ArticleSleep advances : a journal of the Sleep Research Society2026
A user's introduction to an algorithmic method to identify space-time profiles of sleep slow oscillations: dataset constraints, case-use examples, and open-source code.
Article in Sleep advances : a journal of the Sleep Research Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Classifying Sleep Slow Oscillations in Low Density EEG.Neuroinformatics · 2026Article
- Assessing sleep, cognition and overnight memory performance in neurotypically developing youth in a children's hospital.Frontiers in sleep · 2026Article
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3 authors.
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Abstract
Studies of sleep slow oscillations (SOs, 0.5-1.5 Hz) have emphasized their importance for cognition and health, and their variable spatial organization. We have introduced a data-driven method to analyze SOs as events that differentiate in their space-time co-emergence on the electrode manifold. This approach has identified properties of SO organization that are relevant to function, and that can change in clinical populations. In this work, we share a software and user manual that will allow the sleep research community to leverage our method directly in their own datasets. The work formalizes which dataset properties are necessary to deploy our method in terms of number of participants (
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