This file was created by the TYPO3 extension publications --- Timezone: CEST Creation date: 2026-04-14 Creation time: 14:48:48 --- Number of references 5 article 10.1093/sleepadvances/zpaf073 Up-phase-targeted auditory stimulation (up-PTAS) during slow-wave sleep has become a valuable tool for modulating slow oscillations and slow oscillation-spindle-coupling in favor of overnight memory retention. Developing effective, automated protocols for translation into more naturalistic and clinical settings is an ongoing challenge, especially because current PTAS protocols and their behavioral effects vary greatly between studies. Our study contributes to ongoing efforts in characterizing parameter choices in PTAS and compares two up-PTAS protocols with systematic variations of the interstimulus intervals (ISIs) and their effect on the consolidation of a finger-tapping sequence using a mobile PTAS device and an app-based behavioral task in a home setting. Participants tolerated both protocols well and showed high adherence to the study procedures. Electrophysiological stimulus responses and learning trajectories in the finger-tapping task replicated lab-based findings. We extend studies suggesting a nonlinear relationship between stimulus number and PTAS effects by showing that applying fewer stimuli with longer ISIs enhances overnight consolidation of a finger-tapping sequence more effectively than applying more stimuli with shorter ISIs. Exploratory electrophysiological analyses revealed that the behavioral response was positively correlated with the number of stimuli with auditory evoked K-complexes relative to the number of stimuli without K-complexes. PTAS stimuli with longer ISIs (>1.25) were associated with a higher likelihood of K-complex responses and increased spindle power. Our findings demonstrate the feasibility of mobile, at-home PTAS combined with app-delivered behavioral tasks in healthy participants and can inform the development of more effective memory enhancement protocols. 2025 10 2632-5012 10.1093/sleepadvances/zpaf073 SLEEP Advances 6 zpaf073 4 sleeploop sleep sleep sleeploop https://doi.org/10.1093/sleepadvances/zpaf073 Vanessa Kasties Nicole Meier Nora-Hjördis Moser Renske Sassenburg Walter Karlen Maria Laura Ferster Sara Fattinger Angelina Maric Reto Huber article semkiv2025artifact 2025 10.48550/arXiv.2504.08469 arXiv preprint arXiv:2504.08469 eeg sleep ml quality Khrystyna Semkiv Jia Zhang Maria Laura Ferster Walter Karlen article leach2025local 2025 10.1101/2025.03.05.641406 bioRxiv 2025—03 sleep sleeploop Sven Leach Sara Fattinger Elena Krugliakova Jelena Skorucak Georgia Sousouri Sophia Snipes Selina Schühle Maria Laura Ferster Giulia Da Poian Walter Karlen others article his work investigates whether a deep learning model with minimal inputs can accurately identify Rapid Eye Movement Sleep Behavioral Disorder (RBD). We propose an interpretable two-step approach using two convolutional neural networks for sleep staging and RBD classification. Experiments on data from 18 RBD participants and 178 healthy controls demonstrate that reliable classification can be achieved using frontal electroencephalogram (EEG) and electrooculogram (EOG) input signals. GradCAM attention reveals a 22\% increase in importance in the 9-22 Hz band of EOG for RBD cases. Our findings highlight the potential for remote, wearable-based RBD screening at home. 2 2025 10.1101/2025.07.01.25330646 MedRXiv PD sleep wearable Khrystyna Semkiv Walter Karlen article huwiler2025sleep 2025 10.1111/jsr.14328 Journal of Sleep Research 34 e14328 2 sleep arousal remote Stephanie Huwiler M Laura Ferster Luzius Brogli Reto Huber Walter Karlen Caroline Lustenberger