Effects of Brain–Computer Interfaces on Upper and Lower Limb Function Rehabilitation After Stroke

Brain–computer interfaces (BCIs) represent a form of neurotechnology that enables direct interaction with the environment by interpreting brain signals, without relying on conventional neuromuscular pathways. These systems utilize physiological signals such as electroencephalography (EEG) to allow users to control external equipment directly, thereby offering rehabilitation support and daily life assistance for individuals with motor function impairments. BCI is recognized for its high safety profile, ease of use, and demonstrated efficacy in functional recovery, making it increasingly relevant in clinical settings. Based on the method of signal acquisition, BCI is classified into invasive and non-invasive types, with non-invasive BCI being more commonly used in clinical practice due to lower procedural risks and greater usability. A typical BCI system operates through five core stages: signal acquisition, preprocessing, feature extraction, pattern classification, and control output. Currently, this technology has been effectively applied in the rehabilitation of motor functions in stroke patients, showing particularly promising results in the recovery of upper limb and walking functions. By promoting neuroplasticity in the brain, BCI can help stroke patients regain motor function. However, BCI still faces challenges such as signal quality issues, standardized treatment protocols, and treatment selection difficulties. Despite these limitations, BCI offers a promising approach for motor rehabilitation after stroke. Further research is needed to address existing challenges and improve clinical outcomes.