SWS Brain-Wave Music May Improve the Quality of Sleep: An EEG Study

Dongrui Gao; Siyu Long; Hua Yang; Yibo Cheng; Sijia Guo; Yue Yu; Tiejun Liu; Li Dong; Jing Lu; Dezhong Yao

doi:10.3389/fnins.2020.00067

SWS Brain-Wave Music May Improve the Quality of Sleep: An EEG Study

Front Neurosci. 2020 Feb 11:14:67. doi: 10.3389/fnins.2020.00067. eCollection 2020.

Authors

Dongrui Gao^{1

2

3}, Siyu Long³, Hua Yang⁴, Yibo Cheng³, Sijia Guo³, Yue Yu², Tiejun Liu^{2

3}, Li Dong^{2

3}, Jing Lu^{2

3}, Dezhong Yao^{2

3}

Affiliations

¹ School of Computer Science, Chengdu University of Information Technology, Chengdu, China.
² The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.
³ Center for Information in Biomedicine, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China.
⁴ Department of Composition, Sichuan Conservatory of Music, Chengdu, China.

Abstract

Aim: This study investigated the neural mechanisms of brain-wave music on sleep quality.

Background: Sleep disorders are a common health problem in our society and may result in fatigue, depression, and problems in daytime functioning. Previous studies have shown that brain-wave music generated from electroencephalography (EEG) signals could emotionally affect our nervous system and have positive effects on sleep. However, the neural mechanisms of brain-wave music on the quality of sleep need to be clarified.

Methods: A total of 33 young participants were recruited and randomly divided into three groups. The participants listened to rapid eye movement (REM) brain-wave music (Group 1: 13 subjects), slow-wave sleep (SWS) brain-wave music (Group 2: 11 subjects), or white noise (WN) (Control Group: 9 subjects) for 20 min before bedtime for 6 days. EEG and other physiological signals were recorded by polysomnography.

Results: We found that the sleep efficiency increased in the SWS group but decreased in REM and WN groups. The sleep efficiency in the SWS group was ameliorated [t(10) = -1.943, p = 0.076]. In the EEG power spectral density analysis, the delta power spectral density in the REM group and in the control group increased, while that in the SWS group decreased [F(2,31) = 7.909, p = 0.005]. In the network analysis, the functional connectivity (FC), assessed with Pearson correlation coefficients, showed that the connectivity strength decreased [t(10) = 1.969, p = 0.073] between the left frontal lobe (F3) and left parietal lobe (C3) in the SWS group. In addition, there was a negative correlation between the FC of the left frontal lobe and the left parietal lobe and sleep latency in the SWS group (r = -0.527, p = 0.064).

Conclusion: Slow-wave sleep brain-wave music may have a positive effect on sleep quality, while REM brain-wave music or WN may not have a positive effect. Furthermore, better sleep quality might be caused by a decrease in the power spectral density of the delta band of EEG and an increase in the FC between the left frontal lobe and the left parietal lobe. SWS brain-wave music could be a safe and inexpensive method for clinical use if confirmed by more data.

Keywords: brain-wave music; electroencephalography; neural plasticity; power spectra analysis; sleep.