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Goal: This paper deals with the problems that some EEG signals have no goodsparse representation and single channel processing is not computationallyefficient in compressed sensing of multi-channel EEG signals. Methods: Anoptimization model with L0 norm and Schatten-0 norm is proposed to enforcecosparsity and low rank structures in the reconstructed multi-channel EEGsignals. Both convex relaxation and global consensus optimization withalternating direction method of multipliers are used to compute theoptimization model. Results: The performance of multi-channel EEG signalreconstruction is improved in term of both accuracy and computationalcomplexity. Conclusion: The proposed method is a better candidate than previoussparse signal recovery methods for compressed sensing of EEG signals.Significance: The proposed method enables successful compressed sensing of EEGsignals even when the signals have no good sparse representation. Usingcompressed sensing would much reduce the power consumption of wireless EEGsystem.

Compressed Sensing of Multi-Channel EEG Signals: The Simultaneous Cosparsity and Low Rank Optimization