An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System
Joint Authors
Djelouat, Hamza
Baali, Hamza
Amira, Abbes
Bensaali, Faycal
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-11-29
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Djelouat, Hamza& Baali, Hamza& Amira, Abbes& Bensaali, Faycal. 2017. An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System. Wireless Communications and Mobile Computing،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1206388
Modern Language Association (MLA)
Djelouat, Hamza…[et al.]. An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System. Wireless Communications and Mobile Computing No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1206388
American Medical Association (AMA)
Djelouat, Hamza& Baali, Hamza& Amira, Abbes& Bensaali, Faycal. An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System. Wireless Communications and Mobile Computing. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1206388
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1206388