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A Kronecker Compressed Sensing formulation for energy efficient EEG sensing

In Wireless Body Area Networks (WBAN) the energy consumption is dominated by sensing, processing and communication. Previous Compressed Sensing (CS) based solutions to EEG tele-monitoring over WBAN's could only reduce the communication cost. In this work, we propose to reduce the sensing and processing energy costs as well, by randomly under-sampling the signal. We formulate a theoretically sound framework based on Kronecker Compressed Sensing (KCS) for recovering signals acquired via random under-sampling. We have shown experimentally that when the signals are acquired via under-sampling, all previous CS based techniques fail; only our proposed formulation succeeds. We have also carried out a discussion on the power savings provided by our method; the analysis indicate significant reduction in energy cost.