Benjamin Blankertz, Motoaki Kawanabe, Ryota Tomioka, Friederike Hohlefeld, Klaus-Robert Müller, Vadim Nikulin
Brain-Computer Interfaces can suffer from a large variance of the subject condi- tions within and across sessions. For example vigilance ﬂuctuations in the indi- vidual, variable task involvement, workload etc. alter the characteristics of EEG signals and thus challenge a stable BCI operation. In the present work we aim to deﬁne features based on a variant of the common spatial patterns (CSP) algorithm that are constructed invariant with respect to such nonstationarities. We enforce invariance properties by adding terms to the denominator of a Rayleigh coefﬁcient representation of CSP such as disturbance covariance matrices from ﬂuctuations in visual processing. In this manner physiological prior knowledge can be used to shape the classiﬁcation engine for BCI. As a proof of concept we present a BCI classiﬁer that is robust to changes in the level of parietal a -activity. In other words, the EEG decoding still works when there are lapses in vigilance.