  Compensating changes between a subjects' training and testing session in
Brain Computer Interfacing (BCI) is challenging but of great importance for a
robust BCI operation. We show that such changes are very similar between
subjects, thus can be reliably estimated using data from other users and
utilized to construct an invariant feature space. This novel approach to
learning from other subjects aims to reduce the adverse effects of common
non-stationarities, but does not transfer discriminative information. This is
an important conceptual difference to standard multi-subject methods that e.g.
improve the covariance matrix estimation by shrinking it towards the average of
other users or construct a global feature space. These methods do not reduces
the shift between training and test data and may produce poor results when
subjects have very different signal characteristics. In this paper we compare
our approach to two state-of-the-art multi-subject methods on toy data and two
data sets of EEG recordings from subjects performing motor imagery. We show
that it can not only achieve a significant increase in performance, but also
that the extracted change patterns allow for a neurophysiologically meaningful
interpretation.
