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Wireless Communications and Mobile Computing
Volume 2017, Article ID 9823684, 10 pages
Research Article

An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System

College of Engineering, Qatar University, P.O. Box 2713, Doha, Qatar

Correspondence should be addressed to Hamza Djelouat; aq.ude.uq@tauolejd.azmah

Received 28 July 2017; Revised 14 October 2017; Accepted 2 November 2017; Published 29 November 2017

Academic Editor: Gonzalo Vazquez-Vilar

Copyright © 2017 Hamza Djelouat et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The last decade has witnessed tremendous efforts to shape the Internet of things (IoT) platforms to be well suited for healthcare applications. These platforms are comprised of a network of wireless sensors to monitor several physical and physiological quantities. For instance, long-term monitoring of brain activities using wearable electroencephalogram (EEG) sensors is widely exploited in the clinical diagnosis of epileptic seizures and sleeping disorders. However, the deployment of such platforms is challenged by the high power consumption and system complexity. Energy efficiency can be achieved by exploring efficient compression techniques such as compressive sensing (CS). CS is an emerging theory that enables a compressed acquisition using well-designed sensing matrices. Moreover, system complexity can be optimized by using hardware friendly structured sensing matrices. This paper quantifies the performance of a CS-based multichannel EEG monitoring. In addition, the paper exploits the joint sparsity of multichannel EEG using subspace pursuit (SP) algorithm as well as a designed sparsifying basis in order to improve the reconstruction quality. Furthermore, the paper proposes a modification to the SP algorithm based on an adaptive selection approach to further improve the performance in terms of reconstruction quality, execution time, and the robustness of the recovery process.