Greedy KPCA in biomedical signal processing

Biomedical signals are generally contaminated with artifacts and noise. In case 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 multi-dimensional signal vectors for the latter techniques to be applicable. In this work we propose the application of a greedy kernel Principal Component Analysis(KPCA) which allows to decompose the multidimensional vectors into components, and we will show that the one related with the largest eigenvalues correspond to an high-amplitude artifact that can be subtracted from the original.