  Neural recordings are nonstationary time series, i.e. their properties
typically change over time. Identifying specific changes, e.g. those induced by
a learning task, can shed light on the underlying neural processes. However,
such changes of interest are often masked by strong unrelated changes, which
can be of physiological origin or due to measurement artifacts. We propose a
novel algorithm for disentangling such different causes of non-stationarity and
in this manner enable better neurophysiological interpretation for a wider set
of experimental paradigms. A key ingredient is the repeated application of
Stationary Subspace Analysis (SSA) using different temporal scales. The
usefulness of our explorative approach is demonstrated in simulations, theory
and EEG experiments with 80 Brain-Computer-Interfacing (BCI) subjects.
