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

Riemannian classification of EEG signals with missing values