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. 2022 Jul 22;16:898300. doi: 10.3389/fnhum.2022.898300

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Copyright © 2022 Jeong, Cho, Lee, Lee, Shin, Kweon, Millán, Müller and Lee.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Overview of training data from modified Sleep-EDF dataset. In Data Set-B, an open dataset was modified to provide large sleep stage data, unlike other competition tracks. (A) Total number of samples according to classes in the training set. For this competition, we selected a total of 50 EEG data from 20 males and 30 females, excluding missing data and subjects outside the range of 25–56 years of age. (B) Spectrogram of DataSample01 based on classes. From top to bottom, the figure represents the average spectrogram for Wake, NREM1, NREM2, NREM3, and NREM4 states (PSD: power spectral density). (C) Hypnogram of DataSample01. (PSD, power spectral density).