Biology subjects

Zerr, P.

Publications and source records attributed to Zerr, P..

3 recordsLinked to original sources

bioRxiv2025

ezscore-f: A Set of Freely Available, Validated Sleep Stage Classifiers for Forehead EEG

The increasing availability of wearable forehead EEG devices, such as the Hypnodyne ZMax, open-source DCM, CGX PatchEEG, and many others, has significantly expanded opportunities for convenient, at-home sleep monitoring. However, most publicly available sleep classifiers are trained on scalp EEG from traditional polysomnography (PSG) data, and thus generalize poorly to forehead EEG due to differences in electrode placement, referencing montages, and higher susceptibility to artifacts from user movements and electrode displacement. Conventional classifiers typically do not explicitly account for these artifacts, resulting in inaccurate and misleading sleep stage scoring. To address this gap, we developed a suite of artifact-aware sleep stage classifiers trained specifically using forehead EEG data, leveraging two comprehensive datasets--Wearanize+ and Donders2022--that contain concurrent forehead EEG and clinical PSG recordings. We further introduce two classifier variants: one optimized for real-time applications that operates directly on raw EEG amplitudes, and another optimized for offline analysis utilizing normalized EEG signals. Validation results indicate robust and reliable classification performance across standard sleep stages (Wake, N1, N2, N3, REM), along with effective identification of artifact epochs. Importantly, the developed classifiers generalize well to forehead EEG devices beyond the original training platform. These validated classifiers are freely available to the sleep research community through the open-source ezscore-f package, providing versatile and practical tools for forehead EEG-based sleep stage analysis.

neuroscience↗

bioRxiv2024

Highly effective verified lucid dream induction using combined cognitive-sensory training and wearable EEG: a multi-centre study

Lucid dreaming occurs when one is aware of dreaming while asleep. While lucid dreaming can occur spontaneously, it remains rare. This multi-center study (Netherlands, Italy, Canada) tested a combined induction approach integrating senses-initiated lucid dreaming (SSILD) with targeted lucidity reactivation in a large sample of participants with varying lucid dreaming experience. Sixty participants (33 F; 26.5 {+/-}6.2 years old) completed two morning naps in the sleep laboratory. Participants received pre-sleep SSILD training paired with visual, auditory, and tactile cues that were then reintroduced in REM sleep, with stimulation and sham conditions counterbalanced. Lucidity and cue perception were verified using intentional eye movements from within the dream (signal-verified lucid dream, SVLD). SVLDs occurred in 31 participants (51.7%) across both naps. Subjective lucidity occurred in 63 naps (52.5%), including 40 SVLDs (33.3%) with no difference between stimulation (38.3%) and sham (28.3%) conditions. However, SVLD duration and number of predefined eye movements were higher with sensory cueing. Cues were perceived within sleep in most cued REM periods (71.1%), sometimes acting as lucidity signals (39.1% of stim SVLDs) and occasionally disrupting sleep (23.7% of cued periods). Overall, our combined cognitive-sensory induction method produced relatively high lucid dreaming rates, even in participants who rarely experience them. While cues were sometimes perceived as lucidity signals and may help prolong lucid episodes, our results do not show clear benefits of REM cueing over SSILD training alone. Further refinement of these methods may support clinical applications and deepen insight into consciousness and sensory processing during sleep.

neuroscience↗

bioRxiv2023

Fractal cycles of sleep: a new aperiodic activity-based definition of sleep cycles

Nocturnal human sleep consists of 4 - 6 ninety-minute cycles defined as episodes of non-rapid eye movement (non-REM) sleep followed by an episode of REM sleep. While sleep cycles are considered fundamental components of sleep, their functional significance largely remains unclear. One of the reasons for a lack of research progress in this field is the absence of a data-driven definition of sleep cycles. Here, we proposed to base such a definition on fractal (aperiodic) neural activity, a well-established marker of arousal and sleep stages. We explored temporal dynamics of fractal activity during nocturnal sleep using electroencephalography. Based on the observed pattern of fractal fluctuations, we introduced a new concept of fractal activity-based cycles of sleep or "fractal cycles" for short, defined as a time interval during which fractal activity descends from its local maximum to its local minimum and then leads back to the next local maximum. Next, we assessed correlations between fractal and classical (i.e., non-REM - REM) sleep cycle durations. We also studied cycles with skipped REM sleep, i.e., the cycles where the REM phase is expected to appear except that it does not, being replaced by lightening of sleep. Regarding the sample, we examined fractal cycles in healthy adults (age range: 18 - 75 years, n = 205) as well as in children and adolescents (range: 8 - 17 years, n = 21), the group characterized by deeper sleep and a higher frequency of cycles with skipped REM sleep. Further, we studied fractal cycles in major depressive disorder (n = 111), the condition characterized by altered REM sleep (in addition to its clinical symptoms). We found that fractal and classical cycle durations (89 {+/-} 34 min vs 90 {+/-} 25 min) correlated positively (r = 0.5, p < 0.001). Cycle-to-cycle overnight dynamics showed an inverted U-shape of both fractal and classical cycle durations and a gradual decrease in absolute amplitudes of the fractal descents and ascents from early to late cycles. In adults, the fractal cycle duration and participants age correlated negatively (r = -0.2, p = 0.006). Children and adolescents had shorter fractal cycles compared to young adults (76 {+/-} 34 vs 94 {+/-} 32 min, p < 0.001). The fractal cycle algorithm detected cycles with skipped REM sleep in 91 - 98% of cases. Medicated patients with depression showed longer fractal cycles compared to their own unmedicated state (107 {+/-} 51 min vs 92 {+/-} 38 min, p < 0.001) and age-matched controls (104 {+/-} 49 vs 88 {+/-} 31 min, p < 0.001). In conclusion, fractal cycles are an objective, quantifiable, continuous and biologically plausible way to display sleep neural activity and its cycles. They are useful in healthy adult and pediatric populations as well as in patients with major depressive disorder. Fractal cycles should be extensively studied to advance theoretical research on sleep structure. Highlights- Fractal activity-based cycles of sleep or "fractal cycles" for short is a new concept based on cyclic changes in fractal (aperiodic) neural activity during sleep. - Durations of fractal and classical cycles correlate, and both show an inverted U-shape when seen from early to late cycles. - The fractal cycle algorithm is effective in detecting cycles with skipped REM sleep. - Older healthy adults shower shorter fractal - but not classical - cycle durations. - Fractal cycle duration is shorter in children and adolescents compared to young adults. - In major depressive disorder, antidepressant medication is associated with longer fractal cycles.

neuroscience↗