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

Research output: Contribution to journalArticlepeer-review

1 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 languageEnglish
JournalSensors (Basel, Switzerland)
Volume20
Issue number7
DOIs
StatePublished - 3 Apr 2020

Keywords

  • EEG
  • EMG
  • scattering transform
  • sleep stage classification

Fingerprint Dive into the research topics of 'Save Muscle Information-Unfiltered EEG Signal Helps Distinguish Sleep Stages'. Together they form a unique fingerprint.

Cite this