Commentary on “Efficacy of the Conventional Rehabilitation Robot and bio-Signal Feedback-Based Rehabilitation Robot on Upper-Limb Function in Patients with Stroke: A Systematic Review and Network Meta-Analysis”

We read with great interest Zhou et al.'s article comparing the effectiveness of conventional, EMG-based, and BCI-based rehabilitation robots on upper limb function for stroke patients (Zhou et al., 2025). The authors conducted an extensive systematic review and network meta-analysis (59 randomized controlled trials), making this article both critical and timely within the neurorehabilitation community. Using high-quality assessments, including the Cochrane Risk of Bias tool and GRADE, the authors were able to present a hierarchy of the current robotic interventions’ effectiveness.

The authors' finding that BCI-based rehabilitation robots achieve the highest overall, short-term and long-term effects (as determined by SUCRA) is particularly relevant given the emergence of interventions that maximize neuroplasticity and functional recovery, especially in patients with more severe impairments (Zhou et al., 2025). Additionally, these findings are supported by recent systematic review literature regarding the ability of BCI systems to engage both primary and secondary cortical networks, thereby enhancing rehabilitation outcomes (Baniqued et al., 2021). Also, the authors' separation of EMG-based robots, which demonstrated short-term efficacy, from conventional robots, which showed longer-term efficacy, adds clarity to ongoing debates regarding optimal technology and the appropriate dosing of robotic therapy.

Nonetheless, several important issues remain. First, although the results are promising, there are various challenges related to cost, technical expertise, and integration into existing workflows that hinder the implementation of BCI-based technologies in routine clinical practice. Future research should address these barriers so that advanced robotic therapies can become accessible outside of specialized research centers. Second, the included patient populations were heterogeneous regarding stroke severity, chronicity, and demographics, which raises questions about the generalizability of these findings. Subgroup analyses may be helpful to determine which patients will benefit most from specific modalities, allowing for tailored interventions (Winstein et al., 2016). Third, although BCI-based robots yielded the best overall long-term results, the mean difference was not statistically significant, indicating a need for more high-quality long-term research to ensure treatment effects are durable (Zhou et al., 2025). The ongoing challenge of translating short-term benefits into lasting functional changes remains an important issue in the field (Hameed et al., 2020).

Finally, the finding that EMG-based robots are more effective than conventional robots in the short term, but not the long term, raises important questions about the optimal duration of intervention, strategies to promote patient engagement, and therapy design (Ramos-Murguialday et al., 2019). It will be important for future progress to determine how best to leverage the strengths of each modality, possibly through hybrid approaches or individualized protocols.

In conclusion, Zhou et al. present compelling evidence that enhances our understanding of robotics in stroke rehabilitation and underscores the potential of incorporating neurophysiological feedback into therapy. Their work provides useful guidance for both clinical practice and future research, with a clear focus on implementation, patient selection, and ensuring sustained benefits.