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 Cybernjournal_title
Biological cyberneticsauthors
Vorobyov S,Cichocki Adoi
10.1007/s00422-001-0298-6keywords:
subject
Has Abstractpub_date
2002-04-01 00:00:00pages
293-303issue
4eissn
0340-1200issn
1432-0770journal_volume
86pub_type
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