Blind electromagnetic source separation and localization

A Blind Separation Approach to Electromagnetic Source Localization and Assessment

The aim of this paper is to present a neural-network based algorithm for electromagnetic source localization and assessment. The neural network is trained through an independent-component-analysis learning rule, and provides information about the geometry of the sources and the power of emitted eld.

Neural Blind Separation for Electromagnetic Source Localization and Assessment

In this paper we present a possible approach to electromagnetic source localization using a hybrid blind separation – minimum search inversion algorithm. The total electrical field versus time emitted by the working antennas, located at different and unknown geographical positions, is used to reconstruct each separate contribute via a suitable neural network technique. When the emitted electric...

Explorer Blind source separation to enhance spectral and non - linear features of magnetoencephalogram recordings

Blind phase retrieval and source separation of electromagnetic fields.

Blind source separation of two electromagnetic fields is investigated. The difficulty of this task lies in the fact that only the power, which is the square of the sum of the electromagnetic fields, can be directly measured; the cross term of the electromagnetic fields is inevitable, and a strong correlation occurs in blind deconvolution. However, the relative phase is physically different from...

Convergent Bayesian Formulations of Blind Source Separation and Electromagnetic Source Estimation

We consider two areas of research that have been developing in parallel over the last decade: blind source separation (BSS) and electromagnetic source estimation (ESE). BSS deals with the recovery of source signals when only mixtures of signals can be obtained from an array of detectors and the only prior knowledge consists of some information about the nature of the source signals. On the othe...