  Combining information from different sources is a common way to improve
classification accuracy in Brain-Computer Interfacing (BCI). For instance, in
small sample settings it is useful to integrate data from other subjects or
sessions in order to improve the estimation quality of the spatial filters or
the classifier. Since data from different subjects may show large variability,
it is crucial to weight the contributions according to importance. Many
multi-subject learning algorithms determine the optimal weighting in a separate
step by using heuristics, however, without ensuring that the selected weights
are optimal with respect to classification. In this work we apply Multiple
Kernel Learning (MKL) to this problem. MKL has been widely used for feature
fusion in computer vision and allows to simultaneously learn the classifier and
the optimal weighting. We compare the MKL method to two baseline approaches and
investigate the reasons for performance improvement.
