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Removing ocular movement artefacts by a joint smoothened subspace estimator.

Phlypo R, Boon P, D'Asseler Y, Lemahieu I - Comput Intell Neurosci (2007)

Bottom Line: 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).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.

View Article: PubMed Central - PubMed

Affiliation: The 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, 9000 Ghent, Belgium. ronald.phlypo@ugent.be

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.

No MeSH data available.


Related in: MedlinePlus

An example fragment of saccades. The saccades are clearly visible at seconds 1, 5, and 8.
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fig9: An example fragment of saccades. The saccades are clearly visible at seconds 1, 5, and 8.

Mentions: Figures 7 and 9 contain two snippets of patient datasets recorded at the Ghent University Hospital. Figure 7 contains clear blinking artefacts at seconds 1, 3, and 7, whereas Figure 9 contains clear saccades at seconds 1, 5, and 8. Both dataframes have been subjected to JSSE of which the obtained results can be seen in Figures 8 and 10, respectively. For clarity, the spectrum of JSSE that accompanies the results in Figure 7 (i.e., the values on the diagonal of obtained at the second step of JSSE, see Section 2.4) are given in Figure 12 and a profile of the pSVD correction is given in Figure 11. The latter shows how many windows were deflated to reconstruct the current 8samples.


Removing ocular movement artefacts by a joint smoothened subspace estimator.

Phlypo R, Boon P, D'Asseler Y, Lemahieu I - Comput Intell Neurosci (2007)

An example fragment of saccades. The saccades are clearly visible at seconds 1, 5, and 8.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2233983&req=5

fig9: An example fragment of saccades. The saccades are clearly visible at seconds 1, 5, and 8.
Mentions: Figures 7 and 9 contain two snippets of patient datasets recorded at the Ghent University Hospital. Figure 7 contains clear blinking artefacts at seconds 1, 3, and 7, whereas Figure 9 contains clear saccades at seconds 1, 5, and 8. Both dataframes have been subjected to JSSE of which the obtained results can be seen in Figures 8 and 10, respectively. For clarity, the spectrum of JSSE that accompanies the results in Figure 7 (i.e., the values on the diagonal of obtained at the second step of JSSE, see Section 2.4) are given in Figure 12 and a profile of the pSVD correction is given in Figure 11. The latter shows how many windows were deflated to reconstruct the current 8samples.

Bottom Line: 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).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.

View Article: PubMed Central - PubMed

Affiliation: The 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, 9000 Ghent, Belgium. ronald.phlypo@ugent.be

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.

No MeSH data available.


Related in: MedlinePlus