Riemannian classification of EEG signals with missing values
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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.
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