Stroke recovery is increasingly understood as a process shaped by disrupted interactions among motor intention, descending motor output, peripheral movement, and sensory feedback, rather than by motor weakness alone. After stroke, residual motor intention may not be effectively translated into spinal motor output, peripheral movement may be too limited to provide sufficient sensory feedback, and compensatory network recruitment may not always support efficient motor control. Closed-loop brain-computer interfaces offer a mechanistically motivated approach to this problem by detecting motor imagery, motor attempt, or sensorimotor rhythms in real time and translating these signals into contingent functional electrical stimulation, robotic assistance, virtual reality, multisensory feedback, or neuromodulation. By partially restoring the temporal coupling between detected motor intention, assisted or stimulated movement, and sensory feedback, these systems are hypothesized to promote activity-dependent plasticity, reinforce sensorimotor circuits, and modulate distributed motor, sensory, attentional, and interhemispheric networks. In this review, we synthesize the neurobiological basis, recovery mechanisms, implementation strategies, and emerging predictive biomarkers of closed-loop brain-computer interfaces for post-stroke sensorimotor rehabilitation. Although closed-loop brain-computer interfaces show promise for selected patients, treatment effects remain heterogeneous and depend on patient characteristics, decodability of motor-related brain signals, intervention dose, feedback modality, and trial design. Future progress will require mechanism-informed patient stratification, adaptive decoding, individualized feedback, standardized outcomes, and real-world validation.