Removing Ocular Movement Artefacts by a Joint Smoothened Subspace Estimator

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ID: 56051
2007
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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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phlypo2007removingcomputational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Phlypo, Ronald;Boon, Paul;D'Asseler, Yves;Lemahieu, Ignace;
Journal Computational Intelligence and Neuroscience
Year 2007
DOI
DOI not found
URL
http://dx.doi.org/10.1155/2007/75079
Keywords
Medicine Engineering (General). Civil engineering (General) Biotechnology Computer applications to medicine. Medical informatics Mechanical engineering and machinery Science neurosciences. biological psychiatry. neuropsychiatry telecommunication electronics mathematics

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