Biomedical 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.info:eu-repo/semantics/publishedVersio
