An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System
<p>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 mo...
محفوظ في:
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| مؤلفون آخرون: | , , |
| منشور في: |
2017
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إضافة وسم
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| _version_ | 1864513563221032960 |
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| author | Hamza Djelouat (14603379) |
| author2 | Hamza Baali (14603380) Abbes Amira (6952001) Faycal Bensaali (12427401) |
| author2_role | author author author |
| author_facet | Hamza Djelouat (14603379) Hamza Baali (14603380) Abbes Amira (6952001) Faycal Bensaali (12427401) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hamza Djelouat (14603379) Hamza Baali (14603380) Abbes Amira (6952001) Faycal Bensaali (12427401) |
| dc.date.none.fl_str_mv | 2017-11-29T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1155/2017/9823684 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/An_Adaptive_Joint_Sparsity_Recovery_for_Compressive_Sensing_Based_EEG_System/22082966 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electronics, sensors and digital hardware Electrical and Electronic Engineering Computer Networks and Communications Information Systems |
| dc.title.none.fl_str_mv | An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>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. </p> <h2>Other information</h2> <p>Published in: Wireless Communications and Mobile Computing<br> License: <a href="http://creativecommons.org/licenses/by/4.0" target="_blank">http://creativecommons.org/licenses/by/4.0</a><br> See article on publisher's website: <a href="http://dx.doi.org/10.1155/2017/9823684" target="_blank">http://dx.doi.org/10.1155/2017/9823684</a> </p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_fd64392865a4e4689e783f4121914de6 |
| identifier_str_mv | 10.1155/2017/9823684 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/22082966 |
| publishDate | 2017 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG SystemHamza Djelouat (14603379)Hamza Baali (14603380)Abbes Amira (6952001)Faycal Bensaali (12427401)EngineeringElectronics, sensors and digital hardwareElectrical and Electronic EngineeringComputer Networks and CommunicationsInformation Systems<p>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. </p> <h2>Other information</h2> <p>Published in: Wireless Communications and Mobile Computing<br> License: <a href="http://creativecommons.org/licenses/by/4.0" target="_blank">http://creativecommons.org/licenses/by/4.0</a><br> See article on publisher's website: <a href="http://dx.doi.org/10.1155/2017/9823684" target="_blank">http://dx.doi.org/10.1155/2017/9823684</a> </p>2017-11-29T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1155/2017/9823684https://figshare.com/articles/journal_contribution/An_Adaptive_Joint_Sparsity_Recovery_for_Compressive_Sensing_Based_EEG_System/22082966CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/220829662017-11-29T03:00:00Z |
| spellingShingle | An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System Hamza Djelouat (14603379) Engineering Electronics, sensors and digital hardware Electrical and Electronic Engineering Computer Networks and Communications Information Systems |
| status_str | publishedVersion |
| title | An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System |
| title_full | An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System |
| title_fullStr | An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System |
| title_full_unstemmed | An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System |
| title_short | An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System |
| title_sort | An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System |
| topic | Engineering Electronics, sensors and digital hardware Electrical and Electronic Engineering Computer Networks and Communications Information Systems |