Computational Intelligence and Neuroscience

Computational Intelligence and Neuroscience / 2007 / Article
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EEG/MEG Signal Processing

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Research Article | Open Access

Volume 2007 |Article ID 075079 | https://doi.org/10.1155/2007/75079

Ronald Phlypo, Paul Boon, Yves D'Asseler, Ignace Lemahieu, "Removing Ocular Movement Artefacts by a Joint Smoothened Subspace Estimator", Computational Intelligence and Neuroscience, vol. 2007, Article ID 075079, 13 pages, 2007. https://doi.org/10.1155/2007/75079

Removing Ocular Movement Artefacts by a Joint Smoothened Subspace Estimator

Academic Editor: Andrzej Cichocki
Received18 Feb 2007
Revised25 May 2007
Accepted21 Aug 2007
Published05 Dec 2007

Abstract

To cope with the severe masking of background cerebral activity in the electroencephalogram (EEG) by ocular movement artefacts, we present a method which combines lower-order, short-term and higher-order, long-term statistics. The joint smoothened subspace estimator (JSSE) calculates the joint information in both statistical models, subject to the constraint that the resulting estimated source should be sufficiently smooth in the time domain (i.e., has a large autocorrelation or self predictive power). It is shown that the JSSE is able to estimate a component from simulated data that is superior with respect to methodological artefact suppression to those of FastICA, SOBI, pSVD, or JADE/COM1 algorithms used for blind source separation (BSS). Interference and distortion suppression are of comparable order when compared with the above-mentioned methods. Results on patient data demonstrate that the method is able to suppress blinking and saccade artefacts in a fully automated way.

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Copyright © 2007 Ronald Phlypo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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