A multi-class EEG-based BCI classification using multivariate empirical mode decomposition based filtering and Riemannian geometry.

•A subject specific MEMD based filtering is proposed to classify MI EEG signals.•The mean frequency is used to filter the MIMFs to obtain enhanced EEG signals.•The sample covariance matrix feature is extracted from these enhanced EEG signals.•This feature is classified using Riemannian geometry.•Our combined approach outperformed other recognition methods.