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Sleep, Neuroscience, EEG and Sensors
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Sleep, Neuroscience, EEG and Sensors

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2663

Special Issue Editor

Dr. Gary Garcia-Molina

E-Mail Website
Guest Editor
1. Sleep Number Labs, San Jose, CA 95113, USA
2. Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 32556, USA
Interests: EEG signal processing; neuroimaging; EEG; biometrics; authentication; sleep; electroencephalography

Special Issue Information

Dear Colleagues,

Among devices related to polysomnography, electroencephalograms (EEGs) possess unique abilities to exquisitely reveal the macro- and micro-structural patterns of sleep.

Several algorithms have recently emerged that leverage a single EEG signal to perform automatic sleep staging with an accuracy close to that of the agreement between two expert sleep technicians performing sleep staging on a full set of PSG signals.

At the micro-structural level, essential markers of the transition from wakefulness to sleep, such as theta waves and slow eye movements, manifest in EEGs. The hallmarks of NREM sleep, namely, slow waves and spindles, are defined using EEG reads. Periods of phasic and tonic REM sleep can be identified on the same basis.

Fundamental and applied sleep research are supported via the analysis of EEG signals. Several lines of research have focused on developing more convenient EEG monitoring devices that are comfortable enough to be used during sleep. Closed-loop systems to enhance sleep quality, depending on real-time interpretation of sleep EEG reads, have been proposed and implemented, demonstrating successful outcomes during the subsequent period of wakefulness.

This Issue of Sensors aims to present the latest progress in sleep EEG research. Some of the topics considered in this Special Issue are listed below:

  • Unobtrusive EEG sensing for sleep;
  • EEG electrode technology and comfort;
  • Algorithms for sleep EEG interpretation.

Closed-loop sleep EEG-based systems for sleep enhancement and daytime outcome improvement;

  • Falling asleep facilitation;
  • Sleep quality enhancement.

Dr. Gary Garcia-Molina
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sleep EEG
  • unobtrusive sensing
  • closed-loop
  • falling and staying asleep
  • slow wave enhancement
  • AI applied to sleep EEG

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Published Papers (3 papers)

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Research

19 pages, 4944 KiB  
Article
Altered Muscle–Brain Connectivity During Left and Right Biceps Brachii Isometric Contraction Following Sleep Deprivation: Insights from PLV and PDC
by Puyan Chi, Yun Bai, Weiping Du, Xin Wei, Bin Liu, Shanguang Zhao, Hongke Jiang, Aiping Chi and Mingrui Shao
Sensors 2025, 25(7), 2162; https://doi.org/10.3390/s25072162 - 28 Mar 2025
Viewed by 193
Abstract
Insufficient sleep causes muscle fatigue, impacting performance. The mechanism of brain–muscle signaling remains uncertain. In this study, we examined the impact of sleep deprivation on muscle endurance during isometric contractions and explored the changes in brain–muscle connectivity. Methods: The research involved 35 right-handed [...] Read more.
Insufficient sleep causes muscle fatigue, impacting performance. The mechanism of brain–muscle signaling remains uncertain. In this study, we examined the impact of sleep deprivation on muscle endurance during isometric contractions and explored the changes in brain–muscle connectivity. Methods: The research involved 35 right-handed male participants who took part in an exercise test that included isometric contractions of the left and right biceps in both sleep-deprived and well-rested states. Muscle contraction duration and electroencephalogram (EEG) and electromyography (EMG) signals were recorded. Functional connectivity between brain regions was assessed using the phase locking value (PLV), while partial directed coherence (PDC) was used to analyze signal directionality between motor centers and muscles. Results: The connectivity strength between Brodmann areas (BAs) 1-5 and the right BA6, 8 regions was significantly decreased in the isometric contractions after sleep deprivation. Insufficient sleep enhanced the PDC signals from the motor center of the right brain to the left biceps, and it decreased the PDC signals from both biceps to their opposite motor centers. Conclusions: Sleep deprivation shortened muscle isometric contraction duration by affecting the interaction between the somatosensory motor cortex and the right premotor cortex, reducing biceps feedback signal connectivity to the contralateral motor center in the brain. Full article
(This article belongs to the Special Issue Sleep, Neuroscience, EEG and Sensors)
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23 pages, 7090 KiB  
Article
Model-Based Electroencephalogram Instantaneous Frequency Tracking: Application in Automated Sleep–Wake Stage Classification
by Masoud Nateghi, Mahdi Rahbar Alam, Hossein Amiri, Samaneh Nasiri and Reza Sameni
Sensors 2024, 24(24), 7881; https://doi.org/10.3390/s24247881 - 10 Dec 2024
Viewed by 1025
Abstract
Understanding sleep stages is crucial for diagnosing sleep disorders, developing treatments, and studying sleep’s impact on overall health. With the growing availability of affordable brain monitoring devices, the volume of collected brain data has increased significantly. However, analyzing these data, particularly when using [...] Read more.
Understanding sleep stages is crucial for diagnosing sleep disorders, developing treatments, and studying sleep’s impact on overall health. With the growing availability of affordable brain monitoring devices, the volume of collected brain data has increased significantly. However, analyzing these data, particularly when using the gold standard multi-lead electroencephalogram (EEG), remains resource-intensive and time-consuming. To address this challenge, automated brain monitoring has emerged as a crucial solution for cost-effective and efficient EEG data analysis. A critical component of sleep analysis is detecting transitions between wakefulness and sleep states. These transitions offer valuable insights into sleep quality and quantity, essential for diagnosing sleep disorders, designing effective interventions, enhancing overall health and well-being, and studying sleep’s effects on cognitive function, mood, and physical performance. This study presents a novel EEG feature extraction pipeline for the accurate classification of various wake and sleep stages. We propose a noise-robust model-based Kalman filtering (KF) approach to track changes in a time-varying auto-regressive model (TVAR) applied to EEG data during different wake and sleep stages. Our approach involves extracting features, including instantaneous frequency and instantaneous power from EEG, and implementing a two-step classifier for sleep staging. The first step classifies data into wake, REM, and non-REM categories, while the second step further classifies non-REM data into N1, N2, and N3 stages. Evaluation on the extended Sleep-EDF dataset (Sleep-EDFx), with 153 EEG recordings from 78 subjects, demonstrated compelling results with classifiers including Logistic Regression, Support Vector Machines, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM). The best performance was achieved with the LGBM and XGBoost classifiers, yielding an overall accuracy of over 77%, a macro-averaged F1 score of 0.69, and a Cohen’s kappa of 0.68, highlighting the efficacy of the proposed method with a remarkably compact and interpretable feature set. Full article
(This article belongs to the Special Issue Sleep, Neuroscience, EEG and Sensors)
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9 pages, 216 KiB  
Article
Activity-Based Prospective Memory in Insomniacs
by Miranda Occhionero, Lorenzo Tonetti, Federica Giudetti and Vincenzo Natale
Sensors 2024, 24(11), 3612; https://doi.org/10.3390/s24113612 - 3 Jun 2024
Cited by 1 | Viewed by 729
Abstract
Objective: To investigate the activity-based prospective memory performance in patients with insomnia, divided, on the basis of actigraphic evaluation, into sleep onset, maintenance, mixed and negative misperception insomnia. Methods: A total of 153 patients with insomnia (I, 83 females, mean age + SD [...] Read more.
Objective: To investigate the activity-based prospective memory performance in patients with insomnia, divided, on the basis of actigraphic evaluation, into sleep onset, maintenance, mixed and negative misperception insomnia. Methods: A total of 153 patients with insomnia (I, 83 females, mean age + SD = 41.37 + 16.19 years) and 121 healthy controls (HC, 78 females, mean age + SD = 36.99 + 14.91 years) wore an actigraph for one week. Insomnia was classified into sleep onset insomnia (SOI), maintenance insomnia (MaI), mixed insomnia (MixI) and negative misperception insomnia (NMI). To study their activity-based prospective memory performance, all the participants were required to push the actigraph event marker button twice, at bedtime (task 1) and at get-up time (task 2). Results: Only patients with maintenance and mixed insomnia had a significantly lower accuracy in the activity-based prospective memory task at get-up time compared with the healthy controls. Conclusion: The results show that maintenance and mixed insomnia involve an impaired activity-based prospective memory performance, while sleep onset and negative misperception insomnia do not seem to be affected. This pattern of results suggests that the fragmentation of sleep may play a role in activity-based prospective memory efficiency at wake-up in the morning. Full article
(This article belongs to the Special Issue Sleep, Neuroscience, EEG and Sensors)
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