Compressed sensing and energy-aware independent component analysis for compression of EEG signals

In this paper, we propose the use of compressed sensing (CS) that is preceded by an energy-efficient, cross-product based independent component analysis (ICA) preprocessing method to efficiently compress electroencephalogram (EEG) signals in the context of a wireless body sensor network (WBSN). In WBSNs, the battery life puts a strict energy constraint at each sensor node. By providing a simple, nonadaptive compression scheme at the sensor nodes, CS offers an efficient solution to compress EEG signals in WBSNs. Through simulations, we demonstrate that our method requires less energy than other state-of-the-art methods using ICA, with a reduction in computations that can reach up to 94%. We also demonstrate that for a fixed compression ratio, the achievable reconstruction error is similar to the state-of-the-art method using ICA, and is much lower than when CS is used alone.