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Asymptotics for Linear Processes
A method of deriving asymptotics for linear processes is introduced which uses an explicit algebraic decomposition of the linear filter. The method leads to substantial simplifications in theExpand
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Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering
TLDR
We use the Bayes' rule Chapman-Kolmogorov paradigm with a linear state equation and point process observation models to derive adaptive filters appropriate for estimation from neural spike trains. Expand
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Motion and Ballistocardiogram Artifact Removal for Interleaved Recording of EEG and EPs during MRI
TLDR
This paper introduces a method for motion artifact cancellation using piezoelectric motion sensor information to estimate the mapping between motion sensor and EEG space, subtracting the motion-related noise from the raw EEG signal. Expand
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Adaptive Signal Processing Algorithms: Stability and Performance
PART I. 1. Introduction. 2. Offline Analysis. 3. Iterative Minimization. 4. Algorithm Construction. 5. Algorithm Analysis: Gaussian White Noise Setting. 6. Algorithm Analysis: Deterministic GlobalExpand
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On $l_q$ Optimization and Matrix Completion
TLDR
In this paper, we bridge the gap between these two penalties and propose the rank penalized least squares problem for matrix completion. Expand
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Dynamic Analyses of Information Encoding in Neural Ensembles
TLDR
We present a general recursive filter decoding algorithm based on a point process model of individual neuron spiking activity and a linear stochastic state-space model of the biological signal. Expand
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An analysis of neural receptive field plasticity by point process adaptive filtering
TLDR
We present an adaptive filter algorithm for tracking neural receptive field plasticity based on point process models of spike train activity. Expand
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Dimension Estimation in Noisy PCA With SURE and Random Matrix Theory
  • M. Ulfarsson, V. Solo
  • Mathematics, Computer Science
  • IEEE Transactions on Signal Processing
  • 1 December 2008
TLDR
We have applied Stein's unbiased risk estimator (SURE) to the problem of rank selection in noisy PCA in the important practical case where data and variable dimension are in the realm of RMT. Expand
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$l_{q}$ Sparsity Penalized Linear Regression With Cyclic Descent
TLDR
In this paper we present a novel cyclic descent algorithm for optimizing the resulting lq penalized least squares problem for noisy sparse signal estimation. Expand
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Modeling of two-dimensional random fields by parametric cepstrum
  • V. Solo
  • Mathematics, Computer Science
  • IEEE Trans. Inf. Theory
  • 1 November 1986
TLDR
A method is presented for parametric modeling of stationary random fields, based on the cepstrum, using fast Fourier transforms in the fitting process. Expand
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