An accurate home sleep study to assess electroencephalography (EEG)-based sleep stages and EEG power would be advantageous for both clinical and research purposes, such as for longitudinal studies measuring changes in sleep stages over time. The purpose of this study was to compare sleep scoring of a single-channel EEG recorded simultaneously on the forehead against attended polysomnography. Participants were recruited from both a clinical sleep centre and a longitudinal research study investigating cognitively normal ageing and Alzheimer's disease. Analysis for overall epoch-by-epoch agreement found strong and substantial agreement between the single-channel EEG compared to polysomnography (κ = 0.67). Slow wave activity in the frontal regions was also similar when comparing the single-channel EEG device to polysomnography. As expected, Stage N1 showed poor agreement (sensitivity 0.2) due to lack of occipital electrodes. Other sleep parameters, such as sleep latency and rapid eye movement (REM) onset latency, had decreased agreement. Participants with disrupted sleep consolidation, such as from obstructive sleep apnea, also had poor agreement. We suspect that disagreement in sleep parameters between the single-channel EEG and polysomnography is due partially to altered waveform morphology and/or poorer signal quality in the single-channel derivation. Our results show that single-channel EEG provides comparable results to polysomnography in assessing REM, combined Stages N2 and N3 sleep and several other parameters, including frontal slow wave activity. The data establish that single-channel EEG can be a useful research tool.
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http://dx.doi.org/10.1111/jsr.12417 | DOI Listing |
Physiol Meas
January 2025
Department of Electrical Engineering, KU Leuven, Kasteelpark Arenberg 10 postbus 2440 3001 LEUVEN Belgium, Leuven, Flanders, 3000, BELGIUM.
Sleep staging is a crucial task in clinical and research contexts for diagnosing and understanding sleep disorders. This work introduces PhysioEx, a Python library designed to support the analysis of sleep stages using deep learning and Explainable AI (XAI). Approach: PhysioEx provides an extensible and modular API for standardizing and automating the sleep staging pipeline, covering data preprocessing, model training, testing, fine-tuning, and explainability.
View Article and Find Full Text PDFRes Dev Disabil
January 2025
School of Psychological Science, Oregon State University, 2950 SW Jefferson Way, Corvallis, OR 97331, USA. Electronic address:
Introduction: Moebius syndrome is a rare congenital disorder with frequent anecdotal reports of sleep disturbances not sufficiently categorized by prior literature. The present mixed-methods, two-phase study aimed to characterize the sleep health and symptoms of a cohort of adults and children (via parent proxies) with Moebius syndrome.
Methods: In Phase 1, participants were 46 adults with Moebius Syndrome (M=33.
J Neural Eng
January 2025
Department of Electrical and Computer Engineering, Stony Brook University, 211 Light Engineering, Stony Brook University, Stony Brook, NY 11794, Stony Brook, New York, 11794, UNITED STATES.
Objective Key challenges in upper limb prosthetics include a lack of effective control systems, the often invasive surgical requirements of brain-controlled limbs, and prohibitive costs. As a result, disuse rates remain high despite potential for increased quality of life. To address these concerns, this project developed a low cost, noninvasive transhumeral neuroprosthesis-operated via a combination of electroencephalography (EEG) signals and head gestures.
View Article and Find Full Text PDFEntropy (Basel)
January 2025
School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
Emotion recognition is an advanced technology for understanding human behavior and psychological states, with extensive applications for mental health monitoring, human-computer interaction, and affective computing. Based on electroencephalography (EEG), the biomedical signals naturally generated by the brain, this work proposes a resource-efficient multi-entropy fusion method for classifying emotional states. First, Discrete Wavelet Transform (DWT) is applied to extract five brain rhythms, i.
View Article and Find Full Text PDFFront Hum Neurosci
January 2025
Student Affairs Office, Guilin Normal College, Guilin, China.
Introduction: Attention classification based on EEG signals is crucial for brain-computer interface (BCI) applications. However, noise interference and real-time signal fluctuations hinder accuracy, especially in portable single-channel devices. This study proposes a robust Kalman filtering method combined with a norm-constrained extreme learning machine (ELM) to address these challenges.
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