完整後設資料紀錄
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dc.contributor.authorLiu, Gi-Renen_US
dc.contributor.authorLustenberger, Carolineen_US
dc.contributor.authorLo, Yu-Lunen_US
dc.contributor.authorLiu, Wen-Teen_US
dc.contributor.authorSheu, Yuan-Chungen_US
dc.contributor.authorWu, Hau-Tiengen_US
dc.date.accessioned2020-07-01T05:22:12Z-
dc.date.available2020-07-01T05:22:12Z-
dc.date.issued2020-04-01en_US
dc.identifier.urihttp://dx.doi.org/10.3390/s20072024en_US
dc.identifier.urihttp://hdl.handle.net/11536/154615-
dc.description.abstractBased 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.language.isoen_USen_US
dc.subjectEEGen_US
dc.subjectEMGen_US
dc.subjectsleep stage classificationen_US
dc.subjectscattering transformen_US
dc.titleSave Muscle Information-Unfiltered EEG Signal Helps Distinguish Sleep Stagesen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/s20072024en_US
dc.identifier.journalSENSORSen_US
dc.citation.volume20en_US
dc.citation.issue7en_US
dc.citation.spage0en_US
dc.citation.epage0en_US
dc.contributor.department應用數學系zh_TW
dc.contributor.departmentDepartment of Applied Mathematicsen_US
dc.identifier.wosnumberWOS:000537110500220en_US
dc.citation.woscount0en_US
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