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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.

SUBMITTER: Liu GR 

PROVIDER: S-EPMC7180982 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Save Muscle Information-Unfiltered EEG Signal Helps Distinguish Sleep Stages.

Liu Gi-Ren GR   Lustenberger Caroline C   Lo Yu-Lun YL   Liu Wen-Te WT   Sheu Yuan-Chung YC   Wu Hau-Tieng HT  

Sensors (Basel, Switzerland) 20200403 7


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 a  ...[more]

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