Stationary Subspace Analysis (SSA) is a blind source separation algorithm which factorizes a multivariate time series into stationary and non-stationary components.
Contents
Introduction
In many settings, the measured time series contains contributions from various underlying sources that cannot be measured directly. For instance, in EEG analysis, the electrodes on the scalp record the activity of a large number of sources located inside the brain. These sources can be stationary or non-stationary, but they are not discernible in the electrode signals, which are a mixture of these sources. SSA allows the separation of the stationary from the non-stationary sources in an observed time series.
According to the SSA model, the observed multivariate time series
where
Given samples from the time series
Identifiability of the solution
The true stationary sources
Applications and extensions
Stationary subspace analysis has been successfully applied to Brain-computer interfacing, computer vision and temporal segmentation. There are variants of the SSA problem that can be solved analytically in closed form, without numerical optimization.