Blind source separation (BSS) techniques, such as independent component analysis (ICA),
are increasingly being used in biomedical signal processing applications, including the …

Independent component analysis (ICA) is a class of algorithms widely applied to separate
sources in EEG data. Most ICA approaches use optimization criteria derived from temporal …

Objective: Blind source separation (BSS) can be used to decompose complex
electroencephalography (EEG) or magnetoencephalography data into simpler components …

Abstract Blind Source Separation (BSS) approaches for multi-channel EEG processing are
popular, and in particular Independent Component Analysis (ICA) algorithms have proven …

This paper addresses the problem of blind source separation for underdetermined mixtures
(ie, more sources than sensors) of event-related sources that include quasi-periodic sources …

Blind source separation (BSS) methods have received extensive attention over the past two
decades; thanks to its wide applicability in a number of areas such as biomedical …

A mandatory assumption in blind source separation (BSS) of the human
electroencephalogram (EEG) is that the mixing matrix remains invariant, ie, that the sources …

Blind source separation (BSS) algorithms extract neural signals from
electroencephalography (EEG) data. However, it is difficult to quantify source separation …

Independent component analysis (ICA) is increasing in popularity in the field of biomedical
signal processing. It is generally used when it is required to separate measured multi …

Second-order blind identification (SOBI) is a blind source separation (BSS) algorithm that
has been applied to MEG and EEG data collected during a range of sensory, motor, and …