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On the Use of KPCA to Extract Artifacts in One-Dimensional Biomedical Signals
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| 5.83 MB | Adobe PDF |
Orientador(es)
Resumo(s)
Kernel 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.
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Contexto Educativo
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Editora
[IEEE]