One major challenge in Brain-Computer Interface (BCI) research is to cope with the inherent nonstationarity of the recorded brain signals caused by changes in the subjects brain processes during an experiment. Online adaptation of the classifier embedded into the BCI is a possible way of tackling this issue. In this chapter we investigate the effect of adaptation on the performance of the classifier embedded in three different BCI systems, all of them based on non-invasive electroencephalogram (EEG) signals. Through this adaptation we aim to keep the classifier constantly tuned to the EEG signals it is receiving in the current session. Although the experimental results reported here show the benefits of online adaptation, some questions need still to be addressed. The chapter ends discussing some of these open issues.