Haut PDF Multi-subject MEG/EEG source imaging with sparse multi-task regression
Multi-subject MEG/EEG source imaging with sparse multi-task regression
... ill-posed regression problem known as source ...the regression tasks independently for each ...single multi-task regression, one makes the problem better posed, offering the ...
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Manifold-regression to predict from MEG/EEG brain signals without source modeling
... MNE source imaging technique ...the MEG device coordinate system [3] and the coor- dinate alignment is hard to ...MNE with Q = 8196 candidate ...ridge regression and tuned its ...
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Decoding perceptual thresholds from MEG/EEG
... working with ordered targets and demonstrated how the predictions errors can offer interesting insights on the ...a multi-class classifier blind to targets order and with little training samples per ...
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MEG/EEG source imaging with a non-convex penalty in the time-frequency domain
... spatially sparse and temporally ...matrix with a block row structure with intra- row sparsity [5], which we promote by applying a composite non-convex regularization functional ...regularized ...
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M/EEG source localization with multi-scale time-frequency dictionaries
... algorithm; multi-scale dictionary; Gabor ...brain imaging with high tem- poral and good spatial ...the source localization problem from M/EEG data have been proposed in the ...activity ...
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M/EEG source localization with multi-scale time-frequency dictionaries
... algorithm; multi-scale dictionary; Gabor ...brain imaging with high tem- poral and good spatial ...the source localization problem from M/EEG data have been proposed in the ...activity ...
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Group level MEG/EEG source imaging via optimal transport: minimum Wasserstein estimates
... neural source to be either active for all subjects or for none of ...several multi-task regression models that relax this ...the multi-task Wasserstein (MTW) model ...the ...
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The iterative reweighted Mixed-Norm Estimate for spatio-temporal MEG/EEG source reconstruction
... an MEG/EEG in- verse solver based on regularized regression with a non- convex block-separable ...practical MEG/EEG ...offline source reconstruction, which is still the ...
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Improved MEG/EEG source localization with reweighted mixed-norms
... a sparse MEG/EEG source imaging approach based on regularized regression with a ℓ ...to MEG/EEG inverse problems with and without orientation ...
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Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression
... the regression coefficients and the noise ...eroscedastic regression, with an emphasis on brain imaging with magneto- and electroen- cephalography ...identification with correct ...
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Multi-modal EEG and fMRI Source Estimation Using Sparse Constraints
... simultaneous EEG and fMRI are recorded from 8 healthy right-handed subjects while doing a motor task of clenching the right hand ...are task each followed by a rest block. EEG is obtained via ...
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Brain source imaging: from sparse to tensor models
... the source positions, though not their spatial extent as they are conceived for focal sources, while ExSo-MUSIC, STWV-DA, and VB-SCCD also permit to obtain an accurate estimate of the source ...the ...
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Robust Multi-image Processing With Optimal Sparse Regularization
... Abstract Sparse modeling can be used to character- ize outlier type ...to sparse recovery the- ory, it was shown that 1-norm super-resolution is ro- bust to outliers if enough images are ...over, ...
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Improving Multi-Frame Data Association with Sparse Representations for Robust Near-Online Multi-Object Tracking
... these variants, we use for each variant and ∆t value the hyper-optimization procedure discussed previously to find the best set of parameters. MOTA values and IDS are indicated in Fig. 4. First of all, they show that the ...
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Aligning and Updating Cadaster Maps with Aerial Images by Multi-Task, Multi-Resolution Deep Learning
... The multi-task, multi-resolution method presented in this paper can be used to effectively solve the common problem of aligning existing maps over a new satellite image while also detecting new ...
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Adaptive multi-class Bayesian sparse regression - An application to brain activity classification
... Preliminary results on real data shows the advantages of our method. The VBK algorithm gives access to highly interpretable loadings maps which are a powerful tool for understanding brain activity. Moreover, the free ...
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Subject-specific time-frequency selection for multi-class motor imagery-based BCIs using few Laplacian EEG channels
... 夽 This paper is based on Yuan Yang’s Ph.D. work in Télécom ParisTech. Yuan Yang has finished his PhD in Télécom ParisTech and been with Department of Biome- chanical Engineering, Delft University of Technology, ...
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Multi-Task Learning For Option Pricing
... The experiments performed using the single-task learning method were chosen in such a way as to test its generalization performance on different periods of time, and for each period of time to test the effect of ...
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A multi-robot cooperative task achievement system
... A multi-robot cooperative task achievement system Silvia Silva da Costa Botelho, Rachid Alami.. To cite this version: Silvia Silva da Costa Botelho, Rachid Alami.[r] ...
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Study of efficiency of multi view system in multi-disciplinary collaboration task
... and multi-user ...our multi- view CHI technology can help users to work with each other in 3D virtual environment co- located and concurrently 10 ...
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