Proceedings ArticleDOI
Regularized FOCUSS algorithm for EEG/MEG source imaging
Jooman Han,Kwang Suk Park +1 more
- Vol. 1, pp 122-124
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TLDR
A generalized version of the regularized FOCUSS algorithm, derived from a paper by Phillips, JW et al., (1997), allows general forms of noise covariance and reduces depth effect when imaging focal neural sources from electroencephalography / magnetoencephalographic data.Abstract:
We derived a generalized version of the regularized FOCUSS algorithm which was derived in a paper by Phillips, JW et al., (1997). It allows general forms of noise covariance and reduces depth effect when imaging focal neural sources from electroencephalography (EEG) / magnetoencephalography (MEG) data. We compared a depth-weighted regularized algorithm with FOCUSS and a regularized FOCUSS through simulation study. The suggested algorithm gave sparser and less spurious solutions than the others.read more
Citations
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Journal ArticleDOI
Improved M-FOCUSS Algorithm With Overlapping Blocks for Locally Smooth Sparse Signals
Rafal Zdunek,Andrzej Cichocki +1 more
TL;DR: This paper proposes three modifications to the M-FOCUSS algorithm to make it more efficient for sparse and locally smooth solutions, motivated by the simultaneously autoregressive (SAR) model.
Journal ArticleDOI
Convergence Analysis of the FOCUSS Algorithm
TL;DR: In this paper, the convergence of the FOCUSS algorithm is investigated and a rigorous derivation for the algorithm is given by exploiting the auxiliary function and its convergence is further proved by stability analysis.
Journal ArticleDOI
Rate of Convergence of the FOCUSS Algorithm
TL;DR: It is proved that the FOCUSS algorithm converges superlinearly for $0 and linearly for $1\le p<2$ • usually, but maysuperlinearly in some very special scenarios.
Proceedings ArticleDOI
Tomographic image reconstruction from limited-view projections with Wiener filtered focuss algorithm
TL;DR: In tomographic image reconstruction from limited-view projections the underlying inverse problem is ill-posed with the rank-deficient system matrix, and hence a priori knowledge is needed to improve the reconstruction.
Dissertation
Problema inverso dinámico aplicado a la identificación de sistemas multivariables
Fetecua Valencia,Juan Gabriel +1 more
TL;DR: A methodology for the dynamic inverse problem solution applied to the identification of multivariable systems, specifically EEG source localization (ESL), considering Tikhonov based methods, linear and nonlinear Kalman filters with invariant and time-varying parameters with different levels of noise in the signal.
References
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Journal ArticleDOI
Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm
I.F. Gorodnitsky,Bhaskar D. Rao +1 more
TL;DR: A view of the algorithm as a novel optimization method which combines desirable characteristics of both classical optimization and learning-based algorithms is provided and Mathematical results on conditions for uniqueness of sparse solutions are also given.
Journal ArticleDOI
Subset selection in noise based on diversity measure minimization
TL;DR: A Bayesian framework is used to account for noise in the data and a maximum a posteriori (MAP) estimation procedure leads to an iterative procedure which is a regularized version of the focal underdetermined system solver (FOCUSS) algorithm that is superior to the OMP in noisy environments.
Journal ArticleDOI
MEG-based imaging of focal neuronal current sources
TL;DR: A Bayesian formulation of the inverse problem in which a Gibbs prior is constructed to reflect the sparse focal nature of neural current sources associated with evoked response data is described.
Book ChapterDOI
MEG-based imaging of focal neuronal current sources
TL;DR: A Bayesian formulation of the inverse problem in which a Gibbs prior is constructed to reflect the sparse focal nature of the current sources and its performance is compared with several weighted minimum norm methods.