Riemannian Classification of EEG Signals with Missing Values.

This paper proposes two strategies to handle missing data for the classification of electroencephalograms using covariance matrices. The first approach estimates the covariance from imputed data with the $k$-nearest neighbors algorithm; the second relies on the observed data by leveraging the observed-data likelihood within an expectation-maximization algorithm. Both approaches are combined with the minimum distance to Riemannian mean classifier and applied to a classification task of event related-potentials, a widely known paradigm of brain-computer interface paradigms. As results show, the proposed strategies perform better than the classification based on observed data and allow to keep a high accuracy even when the missing data ratio increases.