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On the Use of KPCA to Extract Artifacts in One-Dimensional Biomedical Signals

dc.contributor.authorTeixeira, Ana
dc.contributor.authorTome, A.
dc.contributor.authorLang, E.
dc.contributor.authorSchachtner, R.
dc.contributor.authorStadlthanner, K.
dc.date.accessioned2023-10-23T10:20:18Z
dc.date.available2023-10-23T10:20:18Z
dc.date.issued2006
dc.description.abstractKernel principal component analysis(KPCA) is a nonlinear projective technique that can be applied to decompose multi-dimensional signals and extract informative features as well as reduce any noise contributions. In this work we extend KPCA to extract and remove artifact-related contributions as well as noise from one-dimensional signal recordings. We introduce an embedding step which transforms the one-dimensional signal into a multi-dimensional vector. The latter is decomposed in feature space to extract artifact related contaminations. We further address the preimage problem and propose an initialization procedure to the fixed-point algorithm which renders it more efficient. Finally we apply KPCA to extract dominant Electrooculogram (EOG) artifacts contaminating Electroencephalogram (EEG) recordings in a frontal channel.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/MLSP.2006.275580pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.26/47391
dc.language.isoengpt_PT
dc.publisher[IEEE]pt_PT
dc.titleOn the Use of KPCA to Extract Artifacts in One-Dimensional Biomedical Signalspt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlace[Maynooth]pt_PT
oaire.citation.endPage390pt_PT
oaire.citation.startPage385pt_PT
oaire.citation.title2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processingpt_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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