Journal ArticleDOI
Extraction of a source from multichannel data using sparse decomposition
TLDR
Simulations with synthetic evoked responses mixed into natural 122-channel MEG data show significant improvement in accuracy of signal restoration and the convex optimization problem is solved by a Newton-type method.About:
This article is published in Neurocomputing.The article was published on 2002-12-01. It has received 77 citations till now. The article focuses on the topics: Sparse approximation & Blind signal separation.read more
Citations
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Journal ArticleDOI
ICA with Reference
TL;DR: A neural algorithm is proposed using a Newton-like approach to obtain an optimal solution to the constrained optimization problem and experiments with synthetic signals and real fMRI data demonstrate the efficacy and accuracy of the proposed algorithm.
Journal ArticleDOI
Autofocus for digital Fresnel holograms by use of a Fresnelet-sparsity criterion
Michael Liebling,Michael Unser +1 more
TL;DR: This work proposes a robust autofocus method for reconstructing digital Fresnel holograms that maximizes a sharpness metric related to the sparsity of the signal's expansion in distance-dependent waveletlike Fresnelet bases.
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Lp Norm Iterative Sparse Solution for EEG Source Localization
TL;DR: A novel iterative EEG source imaging algorithm, Lp norm iterative sparse solution (LPISS), which was applied to a real evoked potential collected in a study of inhibition of return (IOR), and the result was consistent with the previously suggested activated areas involved in an IOR process.
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Letters: Robust extraction of specific signals with temporal structure
Zhilin Zhang,Zhang Yi +1 more
TL;DR: A robust algorithm based on eigenvalue decomposition of several delayed covariance matrices is proposed that is faster and has better performance, which is confirmed by theoretical analysis and computer simulations.
Journal ArticleDOI
Letters: Extraction of a source signal whose kurtosis value lies in a specific range
Zhilin Zhang,Zhang Yi +1 more
TL;DR: This letter proposes an algorithm, which extracts the desired signal with a priori knowledge about its statistics, if the authors know the range in which the kurtosis value of the desired sign lies, and can use this algorithm to extract it.
References
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Book
A wavelet tour of signal processing
TL;DR: An introduction to a Transient World and an Approximation Tour of Wavelet Packet and Local Cosine Bases.
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Atomic Decomposition by Basis Pursuit
TL;DR: Basis Pursuit (BP) is a principle for decomposing a signal into an "optimal" superposition of dictionary elements, where optimal means having the smallest l1 norm of coefficients among all such decompositions.
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Fast and robust fixed-point algorithms for independent component analysis
TL;DR: Using maximum entropy approximations of differential entropy, a family of new contrast (objective) functions for ICA enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions.
Journal ArticleDOI
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
TL;DR: It is shown that a learning algorithm that attempts to find sparse linear codes for natural scenes will develop a complete family of localized, oriented, bandpass receptive fields, similar to those found in the primary visual cortex.