This thesis derives, tests and applies two linear projection algorithms for
machine learning under non-stationarity. The first finds a direction in a
linear space upon which a data set is maximally non-stationary. The second aims
to robustify two-way classification against non-stationarity. The algorithm is
tested on a key application scenario, namely Brain Computer Interfacing