Compressed Sensing of EEG for Wireless Telemonitoring with Low Energy Consumption and Inexpensive Hardware

Telemonitoring of electroencephalogram (EEG) through wireless body-area networks is an evolving direction in personalized medicine. Among various constraints in designing such a system, three important constraints are energy consumption, data compression, and device cost. Conventional data compression methodologies, although effective in data compression, consumes significant energy and cannot reduce device cost. Compressed sensing (CS), as an emerging data compression methodology, is promising in catering to these constraints. However, EEG is non-sparse in time domain and also non-sparse in transformed domains (such as wavelet domains). Therefore, it is extremely difficult for current CS algorithms to recover EEG with the quality that satisfies the requirements of clinical diagnosis and engineering applications. Recently, block Sparse Bayesian Learning (bSBL) was proposed as a new CS framework. This study introduces the technique to telemonitoring of EEG. Experimental results show that its recovery quality is better than other typical CS algorithms, and high enough to be of practical use. These results suggest that bSBL can be successfully used in telemonitoring of EEG and other non-sparse physiological signals.