The analysis and characterization of atrial tachyarrhythmias requires the previous estimation of the atrial activity (AA) free from any ventricular activity and other artefacts. This contribution considers a blind source separation (BSS) model to separate the AA from multilead electrocardiograms (ECGs). Previously proposed BSS methods for AA extraction exploit only the spatial diversity introdu...

This paper describes a method for solving the permutation problem in blind source separation (BSS) by frequencydomain independent component analysis (FD-ICA). FDICA is a well-known method for BSS of convolutive mixtures. However, FD-ICA has a source permutation problem, where estimated source components can become swapped at different frequencies. Many researchers have suggested methods to solv...

A blind source separation algorithm is used to estimate the mixing operator from electromagnetic emission signals through independent component analysis (ICA) technique. The mixing operator contains important information about the sources’ features and about the electromagnetic field propagation phenomena, thus, by properly interpreting the results given by the ICA algorithm a blind source loca...

The paper presents an approach that may enable the separation of the vibrations induced by underground traffic from the vibrations induced by other sources, based on Second Order Blind Identification (SOBI) algorithm. The signals recorded in different locations of an instrumented building are mixed signals from different internal and external vibration sources. The blind source separation algor...

When trying to understand the brain, it is of fundamental importance to analyse (e.g. from EEG/MEG measurements) what parts of the cortex interact with each other in order to infer more accurate models of brain activity. Common techniques like Blind Source Separation (BSS) can estimate brain sources and single out artifacts by using the underlying assumption of source signal independence. Howev...