Objective. Magneto-and electroencephalography (M/EEG) measurements record a mix of
signals from the brain, eyes, and muscles. These signals can be disentangled for artifact …

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging.
Linear classifiers are widely employed in the brain decoding paradigm to discriminate …

Background The use of machine learning models to discriminate between patterns of neural
activity has become in recent years a standard analysis approach in neuroimaging studies …

We propose a novel classification model for weak signal data, building upon a recent model
for Bayesian multi-view learning, Group Factor Analysis (GFA). Instead of assuming all data …

Purpose The acoustic source that is attended to by the listener in a mixture can be identified
with a certain accuracy on the basis of their neural response recorded during listening, and …

Improving the interpretability of multivariate models is of primary interest for many
neuroimaging studies. In this study, we present an application of multi-task learning (MTL) to …

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. It
is well known that the brain maps derived from weights of linear classifiers are hard to …