Electromagnetic brain mapping

By Sylvain Baillet, John C. Mosher, Richard M. Leahy. IEEE Signal Processing Magazine, 2001

1,808 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e3083dcadcc1a9c4294d6f83b40d4fb27b8c4666

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Citations in the corpus

Identifying true brain interaction from EEG data using the imaginary part of coherency. (2004, methodology): “A plausible attempt to avoid artefacts from volume conduction is first to apply an inverse method (see Baillet et al. (2001) for an overview) to the data and then to calculate coherence or any other measure of interaction of the estimated source amplitudes (Gross et al., 2001).”
Graph analysis of functional brain networks: practical issues in translational neuroscience (2014, background): “In the latter cases, referring to “graphs”, which do not make explicit assumptions on the nature of the links but rather emphasize the aspect of mathematical modeling, seems to be more appropriate and can be used to improve the interdisciplinary exchange.”
Brain Computer Interfaces, a Review (2012, background): “Conservation of electric charges means that the primary currents are enclosed by extracellular current flows, which are known as secondary currents [10].”
Brain templates and atlases (2012, background): “It has been well-established that anatomical constraints on the expected locations and, optionally, orientations of neural current flows reconstructed fromMEG and EEG sensor traces are of considerable benefit to time-resolved source imaging (Baillet et al., 2001; Lin et al., 2006).”
On the interpretation of weight vectors of linear models in multivariate neuroimaging (2014, methodology): “…process of signal propagation can beaccuratelymodeled by the linear forward model where s v ( n ) is the electrical activity at N v ≫ K voxel in the brain and L is the lead ﬁ eld matrix modeling the propagation of electrical activity from brain voxels to EEGsensors (e.g., Baillet et al., 2001).”
Source connectivity analysis with MEG and EEG (2009, methodology): “First, an estimate of the activity of the neuronal sources is obtained by applying an inverse method (for a review, see [Baillet et al., 2001]).”
Timing of the brain events underlying access to consciousness during the attentional blink (2005, methodology): “Cortical current maps were computed from the EEG time series using a linear inverse estimator (weighted minimum–norm current estimate (WMNE); see ref”
Separable nonlinear least squares: the variable projection method and its applications (2003, background): “[70, 71, 2] have considered the problem of modeling the spatio-temporal neuromagnetic eld or magneto-encephalogram (MEG) produced by neural activity of the brain.”
Towards the utilization of EEG as a brain imaging tool (2012, background): “Several comprehensive reviews on this topic exist (Baillet et al., 2001; He and Lian, 2005; Michel and He, 2011; Michel et al., 2004b; Pascual-Marqui et al., 2009; Sekihara and Nagarajan, 2004).”
Mining event-related brain dynamics. (2004, background): “This result might surprise researchers accustomed to thinking that EEG sources can only be separated by directly solving the biophysical inverse problem, which is heavily underdetermined even for moderate numbers of sources [ 24 ].”
Handbook of Blind Source Separation: Independent Component Analysis and Applications (2010, neutral): “1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.2 Sparse signal representations . . . . . . . . . . . . . . . . . . . . 10 1.2.1 Basic principles of sparsity . . . . . . . . . . . . . . . .”
Real-time fMRI and its application to neurofeedback (2012, background): “Even with modern multi-channel EEG systems the localization of the electric source is an intrinsically ill-posed problem (Baillet et al., 2001).”
The brain–computer interface cycle (2009, methodology): “Alternative spatial filtering approaches are channel re-referencing such as the common average reference or the Laplace filter [58], source imaging methods that make explicit use of a forward model (see [59]) or spatial filters that make use of class information, such as the common spatial patterns ”
A unified Bayesian framework for MEG/EEG source imaging (2009, positive): “In the simplest case, the single-component assumption Σs=γ1C1=γ1I, wchere I is an identity matrix, leads to a weighted minimum-l2-norm solution (Baillet et al., 2001).”
EEG source imaging. (2004, methodology): “…reviews on EEG/MEG source imaging exist, that explain in detail the formal implementation of the a priori constraints in the different algorithms (Baillet et al., 2001; Fuchs et al., 1999; George et al., 1995; Gonzalez Andino et al., 1999; Grave de Peralta Menendez and Gonzalez Andino, 1998;…”