Research not for publishing papers, but for fun, for satisfying curiosity, and for revealing the truth.

This blog reports latest progresses in
(1) Signal Processing and Machine Learning for Biomedicine, Neuroimaging, Wearable Healthcare, and Smart-Home
(2) Sparse Signal Recovery and Compressed Sensing of Signals by Exploiting Spatiotemporal Structures
(3) My Works

Sunday, September 23, 2012

Our paper on compressed sensing of EEG has been accepted

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 is not sparse in the time domain and not in transformed domains (e.g. the DCT domain, the wavelet domain).

(2) The BSBL framework is used to recover the non-sparse signals.

(3) The recovery quality is confirmed by independent component analysis decomposition, which is a regular processing procedure in EEG analysis.

(4) Neutral tone on the comparison of compressed sensing vs. wavelet compression is made.

This paper is our another paper on non-sparse signal recovery. It is also the first step of our big project on brain-computer interface (BCI). Although it is just accepted by the journal, the knowledge has been outdated for our lab, since I have a number of more powerful algorithm, which will be submitted soon.

Anyway, here is the paper:

Zhilin Zhang, Tzyy-Ping Jung, Scott Makeig, Bhaskar D. Rao, Compressed Sensing of EEG for Wireless Telemonitoring with Low Energy Consumption and Inexpensive Hardwareto appear in IEEE Trans. on Biomedical Engineering

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.