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.

翻译：我们提出了开源睡眠监测与调节装置（OSSMM），这是一个面向可及性睡眠研究的开源硬件与软件平台。OSSMM由一个基于3D打印的小型可穿戴头带构成，采用价格低廉的商用现成（COTS）组件，材料成本低于40欧元，并配有配套的安卓应用程序。该系统无需导电凝胶、一次性电极或专用设备，可捕获多种生物信号——包括运动、脉搏、眼电图（EOG）以及推测性脑电图（EEG），并通过无线连接实现数据存储，同时利用板载振动电机具备潜在的睡眠调节能力。一项为期15个夜晚的概念验证单参与者评估表明，所捕获的生物信号可支持基于传统机器学习方法的四阶段睡眠分类（清醒、浅睡、深睡、快速眼动睡眠），其中表现最佳的模型在经验证的非接触式睡眠监测仪（与PSG的κ=0.63）上取得了0.770的宏F1分数和0.776的准确率。有两项技术发现尤为值得关注。第一，来自商用健身胸带的低成本、可重复使用导电热塑性聚氨酯（CTPU）电极捕获的差分信号，其规范EEG频带内的频谱特性（包括与睡眠纺锤波一致的特征）是驱动分类的主要特征。第二，该信号仅通过两个前额电极获取，且无需专用接地参考，表明使用比常规配置更简化的方案即可实现实用的睡眠分期。所有硬件设计、软件和构建说明均已公开提供，以支持研究社区的复现与修改。