Our paper on compressed sensing of EEG for wireless telemonitoring has been accepted by IEEE Trans. on Biomedical Engineering.
Here is the summary of this paper: (1) EEG在时域不是稀疏的,在其它变换域(比如离散余弦变换域,小波域)也不是稀疏的(如上图中左下子图所示); (2) 对于这种非稀疏信号,我们采用BSBL框架中的算法BSBL-BO来恢复;
(3) 恢复的质量不仅用常规的性能评价指标来衡量,还采用独立分量分析(ICA)分解来衡量。当恢复的信号有较大误差时,分解出来的独立分量会出现失真。我们试验显示,BSBL恢复的信号的质量能保证独立分量不会出现明显的失真。
(4) 小波压缩与压缩传感的客观比较在压缩传感领域常常被有意或无意地忽视。文中我们用客观冷静的观点评价了两者的优劣。
Abstract: 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.