  As a lossy compression framework, compressed sensing has drawn much attention
in wireless telemonitoring of biosignals due to its ability to reduce energy
consumption and make possible the design of low-power devices. However, the
non-sparseness of biosignals presents a major challenge to compressed sensing.
This study proposes and evaluates a spatio-temporal sparse Bayesian learning
algorithm, which has the desired ability to recover such non-sparse biosignals.
It exploits both temporal correlation in each individual biosignal and
inter-channel correlation among biosignals from different channels. The
proposed algorithm was used for compressed sensing of multichannel
electroencephalographic (EEG) signals for estimating vehicle drivers'
drowsiness. Results showed that the drowsiness estimation was almost unaffected
even if raw EEG signals (containing various artifacts) were compressed by 90%.
