Oscillatory processes at all spatial scales and on all frequencies underpin brain function.
Electrophysiological Source Imaging (ESI) is the data-driven brain imaging modality that …

Brain electrical activity in different spectral bands has been associated with diverse
mechanisms underlying Brain function. Deeper reconnoitering of these mechanisms entails …

Electromagnetic source imaging (ESI) and independent component analysis (ICA) are two
popular and apparently dissimilar frameworks for M/EEG analysis. This letter shows that the …

The estimation of EEG generating sources constitutes an Inverse Problem (IP) in
Neuroscience. This is an ill-posed problem due to the non-uniqueness of the solution and …

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 Bayesian Learning (SBL) approaches to the EEG inverse problem such as
Champagne have been shown to outperform traditional ℓ 1-norm based methods in terms of …

In this paper, we focus on the inverse problem of reconstructing distributional brain activity
with cortical and weakly detectable deep components in non-invasive …

Electroencephalographic (EEG) source imaging (ESI) is a powerful method for studying
brain functions and surgical resection of epileptic foci. However, accurately estimating the …

Accurate reconstruction of the spatio-temporal dynamics of event-related cortical oscillations
across human brain regions is an important problem in functional brain imaging and human …

Electroencephalographic (EEG) source imaging depends upon sophisticated signal
processing algorithms for data cleaning, source separation, and localization. Typically, these …