On the use of clustering and local singular spectrum analysis to remove ocular artifacts from electroencephalograms

We present a method based on singular spectrum analysis to remove ocular artifacts (EOG) from an electroencephalogram (EEC). After embedding the EEG signals in a feature space of time-delayed coordinates, feature vectors are clustered and the principal components (PCs) are computed locally within each cluster. Then we assume that the EOG artifact is associated with the PCs belonging to the largest eigenvalues. We incorporate a minimum description length (IMDL) criterion to estimate the number of eigenvectors needed to represent the EOG artifact faithfully. The EOG signal thus extracted is then subtracted from the original EEG signal to obtain the corrected EEG signal we are interested in.