Predictive Modeling of Sleep Slow Oscillation Emergence on the electrode manifold: Toward Personalized Closed-Loop Brain Stimulation.

This study establishes a predictive model for SO occurrence during NREM sleep, providing insights into its organization in successive cycles and at different EEG channels, which is relevant to development of personalized stimulation paradigms. These findings imply that personalized model parameters can be estimated by incorporating SO information in the first sleep cycle, and hence SO timing can be predicted before its occurrence with a probability distribution, enabling more precise targeting of SOs.