Electrophysiological source imaging: a noninvasive window to brain dynamics
B He, A Sohrabpour, E Brown… - Annual review of …, 2018 - annualreviews.org
Brain activity and connectivity are distributed in the three-dimensional space and evolve in
time. It is important to image brain dynamics with high spatial and temporal resolution …
time. It is important to image brain dynamics with high spatial and temporal resolution …
[HTML][HTML] A parametric empirical Bayesian framework for the EEG/MEG inverse problem: generative models for multi-subject and multi-modal integration
We review recent methodological developments within a parametric empirical Bayesian
(PEB) framework for reconstructing intracranial sources of extracranial …
(PEB) framework for reconstructing intracranial sources of extracranial …
Time-frequency mixed-norm estimates: Sparse M/EEG imaging with non-stationary source activations
Magnetoencephalography (MEG) and electroencephalography (EEG) allow functional brain
imaging with high temporal resolution. While solving the inverse problem independently at …
imaging with high temporal resolution. While solving the inverse problem independently at …
A distributed spatio-temporal EEG/MEG inverse solver
W Ou, MS Hämäläinen, P Golland - NeuroImage, 2009 - Elsevier
We propose a novel ℓ1ℓ2-norm inverse solver for estimating the sources of EEG/MEG
signals. Based on the standard ℓ1-norm inverse solvers, this sparse distributed inverse …
signals. Based on the standard ℓ1-norm inverse solvers, this sparse distributed inverse …
Combined spatial and non-spatial prior for inference on MRI time-series
AR Groves, MA Chappell, MW Woolrich - Neuroimage, 2009 - Elsevier
When modelling FMRI and other MRI time-series data, a Bayesian approach based on
adaptive spatial smoothness priors is a compelling alternative to using a standard …
adaptive spatial smoothness priors is a compelling alternative to using a standard …
A Parametric Empirical Bayesian framework for fMRI‐constrained MEG/EEG source reconstruction
We describe an asymmetric approach to fMRI and MEG/EEG fusion in which fMRI data are
treated as empirical priors on electromagnetic sources, such that their influence depends on …
treated as empirical priors on electromagnetic sources, such that their influence depends on …
Solving the EEG inverse problem based on space–time–frequency structured sparsity constraints
We introduce STOUT (spatio-temporal unifying tomography), a novel method for the source
analysis of electroencephalograpic (EEG) recordings, which is based on a physiologically …
analysis of electroencephalograpic (EEG) recordings, which is based on a physiologically …
Hierarchical Bayesian inference for the EEG inverse problem using realistic FE head models: depth localization and source separation for focal primary currents
The estimation of the activity-related ion currents by measuring the induced electromagnetic
fields at the head surface is a challenging and severely ill-posed inverse problem. This is …
fields at the head surface is a challenging and severely ill-posed inverse problem. This is …
Fast and robust Block-Sparse Bayesian learning for EEG source imaging
We propose a new Sparse Bayesian Learning (SBL) algorithm that can deliver fast, block-
sparse, and robust solutions to the EEG source imaging (ESI) problem in the presence of …
sparse, and robust solutions to the EEG source imaging (ESI) problem in the presence of …
A spatiotemporal dynamic distributed solution to the MEG inverse problem
C Lamus, MS Hämäläinen, S Temereanca, EN Brown… - NeuroImage, 2012 - Elsevier
MEG/EEG are non-invasive imaging techniques that record brain activity with high temporal
resolution. However, estimation of brain source currents from surface recordings requires …
resolution. However, estimation of brain source currents from surface recordings requires …