Deciphering Sleep Networks: Integrating EEG Complexity with Sleep Stage Identification

Sleep is a vital physiological process involving dynamic transitions across distinct stages, each characterized by specific patterns of brain activity. Electroencephalography (EEG) remains the gold standard for studying sleep architecture, offering unparalleled temporal resolution of neural oscillations. However, conventional sleep stage scoring relies on predefined visual criteria and may not fully capture the complexity of neural interactions underlying different sleep states. Recent advancements have introduced measures of EEG complexity—such as entropy, fractal analysis, and machine learning techniques—opening new avenues for more nuanced and data-driven approaches to understanding sleep networks.
The primary objective of this Research Topic is to advance our understanding of how EEG complexity metrics can enrich the identification, characterization and modeling of sleep stages, thereby offering deeper insights into the neural networks that regulate sleep. Traditional scoring systems, while clinically useful, often overlook subtle dynamics and individual variability inherent in neural signals. By integrating methods that quantify EEG complexity, researchers may reveal hidden features that distinguish sleep stages, track neurophysiological changes across the lifespan, and improve diagnostic precision in sleep-related disorders. We aim to compile research that not only pushes the boundaries of analytic methodologies but also translates these advances into practical tools for sleep medicine and neuroscience. Through this collective effort, we hope to foster innovations that enhance both fundamental knowledge and clinical applications in sleep research.
This Research Topic invites contributions that integrate advanced EEG complexity analysis and modeling with sleep stages identification to unravel the neural networks governing sleep. We encourage submission of original research articles, reviews, mini-reviews, and methodological perspectives on:
o Novel approaches to quantifying EEG complexity during various sleep stages
o Integration of complexity metrics with traditional sleep scoring
o Modeling sleep processes on different temporal scales relating it with cardiac and respiratory systems
o Machine learning and computational models for automated sleep stage classification
o Correlations between EEG complexity, sleep functions, and health outcomes
o Comparative studies across populations or patient cohorts
o Validation of complexity-based measures against clinical gold standards