Spatiotemporal Sparse Bayesian Learning With Applications to Compressed Sensing of Multichannel Physiological Signals

Zhang, Zhilin; Jung, Tzyy-Ping; Makeig, Scott; Pi, Zhouyue; Rao, Bhaskar D.

doi:10.1109/tnsre.2014.2319334

Cited by 99 publications

(74 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This estimate requires K < N and will underestimate the noise for small L. Several estimators for the noise σ 2 are proposed based on EM [8], [12], [13], [22], [23]. Empirically, neither of these converge well in our application.…”

Section: F Noise Variance Estimation (Hyperparameter σmentioning

confidence: 98%

Multisnapshot Sparse Bayesian Learning for DOA

Gerstoft

Mecklenbräuker

Xenaki

et al. 2016

IEEE Signal Process. Lett.

226

178

View full text Add to dashboard Cite

Abstract-March 1, 2016 The directions of arrival (DOA) of plane waves are estimated from multi-snapshot sensor array data using Sparse Bayesian Learning (SBL). The prior source amplitudes is assumed independent zero-mean complex Gaussian distributed with hyperparameters the unknown variances (i.e. the source powers). For a complex Gaussian likelihood with hyperparameter the unknown noise variance, the corresponding Gaussian posterior distribution is derived. For a given number of DOAs, the hyperparameters are automatically selected by maximizing the evidence and promote sparse DOA estimates. The SBL scheme for DOA estimation is discussed and evaluated competitively against LASSO ( 1-regularization), conventional beamforming, and MUSIC.

show abstract

Section: F Noise Variance Estimation (Hyperparameter σmentioning

confidence: 98%

Multisnapshot Sparse Bayesian Learning for DOA

Gerstoft

Mecklenbräuker

Xenaki

et al. 2016

IEEE Signal Process. Lett.

226

178

View full text Add to dashboard Cite

show abstract

“…Another classical way assumes that multiple channels share a similar support of sparse vector. This generalizes the single measurement vector (SMV) problem straightforwardly to a multiple measurement vector (MMV) problem [15] [16]. [17] proposed to incorporate preprocessing and entropy coding in the sampling to reduce the redundance in correlated multi-channel signals, but the added preprocessing and encoder would increase the power consumption in EEG sampling [4]; and the procedure can hardly be realized for analog signals, which implies the analog EEG signals should be sampled at Nyquist sampling rate in the beginning.…”

Section: Introductionmentioning

confidence: 99%

Compressed Sensing of Multichannel EEG Signals: The Simultaneous Cosparsity and Low-Rank Optimization

Liu

Vos

Huffel

2015

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

show abstract

“…• a spatio-temporal sparse Bayesian learning (STSBL) algorithm [37]. For quantitative comparison, the following measures are used:…”

Section: Methodsmentioning

confidence: 99%

Spatio-Temporal Structured Sparse Regression With Hierarchical Gaussian Process Priors

Kuzin

Isupova

Mihaylova

2018

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

This paper introduces a new sparse spatio-temporal structured Gaussian process regression framework for online and offline Bayesian inference. This is the first framework that gives a time-evolving representation of the interdependencies between the components of the sparse signal of interest. A hierarchical Gaussian process describes such structure and the interdependencies are represented via the covariance matrices of the prior distributions. The inference is based on the expectation propagation method and the theoretical derivation of the posterior distribution is provided in the paper. The inference framework is thoroughly evaluated over synthetic, real video and electroencephalography (EEG) data where the spatio-temporal evolving patterns need to be reconstructed with high accuracy. It is shown that it achieves 15% improvement of the F-measure compared with the alternating direction method of multipliers, spatio-temporal sparse Bayesian learning method and one-level Gaussian process model. Additionally, the required memory for the proposed algorithm is less than in the one-level Gaussian process model. This structured sparse regression framework is of broad applicability to source localisation and object detection problems with sparse signals.When the number of observations is less than the number of coefficients the problem is ill-posed in the sense that it has D.Kuzin, L.Mihaylova are with the

show abstract

Spatiotemporal Sparse Bayesian Learning With Applications to Compressed Sensing of Multichannel Physiological Signals

Cited by 99 publications

References 40 publications

Multisnapshot Sparse Bayesian Learning for DOA

Multisnapshot Sparse Bayesian Learning for DOA

Compressed Sensing of Multichannel EEG Signals: The Simultaneous Cosparsity and Low-Rank Optimization

Spatio-Temporal Structured Sparse Regression With Hierarchical Gaussian Process Priors

Contact Info

Product

Resources

About