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Article: Compressed sensing of large-scale local field potentials using adaptive sparsity analysis and non-convex optimization

TitleCompressed sensing of large-scale local field potentials using adaptive sparsity analysis and non-convex optimization
Authors
Keywordsanalysis model
compressed sensing
local field potentials
wireless neural recording
Issue Date2021
PublisherInstitute of Physics Publishing. The Journal's web site is located at http://www.iop.org/EJ/journal/JNE
Citation
Journal of Neural Engineering, 2021, v. 18 n. 2, p. article no. 026007 How to Cite?
AbstractObjective. Energy consumption is a critical issue in resource-constrained wireless neural recording applications with limited data bandwidth. Compressed sensing (CS) has emerged as a powerful framework in addressing this issue owing to its highly efficient data compression procedure. In this paper, a CS-based approach termed simultaneous analysis non-convex optimization (SANCO) is proposed for large-scale, multi-channel local field potentials (LFPs) recording. Approach. The SANCO method consists of three parts: (1) the analysis model is adopted to reinforce sparsity of the multi-channel LFPs, therefore overcoming the drawbacks of conventional synthesis models. (2) An optimal continuous order difference matrix is constructed as the analysis operator, enhancing the recovery performance while saving both computational resources and data storage space. (3) A non-convex optimizer that can by efficiently solved with alternating direction method of multipliers is developed for multi-channel LFPs reconstruction. Main results. Experimental results on real datasets reveal that the proposed approach outperforms state-of-the-art CS methods in terms of both recovery quality and computational efficiency. Significance. Energy efficiency of the SANCO make it an ideal candidate for resource-constrained, large scale wireless neural recording. Particularly, the proposed method ensures that the key features of LFPs had little degradation even when data are compressed by 16x, making it very suitable for long term wireless neural recording applications.
Persistent Identifierhttp://hdl.handle.net/10722/306165
ISSN
2023 Impact Factor: 3.7
2023 SCImago Journal Rankings: 1.094
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorSUN, B-
dc.contributor.authorZHANG, H-
dc.contributor.authorZHANG, Y-
dc.contributor.authorWU, Z-
dc.contributor.authorBAO, B-
dc.contributor.authorHu, Y-
dc.contributor.authorLI, T-
dc.date.accessioned2021-10-20T10:19:42Z-
dc.date.available2021-10-20T10:19:42Z-
dc.date.issued2021-
dc.identifier.citationJournal of Neural Engineering, 2021, v. 18 n. 2, p. article no. 026007-
dc.identifier.issn1741-2560-
dc.identifier.urihttp://hdl.handle.net/10722/306165-
dc.description.abstractObjective. Energy consumption is a critical issue in resource-constrained wireless neural recording applications with limited data bandwidth. Compressed sensing (CS) has emerged as a powerful framework in addressing this issue owing to its highly efficient data compression procedure. In this paper, a CS-based approach termed simultaneous analysis non-convex optimization (SANCO) is proposed for large-scale, multi-channel local field potentials (LFPs) recording. Approach. The SANCO method consists of three parts: (1) the analysis model is adopted to reinforce sparsity of the multi-channel LFPs, therefore overcoming the drawbacks of conventional synthesis models. (2) An optimal continuous order difference matrix is constructed as the analysis operator, enhancing the recovery performance while saving both computational resources and data storage space. (3) A non-convex optimizer that can by efficiently solved with alternating direction method of multipliers is developed for multi-channel LFPs reconstruction. Main results. Experimental results on real datasets reveal that the proposed approach outperforms state-of-the-art CS methods in terms of both recovery quality and computational efficiency. Significance. Energy efficiency of the SANCO make it an ideal candidate for resource-constrained, large scale wireless neural recording. Particularly, the proposed method ensures that the key features of LFPs had little degradation even when data are compressed by 16x, making it very suitable for long term wireless neural recording applications.-
dc.languageeng-
dc.publisherInstitute of Physics Publishing. The Journal's web site is located at http://www.iop.org/EJ/journal/JNE-
dc.relation.ispartofJournal of Neural Engineering-
dc.rightsJournal of Neural Engineering. Copyright © Institute of Physics Publishing.-
dc.rightsThis is an author-created, un-copyedited version of an article published in [insert name of journal]. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://dx.doi.org/[insert DOI].-
dc.subjectanalysis model-
dc.subjectcompressed sensing-
dc.subjectlocal field potentials-
dc.subjectwireless neural recording-
dc.titleCompressed sensing of large-scale local field potentials using adaptive sparsity analysis and non-convex optimization-
dc.typeArticle-
dc.identifier.emailHu, Y: yhud@hku.hk-
dc.identifier.authorityHu, Y=rp00432-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1088/1741-2552/abd578-
dc.identifier.pmid33348334-
dc.identifier.scopuseid_2-s2.0-85101687416-
dc.identifier.hkuros328181-
dc.identifier.volume18-
dc.identifier.issue2-
dc.identifier.spagearticle no. 026007-
dc.identifier.epagearticle no. 026007-
dc.identifier.isiWOS:000620965700001-
dc.publisher.placeUnited Kingdom-

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