This paper presents an extensive review on the artifact removal algorithms used to remove
the main sources of interference encountered in the electroencephalogram (EEG) …

This article provides a new, more comprehensive view of event-related brain dynamics
founded on an information-based approach to modeling electroencephalographic (EEG) …

Signals from eye movements and blinks can be orders of magnitude larger than brain‐
generated electrical potentials and are one of the main sources of artifacts in …

In this paper, we describe a simple set of “recipes” for the analysis of high spatial density
EEG. We focus on a linear integration of multiple channels for extracting individual …

The problem of source separation is an inductive inference problem. There is not enough
information to deduce the solution, so one must use any available information to infer the …

Independent component analysis (ICA) is a family of unsupervised learning algorithms that
have proven useful for the analysis of the electroencephalogram (EEG) and …

Because of the increasing portability and wearability of noninvasive electrophysiological
systems that record and process electrical signals from the human brain, automated systems …

Conventionalanalysis of electroencephalography (EEG) and magnetoencephalography
(MEG) often relies on averaging over multiple trials to extract statistically relevant differences …

Second-order blind identification (SOBI) is a blind source separation (BSS) algorithm that
can be used to decompose mixtures of signals into a set of components or putative …

We investigated the effect of memory load on encoding and maintenance of information in
working memory. Electroencephalography (EEG) signals were recorded while participants …