Commercial sleep devices and mobile-phone applications for scoring sleep are gaining ground. In order to provide reliable information about the quantity and/or quality of sleep, their performance needs to be assessed against the current gold standard, i.e., polysomnography (PSG; measuring brain, eye, and muscle activity). Here, we assessed some commercially available sleep trackers, namely an activity tracker; Mi band (Xiaomi, Beijing, China), a scientific actigraph: Motionwatch 8 (CamNTech, Cambridge, UK), and a much-used mobile phone application: Sleep Cycle (Northcube, Gothenburg, Sweden). We recorded 27 nights in healthy sleepers using PSG and these devices and compared the results. Surprisingly, all devices had poor agreement with the PSG gold standard. Sleep parameter comparisons revealed that, specifically, the Mi band and the Sleep Cycle application had difficulties in detecting wake periods which negatively affected their total sleep time and sleep-efficiency estimations. However, all 3 devices were good in detecting the most basic parameter, the actual time in bed. In summary, our results suggest that, to date, the available sleep trackers do not provide meaningful sleep analysis but may be interesting for simply tracking time in bed. A much closer interaction with the scientific field seems necessary if reliable information shall be derived from such devices in the future.

The brain continues to respond selectively to environmental stimuli during sleep. However, the functional role of such responses, and whether they reflect information processing or rather sensory inhibition, is not fully understood. Here, we present 17 human sleepers (14 females) with their own name and two unfamiliar first names, spoken by either a familiar voice (FV) or an unfamiliar voice (UFV), while recording polysomnography during a full night of sleep. We detect K-complexes, sleep spindles, and microarousals, and assess event-related and frequency responses as well as intertrial phase synchronization to the different stimuli presented during nonrapid eye movement (NREM) sleep. We show that UFVs evoke more K-complexes and microarousals than FVs. When both stimuli evoke a K-complex, we observe larger evoked potentials, more precise time-locking of brain responses in the delta band (1–4 Hz), and stronger activity in the high frequency (>16 Hz) range, in response to UFVs relative to FVs. Crucially, these differences in brain responses disappear completely when no K-complexes are evoked by the auditory stimuli. Our findings highlight discrepancies in brain responses to auditory stimuli based on their relevance to the sleeper and propose a key role for K-complexes in the modulation of sensory processing during sleep. We argue that such content-specific, dynamic reactivity to external sensory information enables the brain to enter a sentinel processing mode in which it engages in the important internal processes that are ongoing during sleep while still maintaining the ability to process vital external sensory information.SIGNIFICANCE STATEMENTPrevious research has shown that sensory processing continues during sleep. Here, we studied the capacity of the sleeping brain to extract and process relevant sensory information. We presented sleepers with their own names and unfamiliar names spoken by either an FV or a UFV. During NREM sleep, UFVs elicited more K-complexes and microarousals than FVs. By contrasting stimuli that evoked K-complexes, we demonstrate that UFVs evoked larger, more synchronized brain responses as well as stronger power at high frequencies (>16 Hz) relative to FVs. These differences in brain responses disappeared when no K-complexes were evoked. Our results suggest a pivotal role for K-complexes in the selective processing of relevant information during NREM sleep.

The aperiodic (1/f-like) component of electrophysiological data, whereby power systematically decreases with increasing frequency, as quantified by the aperiodic exponent, has been shown to differentiate sleep stages. Previous studies typically measured this exponent over narrow frequency ranges and averaged across sleep stages. A systematic review following PRISMA 2020 guidelines, which identified 16 eligible studies examining aperiodic neural activity during sleep, revealed heterogeneous frequency ranges and methodological approaches across studies. Building on these insights, the present study expands the analysis to include wider frequency ranges and alternative models, such as detecting ‘knees’ in the aperiodic component, which reflect bends in the power spectrum indicating changes in the exponent. Additionally, we applied time-resolved analyses to examine the dynamic patterns of aperiodic activity during sleep. We analyzed data from two sources: intracranial EEG (iEEG) from 106 epilepsy patients and high-density EEG from 17 healthy individuals and compared different frequency ranges and model forms of aperiodic activity. Results showed that broadband aperiodic models and the inclusion of a ‘knee’ feature effectively captured sleep stage-dependent differences in aperiodic activity. The knee parameter exhibited stage-specific variations, indicating different processing timescales across sleep stages. Time-resolved analysis of the aperiodic exponent tracked sleep stage transitions and responses to external stimuli, highlighting rapidly varying temporal dynamics during sleep. These findings offer valuable insights into brain dynamics during sleep and reveal novel insights and interpretations for understanding aperiodic neural activity during sleep.

The aperiodic (1/f-like) component of electrophysiological data - whereby power systematically decreases with increasing frequency, as quantified by the aperiodic exponent - has been shown to differentiate sleep stages. Earlier work, however, has typically focused on measuring the aperiodic exponent across a narrow frequency range. In this work, we sought to further investigate aperiodic activity during sleep by extending these analyses across broader frequency ranges and considering alternate model definitions. This included measuring ‘knees’ in the aperiodic component, which reflect bends in the power spectrum, indicating a change in the exponent. We also sought to evaluate the temporal dynamics of aperiodic activity during sleep. To do so, we analyzed data from two sources: intracranial EEG (iEEG) from 106 epilepsy patients and high-density EEG from 17 healthy individuals, and measured aperiodic activity, explicitly comparing different frequency ranges and model forms. In doing so, we find that fitting broadband aperiodic models and incorporating a ‘knee’ feature effectively captures sleep-stage-dependent differences in aperiodic activity as well as temporal dynamics that relate to sleep stage transitions and responses to external stimuli. In particular, the knee parameter shows stage-specific variation, suggesting an interpretation of varying timescales across sleep stages. These results demonstrate that examining broader frequency ranges with the more complex aperiodic models reveals novel insights and interpretations for understanding aperiodic neural activity during sleep.

Commercial sleep devices and mobile-phone applications for scoring sleep are gaining ground. In order to provide reliable information about the quantity and/or quality of sleep, their performance needs to be assessed against the current gold-standard, i.e. polysomnography (PSG; measuring brain, eye and muscle activity). We here assessed some commercially available sleep trackers, namely; a commercial activity tracker: Mi band (Xiaomi, BJ, CHN), a scientific actigraph: Motionwatch 8 (CamNTech, CB, UK), and a much used sleep application: Sleep Cycle (Northcube, GOT, SE). We recorded 27 nights in healthy sleepers using PSG and these devices. Surprisingly, all devices had very poor agreement with the gold standard. Sleep parameter comparisons revealed that specifically the Mi band and the sleep cycle application had difficulties in detecting wake periods which negatively affected the total sleep time and sleep efficiency estimations. However, all 3 devices were good in detecting the most basic parameter, the actual time in bed. In summary, our results suggest that, to-date; available sleep trackers do not provide meaningful sleep analysis but may be interesting for simply tracking times in bed. A much closer interaction with the scientific field seems necessary if reliable information shall be derived from such devices in the future.

Sleep and memory studies often focus on overnight rather than long‐term memory changes, traditionally associating overnight memory change (OMC) with sleep architecture and sleep patterns such as spindles. In addition, (para‐)sympathetic innervation has been associated with OMC after a daytime nap using heart rate variability (HRV). In this study we investigated overnight and long‐term performance changes for procedural memory and evaluated associations with sleep architecture, spindle activity (SpA) and HRV measures (R‐R interval [RRI], standard deviation of R‐R intervals [SDNN], as well as spectral power for low [LF] and high frequencies [HF]). All participants (N = 20, M_age = 23.40 ± 2.78 years) were trained on a mirror‐tracing task and completed a control (normal vision) and learning (mirrored vision) condition. Performance was evaluated after training (R1), after a full‐night sleep (R2) and 7 days thereafter (R3). Overnight changes (R2‐R1) indicated significantly higher accuracy after sleep, whereas a significant long‐term (R3‐R2) improvement was only observed for tracing speed. Sleep architecture measures were not associated with OMC after correcting for multiple comparisons. However, individual SpA change from the control to the learning night indicated that only “SpA enhancers” exhibited overnight improvements for accuracy and long‐term improvements for speed. HRV analyses revealed that lower SDNN and LF power was associated with better OMC for the procedural speed measure. Altogether, this study indicates that overnight improvement for procedural memory is specific for spindle enhancers, and is associated with HRV during sleep following procedural learning.