BackgroundBrain-computer interface-driven functional electrical stimulation (BCI-FES) is a promising approach for post-stroke upper limb rehabilitation. However, considerable variability exists in stimulation parameters and task designs across studies, and evidence remains insufficient to support definitive protocol recommendations.MethodsWe searched PubMed, Embase, Web of Science, and the Cochrane Library for randomized controlled trials (RCTs) up to September 2025. Eligible studies applied BCI-FES and reported the Fugl-Meyer Assessment for the upper extremity (FMA-UE). Risk of bias was assessed with the PEDro scale, and evidence certainty graded with GRADE. Random-effects meta-analyses were performed.ResultsTwelve RCTs (n = 619) showed BCI-FES improved FMA-UE scores versus controls (MD = 5.82, 95% CI 3.04-8.59, p 2 = 39%), with larger benefits in subacute stroke (MD = 8.45). Dynamic-threshold paradigms and motor imagery were associated with higher effect sizes. Higher stimulation frequency (>50 Hz), narrow-pulse width (150 µs) more frequent sessions (≥5/week), shorter session duration (≤30 min), greater total sessions (>20), and longer intervention (>4 weeks) tended to be associated with larger effect sizes, though evidence is limited and based on few studies. Secondary outcomes (ARAT, WMFT, MBI) improved, and no serious adverse events were reported. Evidence certainty was moderate.ConclusionBCI-FES was associated with improvements in upper limb motor recovery after stroke, especially in subacute patients. Some stimulation and training features may relate to greater effects, but current evidence remains insufficient for definitive clinical guidance. Larger multicenter RCTs are needed to clarify dose-response relationships and support biomarker-guided, personalized interventions.
Building similarity graph...
Analyzing shared references across papers
Loading...
Fengjiao Liang
Xiang Chen
B Q Li
Clinical EEG and Neuroscience
Shandong University
Shanghai University
Shanghai University of Traditional Chinese Medicine
Building similarity graph...
Analyzing shared references across papers
Loading...
Liang et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69e1cf985cdc762e9d8587eb — DOI: https://doi.org/10.1177/15500594261441055