A modified row-sparse multiple measurement vector recovery algorithm for reconstructing multichannel EEG signals from compressive measurements

•The problem of efficiently recovering multichannel EEG signals from their compressive measurements is addressed.•The algorithm exploits sub-matrices of a given multichannel EEG matrix for which the assumption of row-sparsity is satisfied.•The results show significant improvements of up to 4% in the reconstruction accuracy measured by the normalized mean squared error.•The main limitation of the proposed recovery method is the need for an uncompressed multichannel EEG epoch to calculate the optimum threshold for the inter-channel correlation.•The proposed method can be deployed in wireless body area network based EEG monitoring systems.