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Similar to most of the real world data, the ubiquitous presence ofnon-stationarities in the EEG signals significantly perturb the featuredistribution thus deteriorating the performance of Brain Computer Interface. Inthis letter, a novel method is proposed based on Joint ApproximateDiagonalization (JAD) to optimize stationarity for multiclass motor imageryBrain Computer Interface (BCI) in an information theoretic framework.Specifically, in the proposed method, we estimate the subspace which optimizesthe discriminability between the classes and simultaneously preservestationarity within the motor imagery classes. We determine the subspace forthe proposed approach through optimization using gradient descent on anorthogonal manifold. The performance of the proposed stationarity enforcingalgorithm is compared to that of baseline One-Versus-Rest (OVR)-CSP and JAD onpublicly available BCI competition IV dataset IIa. Results show that animprovement in average classification accuracies across the subjects over thebaseline algorithms and thus essence of alleviating within sessionnon-stationarities.

Divergence Framework for EEG based Multiclass Motor Imagery Brain Computer Interface