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Computational Intelligence and Neuroscience
Volume 2007 (2007), Article ID 75079, 13 pages
http://dx.doi.org/10.1155/2007/75079
Research Article

Removing Ocular Movement Artefacts by a Joint Smoothened Subspace Estimator

1The Medical Image and Signal Processing (MEDISIP) Group, ELIS Department, Faculty of Engineering Sciences (Firw), Ghent University, The Institute for Broadband Technology (IBBT), Sint-Pietersnieuwstraat 41, Ghent 9000, Belgium
2Department of Neurology, The Laboratory for Clinical and Experimental Neurophysiology (LCEN), Ghent University Hospital 10K1, De Pintelaan 185, Ghent 9000, Belgium

Received 18 February 2007; Revised 25 May 2007; Accepted 21 August 2007

Academic Editor: Andrzej Cichocki

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.

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.