Applications and neural mechanisms of non-invasive brain-computer interfaces in motor function rehabilitation for elderly stroke patients

Stroke is a highly prevalent neurological disorder among older adults, frequently resulting in severe motor impairment. Non-invasive brain-computer interface (BCI), which decodes motor intentions to actuate external devices, offers a promising avenue for motor function rehabilitation in elderly stroke patients. Current mainstream paradigms include motor imagery-BCI (MI-BCI), steady-state visual evoked potential-BCI (SSVEP-BCI), and P300-BCI, each exhibiting distinct profiles in terms of cognitive load and sensory dependence. In clinical practice, BCI is frequently integrated with functional electrical stimulation (FES), robotic exoskeletons, and virtual reality (VR) to constitute closed-loop training systems. However, reduced neuroplasticity, compromised brain network connectivity, and cognitive decline in elderly patients may attenuate the therapeutic efficacy of BCI interventions. Neurophysiological evidence indicates that BCI training can ameliorate motor function by activating Hebbian plasticity, facilitating brain network reorganization, and restoring interhemispheric balance. This review summarizes the current clinical applications and underlying neural mechanisms of non-invasive BCI for motor function rehabilitation in elderly stroke patients, with particular attention to the unique challenges encountered in this population.