Save muscle information–unfiltered eeg signal helps distinguish sleep stages

Gi Ren Liu; Caroline Lustenberger; Yu Lun Lo; Wen Te Liu; Yuan Chung Sheu; Hau Tieng Wu

doi:10.3390/s20072024

Save muscle information–unfiltered eeg signal helps distinguish sleep stages

Gi Ren Liu, Caroline Lustenberger, Yu Lun Lo, Wen Te Liu, Yuan Chung Sheu, Hau Tieng Wu^*

^*Corresponding author for this work

School of Traditional Chinese Medicine

Research output: Contribution to journal › Journal Article › peer-review

4 Scopus citations

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.

Original language	English
Article number	2024
Journal	Sensors
Volume	20
Issue number	7
DOIs	https://doi.org/10.3390/s20072024
State	Published - 04 2020

Bibliographical note

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

EEG
EMG
Scattering transform
Sleep stage classification

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10.3390/s20072024

Cite this

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title = "Save muscle information–unfiltered eeg signal helps distinguish sleep stages",

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.",

keywords = "EEG, EMG, Scattering transform, Sleep stage classification",

author = "Liu, {Gi Ren} and Caroline Lustenberger and Lo, {Yu Lun} and Liu, {Wen Te} and Sheu, {Yuan Chung} and Wu, {Hau Tieng}",

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AU - Lustenberger, Caroline

AU - Lo, Yu Lun

AU - Liu, Wen Te

AU - Sheu, Yuan Chung

AU - Wu, Hau Tieng

PY - 2020/4

Y1 - 2020/4

N2 - 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.

AB - 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.

KW - EEG

KW - EMG

KW - Scattering transform

KW - Sleep stage classification

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U2 - 10.3390/s20072024

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M1 - 2024

ER -

Save muscle information–unfiltered eeg signal helps distinguish sleep stages

Abstract

Bibliographical note

Keywords

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