Most of the subspace based signal separation methods are applicable when sufficient number of signal mixtures are available which require multichannel recordings. Separation of signal sources from single channel recordings on the other hand is often of very poor quality, if not impossible, since so-called subspaces of the signal components are unknown. 

Singular spectrum analysis (SSA) deals with decomposition of the data into more meaningful subspaces where the desired signal components are characterised. Periodic signals, spikes, and those for which some a priori knowledge is available can be well defined in the eigenspace of the SSA. Some applications of this approach will be explained and a new SSA-based adaptive filter for recovery of periodic physiological signals from their single channel mixtures will be presented.