Stroke is the leading cause of long-term adult disability worldwide, leading to an estimated cost burden [1]. Survivors of stroke frequently suffer from hemiplegia, often characterized by motor impairment (e.g., abnormal movement patterns and abnormal reflexes), poor motor function, spasticity in affected limbs, and muscle weakness, presenting a significant challenge in stroke rehabilitation [2–4]. Despite active treatment, 80% of people with stroke failed to achieve full recovery of motor function and disabilities of the upper limb, affecting their activities of daily living and overall quality of life substantially [5]. Various rehabilitation approaches employed nowadays have some limitations [6]. For example, constraint-induced movement therapy (CIMT), is often suitable for individuals with residual movement capability, and robotic therapy and neuromuscular electrical stimulation, often rely on fixed programs without brain activity significant recovery [7]. Hence, finding a more effective method for upper-limb recovery after stroke remains a challenge.

Recently several studies [8–11] have reported upper-limb benefits through BCI-based training. BCI, a novel technology in neurological rehabilitation, facilitates bidirectional communication between the brain and the environment by utilizing recorded brain activity [12]. BCI systems can be categorized as invasive or non-invasive, depending on the location of signal acquisition. The non-invasive BCI is widely adopted due to its safety, portability, and cost-effectiveness [13]. Non-invasive BCI methods for recording brain signals include electroencephalogram (EEG), magnetoencephalogram (MEG), functional near-infrared spectroscopy (fNIRS), and functional magnetic resonance imaging (fMRI), with EEG being the most commonly utilized [14]. Motor imagery (MI), steady-state visual evoked potential (SSVEP), and P300 are common sources of EEG signals [15]. In contrast to common rehabilitation strategies for post-stroke upper-limb motor dysfunction, BCI-based training employs a closed-loop approach called “central-peripheral-central”, offering the benefits of real-time feedback that enables self-regulation of neurophysiological activities [16].

BCI is often combined with various feedback methods such as functional electrical stimulation (FES) [8, 9, 17–21], robotic assistance [10, 11, 22–31], and visual feedback [32, 33]. Although benefits for post-stroke upper-limb motor impairment have been reported, debates persist due to variations in training protocol. Recent systematic reviews mostly focused on the impact of different external feedback types or stroke phases [34, 35] without analysis of the efficacy of specific external feedback across different phases. Moreover, the impact of several key clinical issues, such as the training intensity (time, sessions, and duration) was not considered appropriately. The optimal scheduling for BCI-based training has not been systematically analyzed. Addressing these aspects is essential for developing personalized training programs and enhancing the recovery of upper-limb function, daily activities, and overall quality of life in stroke patients.

The primary objectives of this meta-analysis are as follows: (1) to investigate the clinical effects of BCI-based training on improving upper-limb motor impairment, motor function, and activities of daily living following a stroke, (2) to identify potential factors that may impact the outcomes, including stroke phases, external feedback, external feedback across stroke phases, training intensity (time, sessions, and duration) and (3) to try to determine optimal BCI-based training protocol for stroke patients.

We followed the guidelines provided by the Meta-Analysis of Observational Studies in Epidemiology Group, the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020 guidelines [36], the PRISMA checklist (Supplementary Table S1), and the Cochrane Collaboration definition for systematic review and meta-analysis. The review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO: CRD42022383390).

The meta-analysis explored the efficacy of BCI-based training on upper-limb function in stroke patients, revealing significant enhancements in upper-limb motor impairment and function, and this improvement can be maintained or continued to increase after intervention. Besides, the efficacy of BCI-based training varied with stroke phases, external feedback types, and training intensity. Our present work could give a new insight into BCI-based training for stroke survivors.

Subgroup analysis revealed the effectiveness of BCI-based training for both subacute and chronic stroke patients. The initial six months post-stroke play a crucial role in shaping the recovery trajectory [47]. However, many stroke patients miss that time window and only obtain limited functional gains during the chronic phase [48]. Patients in the chronic phase can still exhibit enduring and extensive cortical reorganization through reinforcement learning and programmed memory, leading to positive prognostic outcomes [49]. BCI-based training facilitates safe and repetitive rehabilitation training, allowing for maximum patient participation [50, 51], enhancing early recovery, and overcoming later functional plateaus.

Clinically, different studies have used different types of external feedback to apply to stroke patients. Our results showed that BCI-FES, BCI-robot, and BCI-visual feedback can significantly improve motor impairment. These findings underscored the distinctive benefits of the active closed-loop feedback inherent to the “central-peripheral-central” paradigm of BCI-based training. The enduring benefits of BCI-based training are likely due to its capacity to enhance motor recovery, cortical reorganization, and functional restoration, resulting in long-term improvements in motor impairment [8, 52]. During BCI-based training, patients develop movement intentions to move the paralyzed limb, and the coupling of external feedback devices completes the loop between cortical activity and movement [10, 53, 54]. This process enables participants to consciously regulate sensorimotor oscillations, generating afferent feedback activity that may restore corticospinal and corticomuscular connections, fostering neuroplasticity and improving motor impairment [25, 55, 56].

In addition, subgroup analysis revealed that BCI-FES is effective for both subacute and chronic stroke patients, whereas BCI-robot is beneficial exclusively for the chronic phase and not for subacute patients. This suggested that for subacute stroke patients, BCI-FES may be more potent in enhancing upper-limb motor impairment compared to BCI-robot. Furthermore, in chronic stroke patients, BCI-FES yielded a higher improvement than BCI-robot. This disparity may arise from FES facilitating voluntary muscle contraction, providing rich proprioceptive and somatosensory information feedback, and increasing perfusion to the sensory-motor cortex [57–59]. The efficacy of BCI-robot may also be influenced by the type of robot used, as differences in materials, joints, and training protocols can affect joint mobility, proprioceptive stimuli, and patient experience [60–62]. The robots used in these studies included end-effector [22, 23], hand exoskeleton [11, 24, 26, 28, 30], arm and hand orthosis [10, 27], and soft robotic gloves [25, 29]. While the pooled results from our studies leaned towards BCI combined with visual feedback, some researchers, such as Ono et al. [63] have posited that proprioceptive feedback generated by real motion is more advantageous. Therefore, FES may be more favorable external feedback for BCI in upper-limb recovery following a stroke. Combining BCI with FES may be a more favorable option for patients in both the subacute and chronic phase.

Regarding training intensity, the majority of researchers opted for a training frequency of 3–5 sessions per week. Moreover, a training duration of 3–4 weeks appeared to be optimal. In general, more intense exercise is associated with more benefits [64]. However, our analysis showed that training for less than 20 min or up to 90 min per day and training 2 weeks or longer than 4 weeks did not significantly improve motor impairment. On the one hand, inadequate daily training durations may preclude meaningful improvement, whereas excessively long sessions could precipitate fatigue and distraction [26], on the other hand, the small number of included studies and the high heterogeneity caused the bias of the results [25]. More high-quality studies are needed to bring more reliable results. Drawing from the subgroup analysis, we can offer tentative recommendations on training intensity: daily training sessions ranging from 20 to 90 min, conducted 2–5 sessions per week for 3–4 weeks, may yield the most favorable results.

MBI scores were not improved compared with the control group after BCI-based training in our analysis. MBI focuses on independence from the activities of daily living, rather than on a single motor function. The functional improvement observed may have not yet been translated into the practical use of the upper limbs for activities of daily living. It is noteworthy to highlight that spasticity is a prevalent complication among stroke survivors, with a notable impact on upper-limb recovery. Interestingly, 5 studies [8–10, 29, 33] consistently demonstrated that BCI-based training did not confer greater improvements in MAS or AS scores compared with control interventions. This observation suggests that BCI-based training may not substantially influence spasticity associated with stroke. More effective rehabilitation strategies for spasticity need further exploration.

Conventional rehabilitation therapies also play a key role in clinical recovery. The studies included in our analysis predominantly encompassed conventional rehabilitation programs that involved physical therapy (e.g. exercise therapy, passive mobilization, stretching, and physical factor therapy) [8–11, 17–22, 24–33] and more than half of the studies also incorporated occupational therapy [9–11, 20, 24, 28–33]. A few studies mentioned acupuncture [18] and cognitive therapy [33]. Overall, conventional rehabilitation pathways were generally consistent across the studies.

BCI-based training holds promise for patients with motor impairment, bridging the gap between motor intention and actual movement. Beyond stroke phases, external feedback, and training intensity, BCI accuracy is always crucial for the efficiency of BCI-based training. Considering moderate to severe brain deficits of stroke patients, motor-related cortical activities may be decreased or even hindered [65], which causes difficulties in detecting motor intention. Much effort has been made to solve the problem, including sophisticated algorithms [66], user training [67], and sensor fusion [68]. Recently, non-invasive brain stimulation techniques, such as anodal transcranial direct current stimulation (tDCS) [69] and repetitive transcranial magnetic stimulation (rTMS) [70], have been considered to be feasible in enhancing BCI performance. These approaches offer novel solutions to address BCI inefficiency, potentially advancing the clinical application of BCI-based stroke rehabilitation.

Finally, given the current findings and the PEDro scale assessment in our analysis, future studies should explore the dose-response relationship of BCI-based training, optimize treatment regimens, and incorporate long-term follow-up to assess the sustainability of the benefits of BCI-based training. Secondly, an individualized BCI-based training program should be developed according to the characteristics and rehabilitation stage of the patient. In addition, the high methodological quality of the study design was ensured by clear criteria, randomization, blinding, and adequate sample size. These recommendations aim to advance the research and application of BCI in stroke rehabilitation.

Limitations: Our study is subject to several limitations. Firstly, the analysis was based on a limited number of randomized clinical trials (21 studies involving 886 patients). Secondly, for a robust subgroup meta-analysis, it is recommended to include at least 5 clinical trials in each subgroup [35]. In some of our subgroup analysis, this criterion was not met. Additionally, this study did not involve the interaction between drug use and BCI-based training. It is suggested that future clinical research should add more details of medication to provide a more meaningful reference for clinical practice. Lastly, as with any meta-analysis, the possibility of publication bias is a concern, as evidenced by the asymmetric appearance of the funnel plot in our analysis.

In summary, BCI-based training emerges as an effective strategy for upper-limb rehabilitation following a stroke. The combination of BCI with FES appears particularly promising, catering to patients in both the subacute and chronic phases. A training intensity of 20 to 90 min per day, 2–5 sessions per week for 3–4 weeks may be most recommended. In both research and clinical contexts, careful consideration of stroke phases and the selection of external feedback is crucial. Future investigations should prioritize enhancing BCI accuracy, and refining and standardizing study designs for BCI-based training.

Below is the link to the electronic supplementary material.

Supplementary Material 1