@article{7391dbed6f7546218ff52c77cafaf176,
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}",
note = "Funding Information: Funding: This work is part of the Taiwan Integrated Database for Intelligent Sleep (TIDIS) project support by Ministry of Science and Technology 109-2119-M-002-014-, NCTS Taiwan. CL is supported by the Swiss National Science Foundation (PZ00P3_179795). Publisher Copyright: {\textcopyright} 2020 by the authors. Licensee MDPI, Basel, Switzerland.",
year = "2020",
month = apr,
doi = "10.3390/s20072024",
language = "English",
volume = "20",
journal = "Sensors (Switzerland)",
issn = "1424-8220",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "7",
}