[PDF] Top 20 Symmetrical EEG-FMRI Imaging by Sparse Regularization
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Symmetrical EEG-FMRI Imaging by Sparse Regularization
... other, by constraining the EEG inverse problem to a specific region determined by fMRI [2], or by using time-frequency patterns from the EEG in the fMRI data processing ... Voir le document complet
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Symmetrical EEG-FMRI Imaging by Sparse Regularization
... other, by constraining the EEG inverse problem to a specific region determined by fMRI [2], or by using time-frequency patterns from the EEG in the fMRI data processing ... Voir le document complet
7
Blind Source Separation Approaches to Remove Imaging Artefacts in EEG Signals Recorded Simultaneously with fMRI
... acquisition EEG was acquired using the MRI-compatible BrainAmp MR (BrainProducts, Munich, Germany) EEG amplifier and the BrainCap electrode cap (EasyCap, Herrsching-Breitbrunn, Germany) with sintered ... Voir le document complet
6
Multi-subject MEG/EEG source imaging with sparse multi-task regression
... performed by the subjects is more advanced, complicating the discussion of the results in terms of ...the fMRI data allows for a quantification of the activation foci between MEG and ...that fMRI and ... Voir le document complet
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Sparse high dimensional regression in the presence of colored heteroscedastic noise : application to M/EEG source imaging
... this regularization requires to solve time-consuming high-dimensional optimization ...corrupted by strong non-white noise, which breaks the classical statistical assumptions of inverse ...solution: ... Voir le document complet
149
Sparse high dimensional regression in the presence of colored heteroscedastic noise: application to M/EEG source imaging
... this regularization requires to solve time-consuming high-dimensional optimization ...corrupted by strong non-white noise, which breaks the classical statistical assumptions of inverse ...solution: ... Voir le document complet
149
Multi-modal EEG and fMRI Source Estimation Using Sparse Constraints
... Resonance Imaging (fMRI) and electroencephalography (EEG). fMRI measures the oxygenation of the blood flow, which is closely correlated to the neuronal ...Although fMRI has a high ... Voir le document complet
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Hybrid EEG and fMRI platform for multi-modal neurofeedback
... and fMRI) NFB platform, at Neurinfo ...on EEG and/or fMRI, but its architecture and design principles are valid for any combination of two or more real-time brain activity measurement ... Voir le document complet
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Sparse-view X-ray CT reconstruction with Gamma regularization
... (delineated by red line) to highlight the local image ...norm regularization fails to provide satisfying results in sparse-view reconstruction (some structures are blurred in the ...-norm ... Voir le document complet
27
Learning 2-in-1: Towards Integrated EEG-fMRI-Neurofeedback
... electrode. fMRI acquisition was performed using echo-planar imaging (EPI) with the following parameters: repetition time (TR) / echo time (TE) = 1000/23ms, FOV = 210 × 210mm 2 , voxel size = 2 × 2 × 4mm 3 , ... Voir le document complet
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Accelerating magnetic resonance imaging by unifying sparse models and multiple receivers
... The first method, DESIGN denoising, addresses the noise amplification problem common to all accelerated parallel imaging methods by post-processing the reconstructed i[r] ... Voir le document complet
148
Sparse EEG Source Localization Using Bernoulli Laplacian Priors
... activity by mod- eling its time evolution and applying Kalman filtering [19], [20] or particle filters ...with sparse properties is to consider Bayesian tech- niques with appropriate priors assigned to the ... Voir le document complet
12
The default mode network and EEG regional spectral power: a simultaneous fMRI-EEG study.
... identified by visual inspection and comparison to previously published data ...spectrum EEG regional power as a covariant in the dual regression ...the EEG regional power at each ...divided by ... Voir le document complet
8
ICA-based sparse feature recovery from fMRI datasets
... Resonance Imaging (fMRI) to study cognition and brain ...of sparse features from fMRI ...and fMRI data and show that it outperforms state-of-the-art ... Voir le document complet
5
Multiclass Sparse Bayesian Regression for fMRI-Based Prediction
... plagued by the curse of dimensionality, as there are far more features (voxels) than samples ...of regularization to the available ...Multi-Class Sparse Bayesian Regression (MCBR), that is a ... Voir le document complet
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Sparse EEG Source Localization Using Bernoulli Laplacian Priors
... activity by mod- eling its time evolution and applying Kalman filtering [19], [20] or particle filters ...with sparse properties is to consider Bayesian tech- niques with appropriate priors assigned to the ... Voir le document complet
13
Can we learn from coupling EEG-fMRI to enhance neuro-feedback in EEG only?
... 𝑋 % 𝑡, 𝑒, 𝑏 = 𝐹𝑟𝑒𝑞 𝐸𝐸𝐺 𝑒, 𝐼 B , 𝐹 C , ∀ 𝑡 ∈ 1, … , 𝑇 and ∀ 𝑏 ∈ {1, … 𝐵} ∀ 𝑒 ∈ 1, … , 𝐸 , and ∀ 𝑏 ∈ {1, … 𝐵} - Optimisation : structured sparse regularisation following 3 ... Voir le document complet
2
Spatial regularization based on dMRI to solve EEG/MEG inverse problem
... VI. C ONCLUSION This paper presented a method to use information from dMRI to solve the EEG/MEG inverse problem. A weight- ing matrix whose elements are obtained from computing the similarity between the ... Voir le document complet
5
Region segmentation for sparse decompositions: better brain parcellations from rest fMRI
... 2 Alexandre Abraham et al. and require a post-processing step to extract the salient features, i.e., brain regions, which is often done manually [5] (see figure 4). To avoid post-processing and directly extract regions, ... Voir le document complet
9
fMRI Deconvolution via Temporal Regularization using a LASSO model and the LARS algorithm
... I. INTRODUCTION Deconvolution methods are used to denoise the blood oxygen level-dependent (BOLD) response, the signal that forms the basis of functional MRI (fMRI) [1]. In this work we propose a temporal ... Voir le document complet
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