  In the Machine Learning (ML) literature, a well-known problem is the Dataset
Shift problem where, differently from the ML standard hypothesis, the data in
the training and test sets can follow different probability distributions,
leading ML systems toward poor generalisation performances. This problem is
intensely felt in the Brain-Computer Interface (BCI) context, where bio-signals
as Electroencephalographic (EEG) are often used. In fact, EEG signals are
highly non-stationary both over time and between different subjects. To
overcome this problem, several proposed solutions are based on recent transfer
learning approaches such as Domain Adaption (DA). In several cases, however,
the actual causes of the improvements remain ambiguous. This paper focuses on
the impact of data normalisation, or standardisation strategies applied
together with DA methods. In particular, using \textit{SEED}, \textit{DEAP},
and \textit{BCI Competition IV 2a} EEG datasets, we experimentally evaluated
the impact of different normalization strategies applied with and without
several well-known DA methods, comparing the obtained performances. It results
that the choice of the normalisation strategy plays a key role on the
classifier performances in DA scenarios, and interestingly, in several cases,
the use of only an appropriate normalisation schema outperforms the DA
technique.
