NONLINEAR PROJECTIVE TECHNIQUES TO EXTRACT ARTIFACTS IN BIOMEDICAL SIGNALS

Biomedical signals are generally contaminated with artifacts and noise. In case the artifacts dominate, the useful sig-nal can easily be extracted with projective subspace tech-niques. 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 ap-plicable. The transformation can be achieved by embedding an observed signal in its delayed coordinates. Using this embedding we propose to cluster the resulting feature vec-tors 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 electroencephalo-gram (EEG) signal recorded in the frontal channel to extract its prominent electrooculogram (EOG) interference. 1