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Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm
TLDR
We present a nonparametric algorithm for finding localized energy solutions from limited data. Expand
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Automatic removal of eye movement and blink artifacts from EEG data using blind component separation.
Signals from eye movements and blinks can be orders of magnitude larger than brain-generated electrical potentials and are one of the main sources of artifacts in electroencephalographic (EEG) data.Expand
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A recursive weighted minimum norm algorithm: Analysis and applications
TLDR
Iterative weighted norm minimization method that utilizes a posteriori constraints for the estimation of highly localized signal from insufficient data . Expand
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Tracking eye fixations with electroocular and electroencephalographic recordings.
We describe a method, based on recordings of the electroencephalogram (EEG) and eye movement potentials (electrooculogram), to track where on a screen (x,y coordinates) an individual is fixating. TheExpand
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JOINT CUMULANT AND CORRELATION BASED SIGNAL SEPARATION WITH APPLICATION TO EEG DATA ANALYSIS �
Current methods in Blind Source Separation (BSS) utilize either the higher order statistics or the time delayed crosscorrelations to perform signal separation. In this paper we investigate a methodExpand
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Dynamical Theory Formalism for Robust Modeling of Damped, Undamped, and Nonlinear Oscillatory Signals
  • I. F. Gorodnitsky
  • Computer Science, Mathematics
  • IEEE International Conference on Acoustics…
  • 15 April 2007
TLDR
The paper explores a novel framework for signal representation based on dynamic information in a signal that is well suited for robust analysis of low SNR signals and extraction of time-varying features. Expand
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Affine scaling transformation based methods for computing low complexity sparse solutions
TLDR
This paper presents affine scaling transformation based methods for finding low complexity sparse solutions to optimization problems. Expand
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Truncated Total Least Squares Regularization Underdetermined Problems
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Variability in AC amplifier distortions: estimation and correction.
AC amplifiers can introduce significant distortions into the low frequency and DC components of recorded electrophysiological data such as event-related potentials (ERPs). Methods for correcting suchExpand
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Analysis of error produced by truncated SVD and Tikhonov regularization methods
The paper considers regularization of inverse solutions to linear problems. We derive the relationship between the error due to regularization and the energy distribution in the observation vectorExpand
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