Electroencephalogram (EEG) plays an important role in identifying brain activity and
behavior. However, the recorded electrical activity always be contaminated with artifacts and …

In this primer, we give a review of the inverse problem for EEG source localization. This is
intended for the researchers new in the field to get insight in the state-of-the-art techniques …

Compressed sensing (CS) utilizes the sparsity of magnetic resonance (MR) images to
enable accurate reconstruction from undersampled k-space data. Recent CS methods have …

We address the sparse signal recovery problem in the context of multiple measurement
vectors (MMV) when elements in each nonzero row of the solution matrix are temporally …

Compressed sensing is a technique to sample compressible signals below the Nyquist rate,
whilst still allowing near optimal reconstruction of the signal. In this paper we present a …

We present a nonparametric algorithm for finding localized energy solutions from limited
data. The problem we address is underdetermined, and no prior knowledge of the shape of …

There has been tremendous advances in our ability to produce images of human brain
function. Applications of functional brain imaging extend from improving our understanding …

In recent years, multiple noninvasive imaging modalities have been used to develop a better
understanding of the human brain functionality, including positron emission tomography …

Sparse Bayesian learning (SBL) and specifically relevance vector machines have received
much attention in the machine learning literature as a means of achieving parsimonious …

Objective: Electroencephalography (EEG) is an important tool for studying the temporal
dynamics of the human brain's large-scale neuronal circuits. However, most EEG …