Transient Artifact Reduction Algorithm (TARA) Based on Sparse Optimization

Selesnick, Ivan; Graber, Harry L.; Ding, Yin; Zhang, Tong; Barbour, Randall L.

doi:10.1109/tsp.2014.2366716

Cited by 38 publications

(26 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, we average referenced them. Based on the artifact properties of sparse spikes and step-like discontinuities of the EEG data, we further use Transient Artifact Reduction Algorithm (TARA, the theory will be detailed latter) (Selesnick et al 2014) to clean these artifacts, the results are shown in Fig. 4 below.…”

Section: Preprocessing Module: Eecog_preprocmentioning

confidence: 99%

“…This platform also supports FieldTrip (Oostenveld et al 2011) and EEGLab (Delorme and Scott 2004) data structure. Here, we give further brief introduction to the theory behind TARA (Selesnick et al 2014). The two types of artifacts that TARA is aiming to remove are: sparse spikes (sparse functions) and step-like discontinuities (functions with sparse first derivatives), and the model (for one channel) can be written as:…”

Section: Preprocessing Module: Eecog_preprocmentioning

confidence: 99%

See 1 more Smart Citation

EECoG-Comp: An Open Source Platform for Concurrent EEG/ECoG Comparisons: applications to connectivity stuides

Wang

Valdés-Hernández

Paz-Linares

et al. 2018

Preprint

View full text Add to dashboard Cite

Electrophysiological Source Imaging (ESI) methods are hampered by the lack of "gold standards" for model comparison. Concurrent electroencephalography (EEG) and electrocorticography (ECoG) recordings (namely EECoG) are considered gold standard to validating EEG generative models with primate models have the unique advantages of both flexibility and translational value in human research. However the severe EEG artifacts during such invasive experiments, the complexity of providing sufficiently detailed biophysical models, as well as lacking sound statistical connectivity comparison methods have hampered the availability and analysis of such datasets. In this paper, 1) we provide EECoG-Comp: an open source platform (https://github.com/Vincent-wq/EECoG-Comp) which encompasses the preprocessing, forward modeling, simulation and comparison module; 2) we take the simultaneous EECoG dataset from www.neurotycho.org as an example to illustrate the use of this platform and compare the source connectivity estimation performance of 4 popular ESI methods named MNE, LCMV, eLORETA and SSBL. The conclusion shows the limits of performance of these ESI connectivity estimators using both simulations and real data analysis. In fact, the use of this platform also suggests the need for both improved simultaneous EEG and ECoG experiments and ESI connectivity estimators.2 Noteworthily, more recent and realistic phantoms, even 3D printed, are now available for these tests (Collier et al., 2012; Li et. Al 2013; Zhang et al. 2013). source setting are: , rand ESI H,eeg,Θ J (human EEG with different ESI methods) and , ,, rand ESI rand ESI M,eeg,Θ M,ecog,Θ J J (monkey EEG and ECoG source activities estimated from different ESI methods), and the source estimators for the block source setting are , block ESI H,eeg,Θ J (human EEG with different ESI methods) and , ,, block ESI block ESI M,eeg,Θ M,ecog,Θ J J (monkey EEG and ECoG source activities estimated from different ESI methods). With this simulation, we have both the "ground truth" source activities , rand block Θ Θ J J with the "ground truth" source connecitivity , rand block   as connectivity, and their corresponding estimators from human EEG, monkey EEG and monkey ECoG simulations with different number of sources, different number of sensors and different ESI methods. Before further analysis, we bandpass all the estimated source activation to 8-12Hz and limit the connectivity estimations to alpha band. In order to compare connectivity from different ESI results, we use graphical LASSO (Hsieh et al. 2014) to reconstruct the connectivity estimators (ˆ, rand block   ) and compare them with the tools provided by EECoG_conComp. Notice that, for the source activities, we have the "ground truth" , rand block Θ Θ J J , and we can estimate the source connectivity from them without applying any ESI methods, which is taken as the basic performance to compared against. Finally, we summaize the simulation results with Performance Improvement Ratio ( PIR ESI ) of a specific ESI method, which is d...

show abstract

Section: Preprocessing Module: Eecog_preprocmentioning

confidence: 99%

Section: Preprocessing Module: Eecog_preprocmentioning

confidence: 99%

EECoG-Comp: An Open Source Platform for Concurrent EEG/ECoG Comparisons: applications to connectivity stuides

Wang

Valdés-Hernández

Paz-Linares

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…This problem can be solved with different approaches, such as alternating direction method of multipliers (ADMM) [34], majorization minimization [35], proximal algorithm [36] and iteratively reweighted least squares minimization [37]. Here we present the formulation using ADMM algorithm.…”

Section: Second Approach: Sparse Decomposition Algorithmmentioning

confidence: 99%

Screen Content Image Segmentation Using Robust Regression and Sparse Decomposition

Minaee

Wang

2016

IEEE J. Emerg. Sel. Topics Circuits Syst.

View full text Add to dashboard Cite

This paper considers how to separate text and/or graphics from smooth background in screen content and mixed document images and proposes two approaches to perform this segmentation task. The proposed methods make use of the fact that the background in each block is usually smoothly varying and can be modeled well by a linear combination of a few smoothly varying basis functions, while the foreground text and graphics create sharp discontinuity. The algorithms separate the background and foreground pixels by trying to fit background pixel values in the block into a smooth function using two different schemes. One is based on robust regression, where the inlier pixels will be considered as background, while remaining outlier pixels will be considered foreground. The second approach uses a sparse decomposition framework where the background and foreground layers are modeled with a smooth and sparse components respectively. These algorithms have been tested on images extracted from HEVC standard test sequences for screen content coding, and are shown to have superior performance over previous approaches. The proposed methods can be used in different applications such as text extraction, separate coding of background and foreground for compression of screen content, and medical image segmentation.

show abstract

“…proposed here uses a similar technique to recover the lowpass component, but in contrast to LPF/TVD, it is more general -the regularization is more flexible with a tunable parameter, so that LPF/TVD can be considered as a special case. Another algorithm related to the approach taken in this paper is the transient artifact reduction algorithm (TARA) [60] which is utilized to suppress additive piecewise constant artifacts and spikes (similar to a hybrid of Type 2 and Type 3 artifact). The approaches proposed in this work target different types of artifacts (Type 0 and Type 1) and applied in different applications.…”

Section: Related Workmentioning

confidence: 99%

Sparsity-based correction of exponential artifacts

Ding

Selesnick

2016

Signal Processing

Self Cite

View full text Add to dashboard Cite

This paper describes an exponential transient excision algorithm (ETEA). In biomedical time series analysis, e.g., in vivo neural recording and electrocorticography (ECoG), some measurement artifacts take the form of piecewise exponential transients. The proposed method is formulated as an unconstrained convex optimization problem, regularized by smoothed ℓ1-norm penalty function, which can be solved by majorization-minimization (MM) method. With a slight modification of the regularizer, ETEA can also suppress more irregular piecewise smooth artifacts, especially, ocular artifacts (OA) in electroencephalography (EEG) data. Examples of synthetic signal, EEG data, and ECoG data are presented to illustrate the proposed algorithms.

show abstract

Transient Artifact Reduction Algorithm (TARA) Based on Sparse Optimization

Cited by 38 publications

References 37 publications

EECoG-Comp: An Open Source Platform for Concurrent EEG/ECoG Comparisons: applications to connectivity stuides

EECoG-Comp: An Open Source Platform for Concurrent EEG/ECoG Comparisons: applications to connectivity stuides

Screen Content Image Segmentation Using Robust Regression and Sparse Decomposition

Sparsity-based correction of exponential artifacts

Contact Info

Product

Resources

About