Quantitative Comparison of EEG Compressed Sensing using Gabor and K-SVD Dictionaries

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aut.relation.conferenceIEEE International Conference on Digital Signal Processing 2018en_NZ
aut.researcherLi, Xuejun
dc.contributor.authorPhuong, Den_NZ
dc.contributor.authorLi, XJen_NZ
dc.contributor.authorGriffin, Aen_NZ
dc.date.accessioned2018-11-26T03:52:12Z
dc.date.available2018-11-26T03:52:12Z
dc.date.copyright2018-11-20en_NZ
dc.date.issued2018-11-20en_NZ
dc.description.abstractWith the fast development of wearable healthcare systems, compressed sensing (CS) has been proposed to be applied in electroencephalogram (EEG) acquisition. For CS, it is desired to build the best-fit dictionary in order to achieve good reconstruction accuracy. While most of existing works focused on static dictionaries such as Gabor, Fourier and wavelets, the dynamic nature of EEG signals motivates us to study learned dictionaries, which are supposed to provide better reconstruction accuracy and lower computation cost. In this paper, we provide the quantitative performance comparison of EEG CS using two different types of dictionaries, i.e., the well-known Gabor dictionaries versus K-SVD learned dictionaries. The performance comparison utilizes the well-established database of scalp EEG from Physiobank, which allows researchers in this field to compare their work with ours. In addition, it also attempts to inspire the systematic study of dictionary learning in EEG CS.
dc.identifier.citationThe 23rd International Conference on Digital Signal Processing (DSP 2018), 19-21 November 2018, Shanghai, China.
dc.identifier.urihttps://hdl.handle.net/10292/12072
dc.publisherIEEE Circuits and Systems, Singapore Chapter
dc.relation.urihttp://dsp2018.org/download/DSP%202018%20Advanced%20Final%20Program.xlsx
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in (see Citation). The original publication is available at (see Publisher's Version).
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectElectroencephalogram signal; Compressed sensing; Compression ratio; Gabor; K-SVD; Dictionary learning
dc.titleQuantitative Comparison of EEG Compressed Sensing using Gabor and K-SVD Dictionariesen_NZ
dc.typeConference Contribution
pubs.elements-id343455
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Design & Creative Technologies
pubs.organisational-data/AUT/Design & Creative Technologies/Engineering, Computer & Mathematical Sciences
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS
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