Blind noise reduction for multisensory signals using ICA and subspace filtering, with application to EEG analysis.

Abstract:

:In many applications of signal processing, especially in communications and biomedicine, preprocessing is necessary to remove noise from data recorded by multiple sensors. Typically, each sensor or electrode measures the noisy mixture of original source signals. In this paper a noise reduction technique using independent component analysis (ICA) and subspace filtering is presented. In this approach we apply subspace filtering not to the observed raw data but to a demixed version of these data obtained by ICA. Finite impulse response filters are employed whose vectors are parameters estimated based on signal subspace extraction. ICA allows us to filter independent components. After the noise is removed we reconstruct the enhanced independent components to obtain clean original signals; i.e., we project the data to sensor level. Simulations as well as real application results for EEG-signal noise elimination are included to show the validity and effectiveness of the proposed approach.

journal_name

Biol Cybern

journal_title

Biological cybernetics

authors

Vorobyov S,Cichocki A

doi

10.1007/s00422-001-0298-6

keywords:

subject

Has Abstract

pub_date

2002-04-01 00:00:00

pages

293-303

issue

4

eissn

0340-1200

issn

1432-0770

journal_volume

86

pub_type

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