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Peer-reviewedCC BY 4.0Frontiers in Neuroscience

Comparative efficacy of brain-computer interface training with different feedback modalities for post-stroke upper-limb motor recovery: a systematic review and network meta-analysis of randomized controlled trials

  • Fuqiang Wang1,2
  • Xiao Lu1,2
  • Yunhong Deng2,3,4
  • Yaojiang Li1,2
  • Xiaodi Li1,2,5
  • Congping Huang1,2
  • Mengliang Ma1,2
  • Bianhong He1,2
  • Lang Yu2,3
  • Lixia Deng1,2
5 affiliations
  1. 1Department of Rehabilitation Medicine, Guangdong Sanjiu Brain Hospital
  2. 2Sanjiu Brain Disease and Rehabilitation Hospital Affiliated to South China Normal University
  3. 3Department of Rehabilitation Therapy, Guangdong Sanjiu Brain Hospital
  4. 4Clinical Medical College of Acupuncture Moxibustion and Rehabilitation, Guangzhou University of Chinese Medicine
  5. 5Institute for Brain Research and Rehabilitation, South China Normal University
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Abstract

Background: Brain-computer interface (BCI) training may improve post-stroke upper-limb recovery through closed-loop feedback, but the comparative efficacy of different feedback modalities remains uncertain. This study compared BCI training with different feedback modalities for post-stroke upper-limb motor impairment.

Methods: PubMed, Embase, Web of Science, Cochrane Library, and Scopus were searched from inception through April 2026 for randomized controlled trials of BCI-based upper-limb rehabilitation after stroke. BCI interventions were classified by feedback modality and feedback contingency into multimodal feedback (BCI-MM), robotic or exoskeleton-assisted feedback (BCI-Robot), functional electrical stimulation feedback (BCI-FES), and visual feedback (BCI-visual), while comparator interventions were classified separately, with usual rehabilitation (UR) as the reference. Random-effects network meta-analysis estimated WMDs with 95% CIs. The primary outcome was Fugl-Meyer Assessment for Upper Extremity (FMA-UE); secondary outcomes were the Action Research Arm Test (ARAT) and Wolf Motor Function Test (WMFT). Risk of bias was assessed using the Cochrane Risk of Bias 2 tool, and certainty was assessed using CINeMA.

Results: Twenty-two trials involving 956 patients across subacute, chronic, and mixed post-stroke stages were included. The FMA-UE network contained 11 connected nodes, with no global or local inconsistency. Compared with UR, BCI-Robot showed greater FMA-UE improvement (WMD = 6.28, 95% CI 2.70–9.86), as did non-BCI robotic training (WMD = 4.26, 95% CI 0.42–8.10) and BCI-MM (WMD = 4.03, 95% CI 1.00–7.06). BCI-FES did not differ significantly from UR. BCI-visual had the highest SUCRA value (77.6%), but its comparison with UR was non-significant, with very low certainty of evidence; no BCI feedback modality was superior to another. Secondary outcomes showed greater ARAT improvement with BCI-Robot versus UR, whereas WMFT findings were non-significant. Certainty for the main FMA-UE comparisons was low or very low.

Conclusion: BCI-Robot and BCI-MM showed statistically significant FMA-UE benefits versus UR, although certainty was low. As non-BCI robotic training also showed benefit, the BCI-Robot effect cannot be attributed to BCI alone. No BCI feedback modality was statistically superior to another. Further high-quality direct-comparison trials are needed.

Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420261370474 , Unique Identifier: CRD420261370474.

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Cite

BibTeXRIS
BibTeX
@article{wang2026comparative,
  title = {{Comparative efficacy of brain-computer interface training with different feedback modalities for post-stroke upper-limb motor recovery: a systematic review and network meta-analysis of randomized controlled trials}},
  author = {Fuqiang Wang and Xiao Lu and Yunhong Deng and Yaojiang Li and Xiaodi Li and Congping Huang and Mengliang Ma and Bianhong He and Lang Yu and Lixia Deng},
  journal = {Frontiers in Neuroscience},
  year = {2026},
  month = oct,
  doi = {10.3389/fnins.2026.1925602},
  url = {https://www.frontiersin.org/articles/10.3389/fnins.2026.1925602/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Comparative efficacy of brain-computer interface training with different feedback modalities for post-stroke upper-limb motor recovery: a systematic review and network meta-analysis of randomized controlled trials
AU  - Fuqiang Wang
AU  - Xiao Lu
AU  - Yunhong Deng
AU  - Yaojiang Li
AU  - Xiaodi Li
AU  - Congping Huang
AU  - Mengliang Ma
AU  - Bianhong He
AU  - Lang Yu
AU  - Lixia Deng
PY  - 2026
DA  - 2026/10/16
JO  - Frontiers in Neuroscience
DO  - 10.3389/fnins.2026.1925602
UR  - https://www.frontiersin.org/articles/10.3389/fnins.2026.1925602/full
AB  - Background: Brain-computer interface (BCI) training may improve post-stroke upper-limb recovery through closed-loop feedback, but the comparative efficacy of different feedback modalities remains uncertain. This study compared BCI training with different feedback modalities for post-stroke upper-limb motor impairment.  Methods: PubMed, Embase, Web of Science, Cochrane Library, and Scopus were searched from inception through April 2026 for randomized controlled trials of BCI-based upper-limb rehabilitation after stroke. BCI interventions were classified by feedback modality and feedback contingency into multimodal feedback (BCI-MM), robotic or exoskeleton-assisted feedback (BCI-Robot), functional electrical stimulation feedback (BCI-FES), and visual feedback (BCI-visual), while comparator interventions were classified separately, with usual rehabilitation (UR) as the reference. Random-effects network meta-analysis estimated WMDs with 95% CIs. The primary outcome was Fugl-Meyer Assessment for Upper Extremity (FMA-UE); secondary outcomes were the Action Research Arm Test (ARAT) and Wolf Motor Function Test (WMFT). Risk of bias was assessed using the Cochrane Risk of Bias 2 tool, and certainty was assessed using CINeMA.  Results: Twenty-two trials involving 956 patients across subacute, chronic, and mixed post-stroke stages were included. The FMA-UE network contained 11 connected nodes, with no global or local inconsistency. Compared with UR, BCI-Robot showed greater FMA-UE improvement (WMD = 6.28, 95% CI 2.70–9.86), as did non-BCI robotic training (WMD = 4.26, 95% CI 0.42–8.10) and BCI-MM (WMD = 4.03, 95% CI 1.00–7.06). BCI-FES did not differ significantly from UR. BCI-visual had the highest SUCRA value (77.6%), but its comparison with UR was non-significant, with very low certainty of evidence; no BCI feedback modality was superior to another. Secondary outcomes showed greater ARAT improvement with BCI-Robot versus UR, whereas WMFT findings were non-significant. Certainty for the main FMA-UE comparisons was low or very low.  Conclusion: BCI-Robot and BCI-MM showed statistically significant FMA-UE benefits versus UR, although certainty was low. As non-BCI robotic training also showed benefit, the BCI-Robot effect cannot be attributed to BCI alone. No BCI feedback modality was statistically superior to another. Further high-quality direct-comparison trials are needed.  Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420261370474 , Unique Identifier: CRD420261370474.
SN  - 1662-453X
SN  - 1662-4548
ER  -