A Kronecker Compressed Sensing formulation for energy efficient EEG sensing

Abstract

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.

Sparse EEG compressive sensing for web-enabled person identification

“…Based on web-enabled sensing architecture such as body area networks, Zhilin proposed a promising compressive algorithm named block sparse Bayesian learning (BSBL) for telemonitoring of EEG [18]. Meanwhile, Ankita and Simon realized the Kronecker based and independent component analysis (ICA) based sparse EEG compressive sensing scheme respectively, to reduce the energy cost of web-enabled sensing systems [19,20]. Furthermore, some researchers focused on realizing compressive sensing in wearable devices.…”