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Nonlinear projective techniques to extract artifacts in biomedical signals

dc.contributor.authorTeixeira, Ana
dc.contributor.authorTomé, A. M.
dc.contributor.authorStadlthanner, K.
dc.contributor.authorLang, E. W.
dc.date.accessioned2023-10-23T10:16:39Z
dc.date.available2023-10-23T10:16:39Z
dc.date.issued2006
dc.description.abstractBiomedical signals are generally contaminated with artifacts and noise. In case the artifacts dominate, the useful signal can easily be extracted with projective subspace techniques. Then, biomedical signals which often represent one dimensional time series, need to be transformed to multidimensional signal vectors for the latter techniques to be applicable. The transformation can be achieved by embedding an observed signal in its delayed coordinates. Using this embedding we propose to cluster the resulting feature vectors and apply a singular spectrum analysis (SSA) locally in each cluster to recover the undistorted signals. We also compare the reconstructed signals to results obtained with kernel-PCA. Both nonlinear subspace projection techniques are applied to artificial data to demonstrate the suppression of random noise signals as well as to an electroencephalogram (EEG) signal recorded in the frontal channel to extract its prominent electrooculogram (EOG) interference.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.urihttp://hdl.handle.net/10400.26/47390
dc.language.isoengpt_PT
dc.publisher[IEEE]pt_PT
dc.titleNonlinear projective techniques to extract artifacts in biomedical signalspt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlace[Florence]pt_PT
oaire.citation.title14th European Signal Processing Conferencept_PT
person.familyNameTeixeira
person.givenNameAna
person.identifier.ciencia-idD619-A151-8BE2
person.identifier.orcid0000-0002-8120-0148
person.identifier.ridA-3100-2014
person.identifier.scopus-author-id7202385348
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationc1ff686d-c3d3-4658-96c6-a1f62a52777a
relation.isAuthorOfPublication.latestForDiscoveryc1ff686d-c3d3-4658-96c6-a1f62a52777a

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