  Goal: 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 multi-channel EEG signals. Methods: An
optimization model with L0 norm and Schatten-0 norm is proposed to enforce
cosparsity and low rank structures in the reconstructed multi-channel EEG
signals. Both convex relaxation and global consensus optimization with
alternating direction method of multipliers are used to compute the
optimization model. Results: The performance of multi-channel EEG signal
reconstruction is improved in term of both accuracy and computational
complexity. Conclusion: The proposed method is a better candidate than previous
sparse signal recovery methods for compressed sensing of EEG signals.
Significance: 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.
