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

Sergiy Vorobyov, Andrzej Cichocki

Research output: Contribution to journalArticlepeer-review

143 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)293-303
Number of pages11
JournalBiological Cybernetics
Volume86
Issue number4
DOIs
Publication statusPublished - 2002
Externally publishedYes

Fingerprint

Dive into the research topics of 'Blind noise reduction for multisensory signals using ICA and subspace filtering, with application to EEG analysis'. Together they form a unique fingerprint.

Cite this