Stationary Subspace Analysis (SSA)[1] in statistics is a blind source separation algorithm which factorizes a multivariate time series into stationary and non-stationary components.
For instance, in EEG analysis, the electrodes on the scalp record the activity of a large number of sources located inside the brain.
SSA allows the separation of the stationary from the non-stationary sources in an observed time series.
According to the SSA model,[1] the observed multivariate time series
is assumed to be generated as a linear superposition of stationary sources
, the aim of Stationary Subspace Analysis is to estimate the inverse mixing matrix
separating the stationary from non-stationary sources in the mixture
are identifiable (up to a linear transformation) and the true non-stationary subspace
cannot be identified, because arbitrary contributions from the stationary sources do not change the non-stationary nature of a non-stationary source.
[1] Stationary subspace analysis has been successfully applied to Brain-computer interfacing,[3] computer vision[4] and temporal segmentation.
There are variants of the SSA problem that can be solved analytically in closed form, without numerical optimization.