Robust Multichannel EEG Compressed Sensing in the Presence of Mixed Noise

Recently, compressed sensing (CS) has been an effective data compression technique for telemonitoring of multichannel electroencephalogram (EEG) signals through wireless body-area networks. Most of the existing multichannel EEG CS methods ignore the noise or only consider the Gaussian noise. However, there are also some other types of noise, such as heavy-tailed impulsive noise. In this paper, to overcome the above mentioned problems, we propose a novel multichannel EEG CS method based on sparse and low rank representation in the presence of mixed noise (SLRMN), which can take both Gaussian noise and impulsive noise into consideration. Moreover, we develop the alternative direction method of multipliers (ADMM) to solve the proposed SLRMN. The experimental results demonstrate the advantage of the proposed SLRMN over state-of-the-art multichannel EEG CS methods in the presence of mixed noise.