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Falling asleep is a gradually unfolding process. We investigated the role of various oscillatory activities including sleep spindles and alpha and delta oscillations at sleep onset (SO) by automatically detecting oscillatory events. We used two datasets of healthy young males, eight with four baseline recordings, and eight with a baseline and recovery sleep after 40 h of sustained wakefulness. We analyzed the 2-min interval before SO (stage 2) and the five consecutive 2-min intervals after SO. The incidence of delta/theta events reached its maximum in the first 2-min episode after SO, while the frequency of them was continuously decreasing from stage 1 onwards, continuing over SO and further into deeper sleep. Interestingly, this decrease of the frequencies of the oscillations were not affected by increased sleep pressure, in contrast to the incidence which increased. We observed an increasing number of alpha events after SO, predominantly frontally, with their prevalence varying strongly across individuals. Sleep spindles started to occur after SO, with first an increasing then a decreasing incidence and a continuous decrease in their frequency. Again, the frequency of the spindles was not altered after sleep deprivation. Oscillatory events revealed derivation dependent aspects. However, these regional aspects were not specific of the process of SO but rather reflect a general sleep related phenomenon. No individual traits of SO features (incidence and frequency of oscillations) and their dynamics were observed. Delta/theta events are important features for the analysis of SO in addition to slow waves., (© Sleep Research Society 2019. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.)

Study Objectives: The two-process model posits that sleep is regulated by 2 independent processes, a circadian Process C and a homeostatic Process S. EEG slow-wave activity (SWA) is a marker of NREM sleep intensity and is used as an indicator of sleep homeostasis. So far, parameters of the two-process model have been derived mainly from average data. Our aim was to quantify inter-individual differences., Design: Polysomnographic recordings (analysis of existing data)., Setting: Sound attenuated sleep laboratory., Patients or Participants: Eight healthy young males., Interventions: 40-h sustained wakefulness., Measurements and Results: Process S was modeled by a saturating exponential function during wakefulness and an exponential decline during sleep. Empirical mean SWA (derivation C3A2) per NREM sleep episode at episode midpoint were used for parameter estimation. Parameters were estimated simultaneously by minimizing the mean square error between data and simulations of Process S. This approach was satisfactory for average data and most individual data. We further improved our methodological approach by limiting the time constants to a physiologically meaningful range. This allowed a satisfactory fit also for the one individual whose parameters were beyond a physiological range. The time constants of the buildup of Process S ranged from 14.1 h to 26.4 h and those of the decline from 1.2 h to 2.9 h with similar inter-individual variability of the buildup and decline of Process S., Conclusions: We established a robust method for parameter estimation of Process S on an individual basis.