  An interesting case of the well-known Dataset Shift Problem is the
classification of Electroencephalogram (EEG) signals in the context of
Brain-Computer Interface (BCI). The non-stationarity of EEG signals can lead to
poor generalisation performance in BCI classification systems used in different
sessions, also from the same subject. In this paper, we start from the
hypothesis that the Dataset Shift problem can be alleviated by exploiting
suitable eXplainable Artificial Intelligence (XAI) methods to locate and
transform the relevant characteristics of the input for the goal of
classification. In particular, we focus on an experimental analysis of
explanations produced by several XAI methods on an ML system trained on a
typical EEG dataset for emotion recognition. Results show that many relevant
components found by XAI methods are shared across the sessions and can be used
to build a system able to generalise better. However, relevant components of
the input signal also appear to be highly dependent on the input itself.
