Still another aspect of self-adaptation is the need for online calibration of the decoding module (which translates EEG activity into external actions) to cope with the inherent non-stationarity of EEG signals. Recently, a number of papers have studied how EEG signals change during BCI sessions (Shenoy et al., 2006; Sugiyama et al., 2007; Vidaurre et al., 2008; von Bünau et al., 2009). This non-stationarity can be addressed in three different ways. First, by rejecting the variation of the signals and retaining the stationary part as in Kawanabe et al. (2009) and von Bünau et al. (2009). In these works, different methods to design robust BCI systems against non-stationarities are described. Second, by choosing features from the EEG that carry discriminative information and, more importantly, that are stable over time (Galán et al., 2007, 2008). Third, by applying adaptation techniques. This adaptation can, as well, be carried out at different modules of the BCI: in the feature extraction (for example with the use of adaptive autoregressive coefficients or time domain parameters, Schlögl, 2000; Vidaurre et al., 2009) in the spatial filtering (Zhang et al., 2007; Vidaurre and Blankertz, 2010) or at the classifier side. Adaptation of any of the modules can be done in a supervised way (when the task to perform is known beforehand) or in an unsupervised manner (no class labels are used to adapt the system). Although not very common, supervised adaptation of the classifier has been explored in several studies (Millán, 2004; Buttfield et al., 2006; Shenoy et al., 2006; Vidaurre et al., 2006; Millán et al., 2007). Recently, some groups have also performed unsupervised adaptation of the features (Schlögl, 2000; Vidaurre et al., 2009) and of the classifier. Unsupervised classifier adaptation has also been applied to P300 data (Lu et al., 2009) and to MI data (Blumberg et al., 2007; Sugiyama et al., 2007; Vidaurre et al., 2008).