Vector-based spatial–temporal minimum L1-norm solution for MEG

@article{Huang2006VectorbasedSM,
  title={Vector-based spatial–temporal minimum L1-norm solution for MEG},
  author={Mingxiong Huang and Anders M. Dale and Tao Song and Eric Halgren and Deborah L. Harrington and Igor Podgorny and Jos{\'e} M. Ca{\~n}ive and Stephen Lewis and Roland R. Lee},
  journal={NeuroImage},
  year={2006},
  volume={31},
  pages={1025-1037}
}

Figures from this paper

Spatio-temporal analysis in functional brain imaging

  • W. Ou
  • Computer Science
  • 2010
The fMRI-informed regional EEG/MEG source estimator (FIRE) is developed, based on a generative model that encourages similar spatial patterns but allows for differences in time courses across imaging modalities.

Combining sparsity and rotational invariance in EEG/MEG source reconstruction

Large-scale EEG/MEG source localization with spatial flexibility

A Distributed Spatio-temporal EEG/MEG Inverse Solver

A novel l1l2-norm inverse solver for estimating the sources of EEG/MEG signals that achieves fewer false positives and a better representation of the source locations than the conventional l2 minimum-norm estimates is proposed.

Space-Time Sparse Reconstruction for Magneto-/Electroencephalography

The new method is shown to be efiective on several MEG simulations of neurological activity as well as data from a self-paced flnger tapping experiment, and a novel Expectation-Maximization algorithm which maximizes the likelihood function is presented.

Bayesian spatio-temporal decomposition for electromagnetic imaging of extended sources based on Destrieux atlas

A new fully data-driven source imaging method, Source Imaging based on Spatio-Temporal Decompositions (SI-STD), which is built upon a Bayesian framework, showing the superior performance of SI-STD in reconstructing extended sources compared with the ¿2-norm constrained methods.
...

References

SHOWING 1-10 OF 98 REFERENCES

Reconstructing spatio-temporal activities of neural sources using an MEG vector beamformer technique

The authors have developed a method suitable for reconstructing spatio-temporal activities of neural sources by using magnetoencephalogram (MEG) data that extends the adaptive beamformer technique to incorporate the vector beamformer formulation in which a set of three weight vectors are used to detect the source activity in three orthogonal directions.

Statistical flattening of MEG beamformer images

A modification to the minimum‐variance beamformer, in which beamformer weights and SPMs of source‐power change are computed in distinct steps, allows the calculation of image smoothness based on the computed weights alone.

Multi-start downhill simplex method for spatio-temporal source localization in magnetoencephalography.

Multistart Algorithms for MEG Empirical Data Analysis Reliably Characterize Locations and Time Courses of Multiple Sources

The results demonstrate that Multistart MEG analysis procedures can localize multiple regions of activity and characterize their time courses in a reliable fashion.

Multiple dipole modeling and localization from spatio-temporal MEG data

The authors present general descriptive models for spatiotemporal MEG (magnetoencephalogram) data and show the separability of the linear moment parameters and nonlinear location parameters in the MEG problem and present a subspace methodology and computational approach to solving the conventional least-squares problem.

Neuromagnetic source imaging with FOCUSS: a recursive weighted minimum norm algorithm.

The use of anatomical constraints with MEG beamformers

EEG and MEG: forward solutions for inverse methods

Novel reformulations of the basic EEG and MEG kernels that dispel the myth that EEG is inherently more complicated to calculate than MEG are presented and evidence that improvements over currently published BEM methods can be realized using alternative error-weighting methods is presented.

EEG source localization and imaging using multiple signal classification approaches.

The MUSIC approach is reviewed and its application to the localization of multiple current dipoles from EEG data is demonstrated and it is shown that the number of detectable sources can be determined in a recursive manner from the data.

Localization of brain electrical activity via linearly constrained minimum variance spatial filtering

This paper presents a development and analysis of the spatial filtering method for localizing sources of brain electrical activity from surface recordings and explores its sensitivity to deviations between actual and assumed data models.
...