Results 1  10
of
23
Kotlov : private communication
, 1999
"... Predator and scavenger aggregation to discarded bycatch from dredge fisheries: importance of damage level ..."
Abstract

Cited by 90 (0 self)
 Add to MetaCart
Predator and scavenger aggregation to discarded bycatch from dredge fisheries: importance of damage level
Separable Nonlinear Least Squares: the Variable Projection Method and its Applications
 Institute of Physics, Inverse Problems
, 2002
"... this paper nonlinear data fitting problems which have as their underlying model a linear combination of nonlinear functions. More generally, one can also consider that there are two sets of unknown parameters, where one set is dependent on the other and can be explicitly eliminated. Models of this t ..."
Abstract

Cited by 52 (1 self)
 Add to MetaCart
this paper nonlinear data fitting problems which have as their underlying model a linear combination of nonlinear functions. More generally, one can also consider that there are two sets of unknown parameters, where one set is dependent on the other and can be explicitly eliminated. Models of this type are very common and we will show a variety of applications in different fields. Inasmuch as many inverse problems can be viewed as nonlinear data fitting problems, this material will be of interest to a wide crosssection of researchers and practitioners in parameter, material or system identification, signal analysis, the analysis of spectral data, medical and biological imaging, neural networks, robotics, telecommunications and model order reduction, to name a few
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 ..."
Abstract

Cited by 30 (11 self)
 Add to MetaCart
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.
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 ..."
Abstract

Cited by 19 (0 self)
 Add to MetaCart
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.
An MEG study of silent meaning
 Journal of Cognitive Neuroscience
, 2007
"... & Although research on the neural bases of language has made significant progress on how the brain accesses the meanings of words, our understanding of sentencelevel semantic composition remains limited. We studied the magnetoencephalography (MEG) responses elicited by expressions whose meaning ..."
Abstract

Cited by 15 (3 self)
 Add to MetaCart
& Although research on the neural bases of language has made significant progress on how the brain accesses the meanings of words, our understanding of sentencelevel semantic composition remains limited. We studied the magnetoencephalography (MEG) responses elicited by expressions whose meanings involved an element not expressed in the syntax, which enabled us to investigate the brain correlates of semantic composition without confounds from syntactic composition. Sentences such as the author began the book, which asserts that an activity was begun although no activity is mentioned in the syntax, were contrasted with control sentences such as the author wrote the book, which involved no implicit meaning. These conditions were further compared with a semantically anomalous condition (the author disgusted the book). MEG responses to the object noun showed that silent meaning and anomaly are associated with distinct effects, silent meaning, but not anomaly, eliciting increased amplitudes in the anterior midline field (AMF) at 350– 450 msec. The AMF was generated in ventromedial prefrontal areas, usually implicated for social cognition and theory of mind. Our results raise the possibility that silent meaning interpretation may share mechanisms with these neighboring domains of cognition. &
Model selection in electromagnetic source analysis with an application to VEF’s
 IEEE Transactions on Biomedical Engineering
, 2002
"... Abstract — In electromagnetic source analysis it is necessary to determine how many sources are required to describe the EEG or MEG adequately. Model selection procedures (MSP’s, or goodness of fit procedures) give an estimate of the required number of sources. Existing and new MSP’s are evaluated i ..."
Abstract

Cited by 7 (4 self)
 Add to MetaCart
Abstract — In electromagnetic source analysis it is necessary to determine how many sources are required to describe the EEG or MEG adequately. Model selection procedures (MSP’s, or goodness of fit procedures) give an estimate of the required number of sources. Existing and new MSP’s are evaluated in different source and noise settings: two sources which are close or distant, and noise which is uncorrelated or correlated. The commonly used MSP residual variance is seen to be ineffective, that is it often selects too many sources. Alternatives like the adjusted Hotelling’s test, Bayes information criterion, and the Wald test on source amplitudes are seen to be effective. The adjusted Hotelling’s test is recommended if a conservative approach is taken, and MSP’s such as Bayes information criterion or the Wald test on source amplitudes are recommended if a more liberal approach is desirable. The MSP’s are applied to empirical data (visual evoked fields). I.
A probabilistic algorithm integrating source localization and noise suppression for MEG and EEG data
 NeuroImage
, 2007
"... We have developed a novel probabilistic model that estimates neural source activity measured by MEG and EEG data while suppressing the effect of interference and noise sources. The model estimates contributions to sensor data from evoked sources, interference sources and sensor noise using Bayesian ..."
Abstract

Cited by 3 (2 self)
 Add to MetaCart
We have developed a novel probabilistic model that estimates neural source activity measured by MEG and EEG data while suppressing the effect of interference and noise sources. The model estimates contributions to sensor data from evoked sources, interference sources and sensor noise using Bayesian methods and by exploiting knowledge about their timing and spatial covariance properties. Full posterior distributions are computed rather than just the MAP estimates. In simulation, the algorithm can accurately localize and estimate the time courses of several simultaneously active dipoles, with rotating or fixed orientation, at noise levels typical for averaged MEG data. The algorithm even performs reasonably at noise levels typical of an average of just a few trials. The algorithm is superior to beamforming techniques, which we show to be an approximation to our graphical model, in estimation of temporally correlated sources. Success of this algorithm using MEG data for localizing bilateral auditory cortex, lowSNR somatosensory activations, and for localizing an epileptic spike source are also demonstrated.
The SCIRun Inverse EEG Pipeline  A Modeling and Simulation System for Cortical Mapping and Source Localization
, 1998
"... The goal of the inverse problem in electroencephalography is to determine electrical activity within the cranial volume, based on potential measurements taken from the scalp. In this paper, we discuss our implementation of a flexible, extensible system to solve the inverse EEG problem. Our implement ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
The goal of the inverse problem in electroencephalography is to determine electrical activity within the cranial volume, based on potential measurements taken from the scalp. In this paper, we discuss our implementation of a flexible, extensible system to solve the inverse EEG problem. Our implementation is constructed on top of the SCIRun problem solving environment, a flexible framework for implementing largescale scientific and engineering applications. We leverage the power of this underlying software architecture in the design and implementation of our inverse EEG pipeline. We apply our inverse EEG software system to a test subject's P300 auditory response. Keywords: inverse EEG problem, computational modeling, finite element method 1 Introduction The inverse EEG problem can be described as the mathematical mapping of EEG scalp recordings back onto the cortical surface or within the cortex to approximate fundamental current sources. This inverse problem lies at the foundation ...
Soft/hard focalization in the EEG inverse problem
 in IEEE Workshop on Statistical Signal Processing (SSP'05
, 2005
"... We present in this paper a novel statistical based focalized reconstruction method for the underdetermined EEG (electroencephalogram) inverse problem. The algorithm is based on the representation of nonGaussian distributions as an Infinite Mixture of Gaussians (IMG) and relies on an iterative proce ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
We present in this paper a novel statistical based focalized reconstruction method for the underdetermined EEG (electroencephalogram) inverse problem. The algorithm is based on the representation of nonGaussian distributions as an Infinite Mixture of Gaussians (IMG) and relies on an iterative procedure consisting out of alternated variance estimation / linear inversion operations. By taking into account noise statistics, it performs implicit spurious data rejection and produces robust focalized solutions allowing for straightforward discrimination of active/nonactive brain regions. We apply the proposed reconstruction procedure to average evoked potentials EEG data and compare the reconstruction results with the corresponding known physiological responses.
Semantics vs. world knowledge in prefrontal cortex
 Lang. Cognit. Process
, 2009
"... Humans have knowledge about the properties of their native language at various levels of representation; sound, structure, and meaning computation constitute the core components of any linguistic theory. Although the brain sciences have engaged with representational theories of sound and syntactic s ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
Humans have knowledge about the properties of their native language at various levels of representation; sound, structure, and meaning computation constitute the core components of any linguistic theory. Although the brain sciences have engaged with representational theories of sound and syntactic structure, the study of the neural bases of sentencelevel semantic computation has so far focused on manipulations that mainly vary knowledge about the world, and not necessarily linguistic knowledge about meaning, as defined by formal semantics. In this MEG study, we vary both semantic and world knowledge in the same experiment, and show that semantic violations, but not world knowledge violations, elicit an effect in the ventromedial prefrontal cortex (vmPFC), while both types of violations engage the left inferior prefrontal cortex. In our previous work, we have shown that the vmPFC is also sensitive to various types of ‘coercions’, i.e., operations that repair semantic typemismatch. Together, these results suggest that the vmPFC is involved in the composition of complex meaning, but not in the evaluation of whether an expression fits one’s knowledge of the world.