Adaptation
The kind of switch mentioned above offers a first level of self-adaptation, in that the user can dynamically choose the best interaction channel at any time. To the best of our knowledge, this is a aspect of BCI that has not been addressed before. A second level of self-adaptation concerns the choice of the EEG phenomena that each user better controls, which can range from evoked potentials like P300 (Farwell and Donchin, 1988; Nijboer et al., 2008) or SSVEP (Sutter, 1992; Gao et al., 2003; Brunner et al., 2010) to spontaneous signals like slow cortical potentials (Birbaumer et al., 1999) and rhythmic activity (Babiloni et al., 2000; Wolpaw et al., 2000; Pfurtscheller and Neuper, 2001; Millán et al., 2002; Blankertz et al., 2007). This necessitates the development of novel training protocols to determine the optimal EEG phenomenon for each user, building upon work on psychological factors in BCI (Neumann and Kübler, 2003; Nijboer et al., 2007).
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).
Regardless whether adaptivity is applied in one or more modules of the BCI, it allows the simultaneous co-adaptation of the BCI to the user and vice versa. A recent study with healthy volunteers (Vidaurre and Blankertz, 2010) who either had no experience or had not been able to control a BCI with sufficient level of control for a communication application (70% of accuracy in a two class system) has shown the advantage of this approach. During the BCI session, of approximately 2 h, some users could develop SMR. This is a big step forward in BCI research, because at least 25–30% of all users are not able to use a BCI with sufficient level of control (Guger et al., 2003). We hypothesize that selection of stable discriminant features and BCI adaptation could facilitate and accelerate subject training. Indeed, these techniques increase the likelihood of providing stable feedback to the user, a necessary condition for people to learn to modulate their brain activity.