Save Muscle Information–Unfiltered EEG Signal Helps Distinguish Sleep Stages
dc.contributor.author
Liu, Gi-Ren
dc.contributor.author
Lustenberger, Caroline
dc.contributor.author
Lo, Yu-Lun
dc.contributor.author
Liu, Wen-Te
dc.contributor.author
Sheu, Yuan-Chung
dc.contributor.author
Wu, Hau-Tieng
dc.date.accessioned
2020-04-20T08:35:10Z
dc.date.available
2020-04-17T09:56:16Z
dc.date.available
2020-04-20T08:35:10Z
dc.date.issued
2020-04
dc.identifier.issn
1424-8220
dc.identifier.other
10.3390/s20072024
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/410315
dc.identifier.doi
10.3929/ethz-b-000410315
dc.description.abstract
Based on the well-established biopotential theory, we hypothesize that the high frequency spectral information, like that higher than 100Hz, of the EEG signal recorded in the off-the-shelf EEG sensor contains muscle tone information. We show that an existing automatic sleep stage annotation algorithm can be improved by taking this information into account. This result suggests that if possible, we should sample the EEG signal with a high sampling rate, and preserve as much spectral information as possible.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
MDPI
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
EEG
en_US
dc.subject
EMG
en_US
dc.subject
sleep stage classification
en_US
dc.subject
scattering transform
en_US
dc.title
Save Muscle Information–Unfiltered EEG Signal Helps Distinguish Sleep Stages
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2020-04-03
ethz.journal.title
Sensors
ethz.journal.volume
20
en_US
ethz.journal.issue
7
en_US
ethz.pages.start
2024
en_US
ethz.size
12 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Basel
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02070 - Dep. Gesundheitswiss. und Technologie / Dep. of Health Sciences and Technology::02535 - Institut für Bewegungswiss. und Sport / Institut of Human Movement Sc. and Sport::03963 - Wenderoth, Nicole / Wenderoth, Nicole
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02070 - Dep. Gesundheitswiss. und Technologie / Dep. of Health Sciences and Technology::02535 - Institut für Bewegungswiss. und Sport / Institut of Human Movement Sc. and Sport::03963 - Wenderoth, Nicole / Wenderoth, Nicole
ethz.date.deposited
2020-04-17T09:56:40Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-04-20T08:35:21Z
ethz.rosetta.lastUpdated
2024-02-02T10:46:42Z
ethz.rosetta.versionExported
true
ethz.COinS
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