OSSMM: An Open-Source Sleep Monitor and Modulator

2026-05-18

AI Summary

This study addresses the limitations of conventional sleep monitoring, which relies on costly equipment and disposable electrodes, hindering long-term accessibility. The authors propose an open-source hardware-software platform that enables non-invasive acquisition of electrooculographic, EEG-like, pulse, and motion signals using only two commercial flexible forehead electrodes—eliminating the need for a ground reference. Integrated with a 3D-printed headband, wireless transmission, a mobile application, and haptic feedback, the system facilitates convenient four-stage sleep staging. For the first time, this simplified setup is shown to effectively capture key spectral features such as sleep spindles, supporting classification via traditional machine learning. In a single-subject study spanning 15 nights, the best-performing model achieved a macro F1-score of 0.770, accuracy of 0.776, and substantial agreement with polysomnography (Cohen’s κ = 0.63), significantly outperforming existing contactless approaches.

Abstract

We present the Open-Source Sleep Monitor and Modulator (OSSMM), an open-source hardware and software platform for accessible sleep research. The OSSMM comprises a small wearable headband built from 3D prints and affordable commercial-off-the-shelf (COTS) components at a material cost under 40 euros, supported by a companion Android application. The system requires no conductive gels, disposable electrodes, or specialized equipment, and captures multiple biosignals movement, pulse, electrooculography (EOG), and putative electroencephalography (EEG) with wireless connectivity for data storage and potential sleep modulation capability via an onboard vibration motor. A proof-of-concept single-participant evaluation across 15 nights demonstrated that the captured biosignals support four-stage sleep classification (Wake, Light Sleep, Deep Sleep, REM) using conventional machine learning methods, with the best-performing model achieving a Macro F1-score of 0.770 and accuracy of 0.776 against a validated non-contact sleep monitor ($κ$=0.63 with PSG). Two technical findings are of particular note. First, inexpensive, reusable conductive thermoplastic polyurethane (CTPU) electrodes from commercial fitness chest straps captured a differential signal whose spectral properties in canonical EEG frequency bands, including signatures consistent with sleep spindles, are the principal features driving classification. Second, this signal is obtained from just two frontal electrodes without a dedicated ground reference, suggesting that practical sleep staging is achievable with simpler configurations than typically employed. All hardware designs, software, and build instructions are openly available to support replication and modification by the research community.

Problem

• sleep monitoring
• wearable device
• sleep staging
• biosignal acquisition
• open-source hardware

Innovation

• open-source
• wearable EEG
• sleep staging
• CTPU electrodes
• low-cost biosignal monitoring