magnetoencephalographic Activity
 in Proc. Int. Conf. on Independent Component Analysis and Signal Separation (ICA2001
, 2001
"... Dynamical factor analysis (DFA) is a generative dynamical algorithm, with linear mapping from factors to the observations and nonlinear mapping of the factor dynamics. The latter is modeled by a multilayer perceptron. Ensemble learning is used to estimate the DFA model in an unsupervised manner. The ..."
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Cited by 12 (6 self)
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Dynamical factor analysis (DFA) is a generative dynamical algorithm, with linear mapping from factors to the observations and nonlinear mapping of the factor dynamics. The latter is modeled by a multilayer perceptron. Ensemble learning is used to estimate the DFA model in an unsupervised manner. The performance of the DFA have been tested in a set of artificially generated noisy modulated sinusoids. Furthermore, we have applied it to magnetoencephalographic data containing bursts of oscillatory brain activity. This paper shows that DFA can correctly estimate the underlying factors in both data sets.
Systematic regularization of linear inverse solutions of the EEG source localization problem
 NeuroImage
"... Distributed linear solutions of the EEG source localization problem are used routinely. Here we describe an approach based on the weighted minimum norm method that imposes constraints using anatomical and physiological information derived from other imaging modalities to regularize the solution. In ..."
Abstract

Cited by 12 (2 self)
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Distributed linear solutions of the EEG source localization problem are used routinely. Here we describe an approach based on the weighted minimum norm method that imposes constraints using anatomical and physiological information derived from other imaging modalities to regularize the solution. In this approach the hyperparameters controlling the degree of regularization are estimated using restricted maximum likelihood (ReML). EEG data are always contaminated by noise, e.g., exogenous noise and background brain activity. The conditional expectation of the source distribution, given the data, is attained by carefully balancing the minimization of the residuals induced by noise and the improbability of the estimates as determined by their priors.