Teixeira, AnaTome, A.Lang, E.Schachtner, R.Stadlthanner, K.2023-10-232023-10-232006http://hdl.handle.net/10400.26/47391Kernel 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.engconference object10.1109/MLSP.2006.275580