This thesis investigated the abandonment of rapid eye movement (REM) sleep reduction as a therapeutic intervention for depression and developed an open-source solution to accessibility barriers in sleep research. A targeted review revealed that all three controlled human studies of REM sleep reduction reported symptom improvement, but contained significant methodological concerns. Even so, these limitation do not justify abandonment of this research. The primary contribution is the Open-Source Sleep Monitor and Modulator (OSSMM) system, with a component cost under €40, which captures putative frontal electroencephalography (EEG), electrooculography (EOG), photoplethysmography (PPG), plus movement data from an inertial measurement unit (IMU). Notably, EEG and EOG signals are captured using inexpensive conductive thermoplastic polyurethane (CTPU) electrodes sourced from consumer heart-rate monitor chest straps. A technical evaluation over a 17-night, single-participant dataset, with a silver-standard reference monitor demonstrated that machine learning classifiers achieved a Macro F1 of 0.810 and a Cohen’s kappa (κ) of 0.733, confirming the system captures physiologically relevant information for reliable sleep stage discrimination. Complete hardware designs, software, and assembly instructions are freely available, providing researchers worldwide with an accessible platform for sleep monitoring and modulation studies, thereby supporting renewed investigation of sleep interventions for depression.