  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 the time domain and also non-sparse in
transformed domains (such as the wavelet domain). 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 method to
the CS problem. This study introduces the technique to the telemonitoring of
EEG. Experimental results show that its recovery quality is better than
state-of-the-art CS algorithms, and sufficient for practical use. These results
suggest that BSBL is very promising for telemonitoring of EEG and other
non-sparse physiological signals.
