Over the last ten years blind source separation (BSS) has become a prominent processing
tool in the study of human electroencephalography (EEG). Without relying on head modeling …

An outstanding challenge for consciousness research is to characterize the neural signature
of conscious access independently of any decisional processes. Here we present a model …

In this chapter, we discuss a statistical generative model called independent component
analysis. It is basically a proper probabilistic formulation of the ideas underpinning sparse …

Second‐order source separation (SOS) is a data analysis tool which can be used for
revealing hidden structures in multivariate time series data or as a tool for dimension …

Starting with a simple generative model and the assumption of statistical independence of
the underlying components, independent component analysis (ICA) decomposes a given …

Approximate joint diagonalization of a set of matrices is an essential tool in many blind
source separation (BSS) algorithms. A common measure of the attained diagonalization of …

This paper proposes an automatic method for artefact removal and noise elimination from
scalp electroencephalogram recordings (EEG). The method is based on blind source …

We propose a new low-complexity approximate joint diagonalization (AJD) algorithm, which
incorporates nontrivial block-diagonal weight matrices into a weighted least-squares (WLS) …

A novel (differential) entropy estimator is introduced where the maximum entropy bound is
used to approximate the entropy given the observations, and is computed using a numerical …

Independent Component Analysis (ICA) plays an important role in biomedical engineering.
Indeed, the complexity of processes involved in biomedicine and the lack of reference …