Editorial: Methods to modulate sleep with neurotechnology, devices, or wearables

electrical, magnetic, thermal, or ultrasound-based stimulation. This review also outlines persistent engineering challenges: the importance of precise timing of stimuli, refractory periods that limit the number of effective stimuli, and the need for individualized, state-aware calibration to prevent 7 . Their findings underscore the importance of scalable, generalizable machine-learning models for enabling wearable devices to monitor brain states with sufficient precision for neurostimulation. However, meta-analytic approaches highlight uncertainty across biofeedback studies, as illustrated by the systematic review and meta-analysis provided by, Recio-Rodriguez et al 12 .evaluating neurofeedback interventions for insomnia. Curiously, across randomized controlled trials, 'surface neurofeedback' (based on real time EEG data), showed an overall effect which favored improvements in sleep quality (PSQI scores) in control conditions over-active conditions, whereas insomnia symptom severity showed no significant change. This contribution is an important reminder that methodological rigor, including placebo controls and standardized protocol, remains essential. Recio-Rodriguez 12 This topic highlights a departure from devices that target one sensory channel (namely auditory stimulation) toward integrated multi-modal approaches such as skin-temperature feedback and tactile stimulation. Future work demonstrating a combination of approaches is warranted.Proprietary, 'black box' sleep devices limit scientific replication and slow innovation. The field must move toward open-source hardware, transparent stimulation algorithms, and modifiable realtime decoding pipelines that researchers can inspect, validate, and refine. Such openness enables reproducibility, accelerates methodological improvements, and supports interoperable systems that can drive genuine advances in sleep neurotechnology.Taken together, the studies presented in this Research Topic demonstrate how neurotechnology, wearable systems, and computational methods are reshaping both the scientific understanding of sleep and the practical tools available to treat sleep disturbances. By integrating physiological precision, user-centered design, and open scientific frameworks, these approaches hold substantial promise for advancing sleep health across diverse populations, when applied correctly.