A unified Bayesian framework for MEG/EEG source imaging.
about
A multi-subject, multi-modal human neuroimaging dataset.Incorporating priors for EEG source imaging and connectivity analysisHow to use fMRI functional localizers to improve EEG/MEG source estimation.Informatics and data mining tools and strategies for the human connectome projectThe Iterative Reweighted Mixed-Norm Estimate for Spatio-Temporal MEG/EEG Source Reconstruction.A spatiotemporal dynamic distributed solution to the MEG inverse problem.A framework for the design of flexible cross-talk functions for spatial filtering of EEG/MEG data: DeFleCT.MNE software for processing MEG and EEG dataMEG and EEG data analysis with MNE-Python.STRAPS: A Fully Data-Driven Spatio-Temporally Regularized Algorithm for M/EEG Patch Source Imaging.Accurate reconstruction of brain activity and functional connectivity from noisy MEG data.Computational and dynamic models in neuroimaging.Cognitive Impairments in Schizophrenia as Assessed Through Activation and Connectivity Measures of Magnetoencephalography (MEG) DataSparse EEG/MEG source estimation via a group lasso.A Parametric Empirical Bayesian Framework for the EEG/MEG Inverse Problem: Generative Models for Multi-Subject and Multi-Modal Integration.Selecting forward models for MEG source-reconstruction using model-evidenceHuman brain dynamics accompanying use of egocentric and allocentric reference frames during navigation.EEG/fMRI fusion based on independent component analysis: integration of data-driven and model-driven methods.Source reconstruction accuracy of MEG and EEG Bayesian inversion approaches.Bayesian symmetrical EEG/fMRI fusion with spatially adaptive priors.Efficient posterior probability mapping using Savage-Dickey ratios.Non-Gaussian probabilistic MEG source localisation based on kernel density estimation.Reconstructing coherent networks from electroencephalography and magnetoencephalography with reduced contamination from volume conduction or magnetic field spreadA hierarchical Bayesian approach for learning sparse spatio-temporal decompositions of multichannel EEG.GALA: group analysis leads to accuracy, a novel approach for solving the inverse problem in exploratory analysis of group MEG recordings.Comparing the similarity and spatial structure of neural representations: a pattern-component model.Simultaneous head tissue conductivity and EEG source location estimation.Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods.Time-frequency mixed-norm estimates: sparse M/EEG imaging with non-stationary source activations.Increasing the accuracy of electromagnetic inverses using functional area source correlation constraints.Global and regional functional connectivity maps of neural oscillations in focal epilepsy.Electromagnetic source reconstruction for group studies.Magnetoencephalographic imaging of resting-state functional connectivity predicts postsurgical neurological outcome in brain gliomas.A Subspace Pursuit-based Iterative Greedy Hierarchical solution to the neuromagnetic inverse problem.Multivariate dynamical modelling of structural change during development.Using generative models to make probabilistic statements about hippocampal engagement in MEG.Comparing variational Bayes with Markov chain Monte Carlo for Bayesian computation in neuroimaging.Reconstructing anatomy from electro-physiological data.Comparing dynamic causal models using AIC, BIC and free energy.Multimodal functional imaging using fMRI-informed regional EEG/MEG source estimation.
P2860
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P2860
A unified Bayesian framework for MEG/EEG source imaging.
description
2008 nî lūn-bûn
@nan
2008年の論文
@ja
2008年論文
@yue
2008年論文
@zh-hant
2008年論文
@zh-hk
2008年論文
@zh-mo
2008年論文
@zh-tw
2008年论文
@wuu
2008年论文
@zh
2008年论文
@zh-cn
name
A unified Bayesian framework for MEG/EEG source imaging.
@en
A unified Bayesian framework for MEG/EEG source imaging.
@nl
type
label
A unified Bayesian framework for MEG/EEG source imaging.
@en
A unified Bayesian framework for MEG/EEG source imaging.
@nl
prefLabel
A unified Bayesian framework for MEG/EEG source imaging.
@en
A unified Bayesian framework for MEG/EEG source imaging.
@nl
P2860
P1433
P1476
A unified Bayesian framework for MEG/EEG source imaging.
@en
P2093
David Wipf
Srikantan Nagarajan
P2860
P304
P356
10.1016/J.NEUROIMAGE.2008.02.059
P407
P577
2008-03-18T00:00:00Z