  Similar to most of the real world data, the ubiquitous presence of
non-stationarities in the EEG signals significantly perturb the feature
distribution thus deteriorating the performance of Brain Computer Interface. In
this letter, a novel method is proposed based on Joint Approximate
Diagonalization (JAD) to optimize stationarity for multiclass motor imagery
Brain Computer Interface (BCI) in an information theoretic framework.
Specifically, in the proposed method, we estimate the subspace which optimizes
the discriminability between the classes and simultaneously preserve
stationarity within the motor imagery classes. We determine the subspace for
the proposed approach through optimization using gradient descent on an
orthogonal manifold. The performance of the proposed stationarity enforcing
algorithm is compared to that of baseline One-Versus-Rest (OVR)-CSP and JAD on
publicly available BCI competition IV dataset IIa. Results show that an
improvement in average classification accuracies across the subjects over the
baseline algorithms and thus essence of alleviating within session
non-stationarities.
