  This paper proposes a strategy to handle missing data for the classification
of electroencephalograms using covariance matrices. It relies on the
observed-data likelihood within an expectation-maximization algorithm. This
approach is compared to two existing state-of-the-art methods: (i) covariance
matrices computed with imputed data; (ii) Riemannian averages of partially
observed covariance matrix. All approaches are combined with the minimum
distance to Riemannian mean classifier and applied to a classification task of
two widely known paradigms of brain-computer interfaces. In addition to be
applicable for a wider range of missing data scenarios, the proposed strategy
generally performs better than other methods on the considered real EEG data.
