Compressed Sensing of Multichannel EEG Signals: The Simultaneous Cosparsity and Low-Rank Optimization.

Abstract

This paper deals with the problems that some EEG signals have no good sparse representation and single-channel processing is not computationally efficient in compressed sensing of multichannel EEG signals. An optimization model with L0 norm and Schatten-0 norm is proposed to enforce cosparsity and low-rank structures in the reconstructed multichannel EEG signals. Both convex relaxation and global consensus optimization with alternating direction method of multipliers are used to compute the optimization model. The performance of multichannel EEG signal reconstruction is improved in term of both accuracy and computational complexity. The proposed method is a better candidate than previous sparse signal recovery methods for compressed sensing of EEG signals. The proposed method enables successful compressed sensing of EEG signals even when the signals have no good sparse representation. Using compressed sensing would much reduce the power consumption of wireless EEG system.