The problem of recovering multi-channel EEG signals from their randomly under-sampled measurements is addressed. The objective is to reduce the energy consumed by sensing, processing and transmission in an EEG wireless body area network. Our work is based on the Blind Compressed Sensing (BCS) framework, however instead of exploiting only the sparsity of the multi-channel ensemble in a learned basis, we also make use of the ensembles’ approximate rank deficiency. Our proposed formulation requires solving new optimization problems. To solve these problems, we derive algorithms based on the Split Bregman approach. The resulting recovery results are considerably better than those of previous techniques, in terms of the quantitative and qualitative evaluations. 
Energy efficient EEG sensing and transmission for wireless body area networks: A blind compressed sensing approach