AI-based Real-Time Sleep Decoding and Closed-Loop Intervention from Sparse Wearable EEG

Sleep is essential for physical health, cognitive function, and overall well-being. Yet most advanced sleep foundation models are still designed for retrospective, offline analysis in data-rich clinical settings. We build SleepLoopFM, a real-time sleep staging AI model to bring this level of analysis into the home.

Traditional sleep analysis often depends on multichannel polysomnography. Home devices do not have that luxury. Consumer-grade headbands typically provide only sparse EEG, creating a major gap between clinical sleep models and real-world deployment. SleepLoopFM is designed specifically for this sparse-signal setting.

SleepLoopFM aims to narrow the gap between lab-grade sleep intelligence and practical home deployment. By combining sparse wearable EEG, temporal context, and real-time inference, it moves sleep technology closer to systems that can monitor, interpret, and eventually respond to sleep as it happens.

This allows to detect sleep phases in real-time, and immediately apply the right vestibular, tactile or auditory stimuli during dedicated sleep phases, in order to provide sleep therapy, stress reduction or health promotion.