The effect of brain–computer interface training on cognitive function in stroke patients: a systematic review and meta-analysis

Abstract

Objective

This study aims to assess the therapeutic impact of BCI-based interventions on global and domain-specific cognitive functions (attention, memory, and executive function), and activities of daily living in stroke survivors. Furthermore, we seek to identify the potential moderating effects of feedback modes and BCI paradigms on the overall rehabilitative efficacy.

Methods

A systematic search of PubMed, Embase, Web of Science, the Cochrane Library, and CNKI databases was conducted to identify eligible randomized-controlled trials (RCTs). Meta-analyses were performed by pooling standardized mean differences (SMDs) to synthesize effect sizes. To explore sources of heterogeneity and the effects of potential moderators, subgroup analyses were conducted according to outcome measures, stroke phase, BCI paradigm, and feedback type.

Results

Twelve studies were included. The meta-analysis demonstrated that BCI training significantly improved global cognitive function (SMD = 0.62, P < 0.00001), attention, and executive function, alongside enhanced activities of daily living performance. However, no significant improvement was observed in memory function. Subgroup analyses revealed that superior and more robust effects were associated with subacute patients, active BCI paradigms, and multimodal feedback (visual + auditory + proprioceptive).

Conclusion

BCI training is an effective intervention for post-stroke cognitive recovery. Early initiation of therapy and the integration of multimodal feedback appear to be critical factors for maximizing therapeutic outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00415-026-13915-w.

Introduction

As one of the leading causes of disability worldwide [1], the clinical management of stroke has shifted from acute-phase emergency care to long-term functional rehabilitation, following the widespread adoption of reperfusion therapies such as intravenous thrombolysis and endovascular thrombectomy. Conventional rehabilitation research has primarily focused on the restoration of motor function [2, 3]; however, clinical evidence indicates that the incidence of post-stroke cognitive impairment (PSCI) is extremely high [4]. Despite advancements in state-of-the-art stroke treatments, PSCI remains pervasive and significantly contributes to long-term morbidity [5]. Epidemiological studies indicate that approximately 35–80% of stroke survivors experience varying degrees of cognitive decline within 1 year of onset [4, 6]. This decline is characterized predominantly by deficits in attention, impaired executive function, memory disturbances, and reduced information processing speed [7, 8]. Early stage cognitive impairment following a stroke may serve as a potential predictor of persistent depressive states and limitations in both short-term and long-term participation in daily activities [9]. However, the long-term efficacy and side-effect profiles of current pharmacological interventions for PSCI, such as acetylcholinesterase inhibitors, remain subjects of clinical debate [10, 11]. Furthermore, traditional compensatory cognitive training often lacks targeted neurophysiological feedback, resulting in the inefficient induction of neural plasticity. Consequently, the exploration of novel neurorehabilitation techniques capable of directly intervening in brain neural activity and reshaping cognitive functional networks has become an urgent priority in the field of stroke rehabilitation.

In recent years, brain–computer interface (BCI) technology has emerged as a promising neurorehabilitation tool, demonstrating significant potential for application in the field of stroke. Compared with conventional rehabilitation modalities, such as physical therapy or passive electrical stimulation, the core advantage of BCI technology lies in its facilitation of “top–down” active neural engagement [12]. During BCI training, patients perform motor imagery (MI) or attempt to evoke specific neuroelectrophysiological signals, such as P300 event-related potentials or sensorimotor rhythms (SMR). The system decodes these signals in real time to drive external effectors—such as robotic exoskeletons, virtual reality feedback, or functional electrical stimulation (FES) [13]—thereby generating immediate, multimodal sensory feedback. This mechanism establishes an artificial “closed-loop” circuit that tightly couples the patient’s rehabilitative intent with real-time multimodal feedback. From a neurophysiological perspective, this synchrony substantially strengthens the functional connectivity between the damaged cortex and peripheral effectors. This alignment with Hebb’s Principle—“neurons that fire together, wire together”—effectively induces structural and functional remodeling of the central nervous system [14].

Furthermore, BCI possesses a distinct neuroanatomical foundation for the restoration of cognitive functions. Research demonstrates that performing BCI tasks not only activates the primary motor cortex (M1) [15] but also extensively engages neural networks primarily associated with attentional allocation and executive control, such as dorsolateral prefrontal cortex (dlPFC), posterior parietal cortex (PPC), and anterior cingulate cortex (ACC) [16, 17]. This anatomical and functional overlap offers a plausible mechanistic framework for BCI training to promote synergistic, cognitive–motor dual rehabilitation. During BCI training protocols involving mental tasks (such as motor imagery), patients are generally required to mobilize sustained attention and undergo rigorous neurofeedback regulation, which typically elicits the activation of the aforementioned cognition-related cortical networks [18]. Consequently, motor-oriented BCI interventions possess the potential to induce a cross-domain transfer of therapeutic benefits, thereby contributing to the observed cognitive gains [19].

Against this backdrop, while numerous studies and meta-analyses [12] have robustly demonstrated the efficacy of BCI in restoring upper-limb motor function after stroke, its independent effects on cognitive recovery have yet to be comprehensively and systematically evaluated. Clinical evidence regarding BCI’s impact on post-stroke cognition remains fragmented across small-scale studies, with often contradictory findings. Existing studies exhibit substantial variation in the selection of BCI paradigms, and different paradigms activate cognitive neural networks in distinctly different ways. Furthermore, the standardization of intervention dosage remains a critical challenge; it remains unclear how the frequency of weekly training sessions or the total duration of intervention quantitatively influences cognitive benefits, and whether a dose–response relationship similar to that observed in motor rehabilitation exists remains unknown [20]. Therefore, systematically synthesizing existing data and identifying key moderating variables influencing efficacy through meta-regression analysis holds significant scientific value and clinical relevance for optimizing BCI clinical intervention protocols and advancing their precision-based application in neurological rehabilitation.

Building upon this premise, the present study aims to quantitatively evaluate the impact of BCI training on overall cognitive function and specific cognitive domains in stroke patients via a meta-analytical approach. Furthermore, subgroup analyses and meta-regressions will be conducted to explore the moderating effects of clinical patient characteristics, BCI technical parameters, and intervention dosages on therapeutic outcomes. The findings of this study are expected to provide robust evidence-based support for the clinical application of BCI in post-stroke cognitive rehabilitation and offer critical insights into parameter optimization for the design of future BCI-based systems.

Methods

This systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement [21] and the guidelines outlined in the Cochrane Handbook for Systematic Reviews of Interventions [22]. The study protocol was prospectively registered with the International Prospective Register of Systematic Reviews (PROSPERO) under the registration number CRD420261350282, ensuring transparency and methodological rigor throughout the research process.

Search strategy

Two independent reviewers systematically searched PubMed, Embase, Web of Science, the Cochrane Library, and CNKI databases for randomized-controlled trials (RCTs) from their inception through March 26, 2026. The search strategy employed a combination of Medical Subject Headings (MeSH) and free-text terms, including “Stroke”, “Brain–Computer Interfaces”, “Neurofeedback”, “Cognition”, “Attention”, “Executive Function”, and “Memory”. Furthermore, the reference lists of the retrieved articles were manually screened to identify additional relevant studies. The comprehensive search strategy for all databases is detailed in Supplementary material S1.

Eligibility criteria and study selection

The inclusion criteria were established based on the PICOS framework as follows: Populations (P): Adult patients diagnosed with stroke who presented with concomitant cognitive impairment. Interventions (I): Utilization of Brain–Computer Interface (BCI) as a primary therapeutic intervention. Comparators (C): Control groups receiving conventional treatment, wait-list protocols, or sham/placebo interventions. Outcomes (O): Outcome measures included global cognitive function, specific cognitive domains (such as attention, memory, and executive function), neurophysiological indicators, and activities of daily living. Study designs (S): RCTs.

Studies were excluded if they met any of the following criteria: (1) conference abstracts, editorials, study protocols, expert consensuses, reviews, meta-analyses, and non-randomized-controlled trials (e.g., case reports, cross-sectional studies, or cohort studies); (2) studies that did not report any of the primary or secondary outcomes defined in this review; (3) studies with incomplete or unextractable data, where authors did not respond to contact; and (4) studies with a modified Physiotherapy Evidence Database (PEDro) scale score of less than 4. Literature retrieved from all databases was imported into EndNote software for deduplication. Subsequently, two researchers independently screened the titles, abstracts, and full texts to evaluate eligibility. Any discrepancies were resolved through consensus or by consultation with a third senior researcher.

Data extraction

Data were independently extracted by two researchers and recorded in a standardized Excel spreadsheet. The extracted information encompassed: first author, publication year, study design, sample size, stroke stage, intervention protocols, BCI paradigms, feedback types, intervention dosage, and outcome measures. Mean and standard deviation (SD) values for change scores from baseline were directly extracted when available; otherwise, they were calculated using the following formula based on the principles of the Cochrane Handbook for Systematic Reviews of Interventions [22]. For studies reporting outcomes as medians and interquartile ranges (IQRs), means and SDs were estimated using the method of Wan et al. [23]. To prevent unit-of-analysis errors, multiple intervention arms within a single study were merged into a single comparison group following Cochrane guidelines. Furthermore, if a study employed multiple scales to assess cognitive function, these were combined into a composite effect size. For reverse-scaled data (where higher scores indicate poorer outcomes), mean values were multiplied by − 1 to ensure consistency in the direction of effect across all studies

$${\text{Mean}}_{\text{change}}={\text{Mean}}_{\text{final}}-{\text{Mean}}_{\text{baseline}}$$

$${\text{SD}}_{\text{change}}=\sqrt{{\text{SD}}_{\text{baseline}}^2+{\text{SD}}_{\text{final}}^2-\left(2\times R\times {\text{SD}}_{\text{baseline}}\times {\text{SD}}_{\text{final}}\right)}.$$

Note: The value of R is 0.5 [22].

Quality assessment

The methodological quality of the included studies was evaluated using the Physiotherapy Evidence Database (PEDro) scale [24]. This 11-item scale assesses various aspects of trial quality; however, the first item (eligibility criteria) is not included in the total score calculation. The remaining 10 items are scored as either 1 (met) or 0 (not met), resulting in a maximum possible score of 10. Study quality was categorized based on the total scores: poor (less than 4), fair (4–5), good (6–8), and excellent (9–10) [25]. Quality assessment was performed independently by two researchers. Any discrepancies were resolved through discussion or, if necessary, by consultation with a third researcher to reach a consensus.

Statistical analysis

Statistical analyses were performed using Review Manager (version 5.4) and R software (version 4.5.2). As all outcome measures were continuous variables with diverse assessment methodologies and units, the standardized mean difference (SMD) and 95% confidence intervals (CIs) were used for data synthesis. Statistical heterogeneity was assessed using the I² statistic. A fixed-effects model was employed when I² ≤ 50%, whereas a random-effects model was applied when I² > 50%. Heterogeneity was classified as negligible (I² < 25%), low (25%–50%), moderate (50%–75%), or high (> 75%) [26]. To address significant heterogeneity (I² > 50%), a leave-one-out sensitivity analysis was performed; heterogeneity was deemed acceptable if the I² value subsequently fell to ≤ 50%. Publication bias was appraised via funnel plots and Egger’s test. Meta-analysis was only conducted for outcomes reported in at least two independent studies.

Results

Search results

A total of 2625 records were identified through systematic database searching, of which 12 studies [27–38] were ultimately included in the meta-analysis. The literature screening process is illustrated in the PRISMA flow diagram (Fig. 1), and the primary characteristics of the included studies are summarized in Table 1. Additionally, to provide a comprehensive overview of the specific components of the BCI interventions, the relevant characteristics of all included trials are systematically detailed in Table 2. Notably, while Chen et al. [37] provided complete data for activities of daily living (MBI) outcomes, their reporting of the Montreal Cognitive Assessment (MoCA) was incomplete, providing only the mean and SD of change scores for the intervention group. As the SD for the control group was missing and further clarification from the authors was unavailable, the findings from Chen et al. [37] were selectively included only in subgroup analyses for which data were sufficient.

Fig. 1.

PRISMA flowchart of study selection

Table 1.

Characteristics of the included studies

Study	Study design	Stroke phase	Age (years)	Sample size (E/C)	Experimental interventions	Control interventions	BCI paradigm	Feedback type	Neuroimaging modality	Dosage (min/freq/week)	Outcome
Fateeva, 2023 [27]	Double-arm RCT	Subacute	E: 57.4 ± 6.9 C: 57.0 ± 6.5	E: 14 C: 15	P300-BCI	General rehabilitation	P300-BCI	Visual	EEG	60/10/1	MoCA
Wan, 2025 [28]	Double-arm RCT	Subacute and chronic	E: 57.07 ± 11.65 C: 56.25 ± 15.19	E: 14 C: 16	MI + VR–BCI + General rehabilitation	General rehabilitation	MI-BCI	Visual + Proprioceptive	EEG	20/5/4	SDMT
Zhao, 2022 [29]	Double-arm RCT	Subacute	53.1 ± 11.5	E: 14 C: 14	BCI-robot + General rehabilitation	Sham BCI + General rehabilitation	SSMVEP-BCI	Visual + Proprioceptive	EEG	30/6/4	LOTCA MBI
Lu, 2025 [30]	Double-arm RCT	Chronic	E: 62.17 ± 5.27 C: 61.34 ± 5.36	E: 21 C: 19	BCI + Acupuncture + General rehabilitation	Acupuncture + General rehabilitation	MI-BCI	Visual + Proprioceptive	EEG	20/6/8	MoCA MBI
Yang, 2023 [31]	Double-arm RCT	Subacute	E: 55.72 ± 11.55 C: 50.92 ± 10.43	E: 25 C: 25	BCI-visual feedback + General rehabilitation	General rehabilitation	MI-BCI	Visual + Auditory + Proprioceptive	EEG	20/5/2	MoCA MBI
Gao, 2023 [32]	Three-arm RCT	Subacute	E: 53.94 ± 11.48 C(MI): 54.63 ± 11.11 C: 55.71 ± 12.54	E: 32 C: 32/34	MI-BCI + General rehabilitation	MI + General rehabilitation/General rehabilitation	MI-BCI	Visual + Auditory + Proprioceptive	EEG	30/12/2	MMSE MBI
Huang, 2024 [33]	Double-arm RCT	Subacute	E: 66.80 ± 4.41 C: 64.73 ± 4.48	E: 15 C: 15	BCI-visual feedback + General rehabilitation	General rehabilitation	MI-BCI	Visual + Auditory + Proprioceptive	EEG	20/5/2	MoCA MMSE DST SDMT
Isakova, 2026 [34]	Three-arm RCT	Subacute and chronic	P300-BCI: 59.7 ± 12.3 MI-BCI: 60.7 ± 9.2 C: 65.4 ± 8.0	E: 37/33 C: 19	P300-BCI + General rehabilitation/MI-BCI + General rehabilitation	Computer-based cognitive training + General rehabilitation	P300-BCI/MI-BCI	Visual/Visual + Proprioceptive	EEG	25/9/1	MoCA Schulte Grid Tracking test; Kohs blocks; Tracking test-A; Luria “10 words”
Kotov, 2022 [35]	Three-arm RCT	Subacute and chronic	P300-BCI: 61 [50,68] MI-BCI: 57 [48,67] C: 66 [57,69]	E: 10/13 C: 11	P300-BCI/MI-BCI	Computer-based cognitive training	P300-BCI/MI-BCI	Visual/Visual + Proprioceptive	EEG	30/9/1	MoCA CDT Luria “10 words”
Liu, 2023 [36]	Double-arm RCT	Subacute	E: 52.5 [45.0,59.3] C: 53.0 [38.5,59.5]	E: 30 C: 30	MI‑BCI + FES + General rehabilitation	FES + General rehabilitation	MI‑BCI	Visual + Auditory + Proprioceptive	EEG	20/5/3	ANT Schulte Grid Tracking test SDMT MBI
Chen, 2025 [37]	Double-arm RCT	Subacute and chronic	60–90	E: 12 C: 12	BCI-FES	FES	MI-BCI	Visual + Proprioceptive	EEG	E: 40/3/8 C: 30/3/8	MBI
Chung, 2015 [38]	Double-arm RCT	Chronic	E: 43.6 ± 10.9 C: 50.2 ± 7.1	E: 5 C: 5	BCI-FES	FES	MI-BCI	Visual + Proprioceptive	EEG	30/5/1	Attention EEG

E experimental group, C control group, BCI brain–computer interface, Dosage (min/freq/week) duration per session (min), frequency (times per week), and total duration (weeks), RCT randomized controlled trial, P300 event-related potential P300, EEG electroencephalography, MoCA Montreal Cognitive Assessment, MI motor imagery, VR virtual reality, SDMT Symbol Digit Modalities Test, SSMVEP steady-state motion visual evoked potential, LOTCA Loewenstein Occupational Therapy Cognitive Assessment, MBI Modified Barthel Index, MMSE Mini-mental State Examination, DST Digit Span Test, CDT Clock Drawing Test, FES functional electrical stimulation, ANT Attention Network Test, Attention EEG attention index by EEG

Table 2.

Technical configurations and outcome attributes of included BCI interventions

Study	BCI paradigm	Mental task	Feedback type and devices	Outcomes of original study (P/S)	Cognitive outcome measures
Fateeva, 2023 [27]	P300-BCI	Spelling task: Patients sequentially fixated on flashing letters to spell words (gradually increasing from 3 letters). Selected letters appeared in the input line with a 5-s transition interval. Protocol required a minimum of 3 words per session	Visual: Target letter highlighting and real-time input display Devices: 8-channel EEG headset (monopolar, right earlobe reference) and LCD monitor matrix	P: MoCA (total and domain scores) S: Number of correct letters per session	MoCA
Wan, 2025 [28]	MI-BCI	Dual-task MI task: Simultaneous motor imagery of lower-limb pedaling and upper-limb interactive VR gameplay (e.g., steering and task completion)	Visual + proprioceptive: Real-time attention index, game score, and immersive VR env. Devices: ZhenTec R1 lower-limb pedaling robot, ZhenTec NH1 EEG system (prefrontal α/β bands), VR controller, and monitor	P: BBS S: TUGT, FMA-LE, SDMT	SDMT
Zhao, 2022 [29]	SSMVEP-BCI	SSVEP-based visual fixation: Patients gazed at Newton’s rings oscillating at specific frequencies to trigger corresponding gait commands (e.g., locomotion, speed control). Training advanced through multiple difficulty levels (e.g., turning, obstacle avoidance)	Visual + proprioceptive: Real-time on-screen avatar and synchronized robotic motion Devices: Lower-limb training robot, EEG acquisition system (g.USBamp, 10–20 system electrodes: O1, O2, Oz, PO3, PO4, POz), Newton’s ring stimulator	P: LOTCA; FMA-LE S: FMA-B; FAC; MBI; Serum BDNF; MEP latency	LOTCA
Lu, 2025 [30]	MI-BCI	Adaptive EEG modulation task: Patients actively focused attention or imagined lower-limb movements to achieve target control accuracy, with training difficulty dynamically adjusted based on real-time performance	Visual + proprioceptive: Real-time accuracy scores and performance-based robotic speed adjustments Devices: L-B300 BCI system (EEG cap, amplifier, host PC with evaluation software) integrated with a lower-limb training device (adjustable speed: 5–45 r/min)	P: FMA; MoCA; MBI S: Serum biomarkers; Middle cerebral artery flow velocity; Clinical efficacy rate	MoCA
Yang, 2023 [31]	MI-BCI	Cued MI task: 2-s visual cue followed by 4-s focused hand-grasping imagery (without overt movement). Exceeding a pre-set brain engagement threshold dynamically triggered robotic hand assistance	Visual + auditory + proprioceptive: VR environment tasks, auditory encouragement, and soft-robot-assisted hand grasping Devices: 32-channel saline-electrode EEG system (ZhenTec NT1; CPz reference, Fpz ground), soft robotic hand rehabilitation glove, and VR training platform	P: FMA-UE; MoCA; MBI S: Brain engagement index	MoCA
Gao, 2023 [32]	MI-BCI	Multi-limb MI task: Alternating upper-limb (swimming arm strokes) and lower-limb (bicycle pedaling) MI. Upon exceeding a pre-set MScore threshold, synchronized visual (virtual avatar) and kinesthetic (robot-driven limbs) feedback was dynamically triggered	Visual + auditory + proprioceptive: Immersive VR avatar, voice prompts, and robot-driven limb movement Devices: L-B300 EEG system (8 channels: FP1/2, F3/4, C3/4, Fz, Cz; A1 reference, A2 bias), MOTOmed upper/lower limb training robot, and monitor	P: MMSE; FMA-UE; FMA-LE; HAMD S: EQ-5D; 6MWD; MBI; MEP latency; MEP amplitude	MMSE
Huang, 2024 [33]	MI-BCI	Dual-task MI task: Focused lower-limb pedaling imagery monitored via prefrontal EEG (Fp1). Exceeding a pre-set brain engagement threshold triggered robotic leg driving, while patients concurrently steered handlebars for upper-limb cognitive-motor tasks (e.g., target collection) in a virtual env	Visual + auditory + proprioceptive: Gamified VR interface (avatar, route, score, real-time brain engagement), music/voice prompts, and robotic lower-limb pedaling Devices: ZhenTec NT1 32-channel saline-electrode EEG system, VR mountain-cycling platform, and a pedal-driven rehabilitation robot	P: FMA-LE; MoCA; MMSE; SDMT S: DST; Brain engagement index	MoCA; MMSE; SDMT; DST
Isakova, 2026 [34]	P300-BCI/MI-BCI	P300 oddball task: Target stimuli discrimination within an interactive visual oddball paradigm (image matching, virtual typing, and question answering) MI task: The screen displays tasks to the patient at 10-s intervals: imagining opening the right or left hand, and the process of relaxing	Visual/visual + proprioceptive: (1) P300 group: visual letter output/image manipulation; (2) MI group: exoskeleton-driven hand flexion/extension Devices: (1) P300 group: Neurochat BCI + BFB system; (2) MI group: Ekzokist-2 (Exohand-2) BCI + BFB system	P: MoCA S: Tracking test-A; Schulte Grid Test; Kohs blocks; Luria “10 words”	MoCA Tracking test-A; Schulte Grid Test; Kohs blocks; Luria “10 words”
Kotov, 2022 [35]	P300-BCI/MI-BCI	P300 oddball task: Target stimuli discrimination within an interactive visual oddball paradigm (image matching, virtual typing, and question answering) MI task: The screen displays tasks to the patient at 10-s intervals: imagining opening the right or left hand, and the process of relaxing	Visual/visual + proprioceptive: (1) P300 group: visual letter output/image manipulation; (2) MI group: exoskeleton-driven hand flexion/extension Devices: (1) P300 group: Neurochat BCI; (2) MI group: Ekzokist-2 (Exohand-2) BCI	P: MoCA S: CDT; Luria “10 words”	MoCA CDT Luria “10 words”
Liu, 2023 [36]	MI-BCI	Individualized MI task: Individualized upper-limb motor imagery tailored to patients’ functional status, including shoulder abduction, upper arm adduction, forearm flexion, wrist dorsiflexion, fist clenching, and wrist inversion	Visual + auditory + proprioceptive: Animated motion cues, voice prompts, and FES-induced muscle contractions Devices: LSR-AII BCI system (14-channel EEG cap with 2 references per 10–20 system, amplifier, host PC) and a FES	P: FMA-UE; ANT S: WMFT; Schulte Grid Test; SDMT	ANT Schulte Grid Test SDMT
Chen, 2025 [37]	MI-BCI	MI task: Motor imagery of wrist dorsiflexion (extension) and flexion. Patients imagine performing wrist movements while visual feedback (virtual hand) and auditory cues are presented. Successful MI detection triggers FES to produce actual movement	Visual + proprioceptive: Virtual hand proportionally driven by MI intensity, alongside FES-evoked muscle contraction Devices: NCERP Series D EEG amplifier (24-channel, 10–20 system), commercial FES stimulator	P: FMA-UE; MBI; MoCA S: Neurophysiological biomarkers	MoCA
Chung, 2015 [38]	MI-BCI	MI task: Focused attention on ankle dorsiflexion movement on a monitor screen	Visual + proprioceptive: When attention index exceeded threshold, FES activated to produce ankle dorsiflexion Devices: Poly-G I EEG system and Microstim Ω FES	P: Attention EEG; Activation EEG S: –	Attention EEG

P primary, S Secondary, LCD liquid crystal display, MoCA Montreal Cognitive Assessment, MI motor imagery, VR virtual reality, BBS Berg Balance Scale, TUGT timed up and go test, FMA-LE Fugl-Meyer Lower Extremity Assessment, SDMT Symbol Digit Modalities Test, SSMVEP steady-state motion visual evoked potential, LOTCA Loewenstein Occupational Therapy Cognitive Assessment, FMA-LE Fugl-Meyer Assessment for Lower Extremity, FMA-B Fugl-Meyer Assessment for Balance, FAC functional ambulation category, MBI Modified Barthel Index, BDNF brain-derived neurotrophic factor, MEP motor-evoked potential, FMA-UE Fugl-Meyer Assessment-Upper Extremities, MMSE Mini-Mental State Examination, HAMD Hamilton Depression Scale, EQ-5D EuroQol-5D visual analogue scale, 6MWD 6-min walking distance, DST Digit Span Test, BFB Biofeedback, CDT Clock Drawing Test, FES Functional Electrical Stimulator, ANT Attention Network Test, WMFT Wolf Motor Function Test

Quality assessment

All included studies were evaluated using the PEDro scale, with total scores ranging from 4 to 8 and a mean score of 5.75. These findings suggest that the included literature overall exhibits a moderate-to-high level of methodological rigor. The comprehensive results of the quality assessment for each study are provided in Table 3.

Table 3.

PEDro scale for assessing the methodological quality of the included studies

Study	Eligibility criteria specified	Randomly allocated	Allocation was concealed	Baseline comparability	Blinding of subjects	Blinding of therapists	Blinding of assessors	Adequate follow-up	Intention of treat analysis	Between-group comparisons	Points estimates and variability	Total scores
Chung, 2015 [38]	1	1	0	0	0	0	0	1	1	1	1	5
Fateeva, 2023 [27]	1	1	0	1	0	0	0	1	0	1	1	5
Liu, 2023 [36]	1	1	1	1	0	0	1	1	1	1	1	8
Wan, 2025 [28]	1	1	1	1	0	0	1	0	0	1	1	6
Isakova, 2026 [34]	1	1	0	1	0	0	0	1	0	1	1	5
Zhao, 2022 [29]	1	1	1	1	0	0	1	1	0	1	1	7
Chen, 2025 [37]	1	1	0	1	0	0	0	1	1	1	1	6
Kotov, 2022 [35]	1	0	0	1	0	0	0	1	0	1	1	4
Gao, 2023 [32]	1	1	0	1	0	0	0	1	1	1	1	6
Huang, 2024 [33]	1	1	0	1	0	0	0	1	1	1	1	6
Lu, 2025 [30]	1	0	0	1	0	0	0	1	1	1	1	5
Yang, 2023 [31]	1	1	0	1	0	0	0	1	1	1	1	6

Global cognitive function

Eleven studies reported data on cognitive function. The meta-analysis demonstrated that BCI-based training significantly improved global cognitive function in stroke patients (SMD = 0.62; 95% CI 0.43–0.81; P < 0.00001), as illustrated in the forest plot (Fig. 2). Given the low statistical heterogeneity observed across the studies (I² = 22%, P = 0.23), a fixed-effects model was utilized for the pooled analysis.

Fig. 2.

Forest plot of a meta-analysis on global cognitive function

Publication bias

Publication bias was qualitatively assessed via a funnel plot (Fig. 3), which exhibited a generally symmetrical distribution upon visual inspection. Furthermore, Egger’s linear regression test was employed to provide a quantitative evaluation of potential bias. The results indicated that the regression intercept did not deviate significantly from zero (t = 0.48, df = 8, P = 0.642), suggesting no substantial risk of publication bias in the current meta-analysis.

Fig. 3.

Publication bias funnel plot

Subgroup analysis

Outcome measures

Subgroup analyses based on different outcome measures demonstrated that BCI-based training significantly improved cognitive scores (MoCA/MMSE: SMD = 0.56; 95% CI 0.24–0.89; P = 0.0006), with low-to-moderate heterogeneity (I² = 49%, P = 0.07). Significant therapeutic effects were also identified for attention (SMD = 1.52; 95% CI 0.17–2.88; P = 0.03; I² = 89%, P < 0.00001), executive function (SMD = 0.90; 95% CI 0.08–1.71; P = 0.03; I² = 86%, P < 0.00001), and activities of daily living (MBI: SMD = 1.39; 95% CI 0.30–2.48; P = 0.01; I² = 92%, P < 0.00001). Conversely, no significant improvement was observed in memory function (SMD = -0.37; 95% CI − 1.29 to 0.54; P = 0.43; I² = 82%, P = 0.004). These findings are illustrated in Fig. 4.

Fig. 4.

Forest plot of subgroup analyses based on different outcome measures

Stroke phase

Compared with the control groups, our subgroup analysis revealed that BCI-based training exerted a significant positive effect on global cognitive function in patients during the subacute stage (SMD = 0.74; 95% CI 0.50–0.98; P < 0.00001), with negligible statistical heterogeneity (I² = 0%, P = 0.55). A significant improvement was also observed in mixed populations comprising both subacute and chronic patients (SMD = 0.47; 95% CI 0.10–0.84; P = 0.01; I² = 61%, P = 0.08). Conversely, BCI-based training yielded no significant cognitive benefits for patients exclusively in the chronic stage (SMD = 0.31; 95% CI − 0.25 to 0.88; P = 0.27; I² = 0%, P = 0.32). These results are illustrated in Fig. 5.

Fig. 5.

Forest plot of subgroup analyses based on stroke phase

BCI paradigm

The results demonstrate that both active and passive BCI training significantly improved global cognitive function in stroke patients compared with control groups (Active BCI: SMD = 0.59, 95% CI 0.38–0.79, P < 0.00001; Passive BCI: SMD = 0.61, 95% CI 0.25–0.97, P = 0.0009). Statistical heterogeneity in the active BCI subgroup was negligible (I² = 0%, P = 0.48), underscoring the robustness of the findings. In contrast, the passive BCI subgroup exhibited moderate heterogeneity (I² = 60%, P = 0.06). These subgroup comparisons are illustrated in Fig. 6.

Fig. 6.

Forest plot of subgroup analyses based on the BCI paradigm

Feedback type

Subgroup analysis based on feedback modalities indicated that all types of BCI interventions exerted a positive influence on the global cognitive function of stroke patients. Notably, the multimodal feedback combination of “visual, auditory, and proprioceptive” stimuli provided the most robust evidence of efficacy (SMD = 0.67; 95% CI 0.41–0.94; P < 0.00001). Within this specific subgroup, statistical heterogeneity was negligible (I² = 0%, P = 0.82), indicating high consistency across the included studies. These findings are illustrated in Fig. 7.

Fig. 7.

Forest plot of subgroup analyses by feedback type

Dose–response analysis

Meta-regression analysis indicated that neither the total number of sessions (β = − 0.0027, P = 0.7554) nor the cumulative training duration (β = 0.0094, P = 0.6331) acted as significant predictors of the overall effect size. However, the bubble plot for cumulative training duration (Fig. 8B) demonstrated that the improvement in cognitive function (SMD) exhibited an upward trend with increasing total training time. This observation implies that training duration might be a key potential moderator of BCI efficacy, suggesting a possible dose–response relationship that necessitates further validation through large-scale, high-quality studies in the future.

Fig. 8.

Meta-regression bubble plot shows the relationship between the effect size (standardized mean difference) and the total number of sessions (A) and the cumulative training time (B). In each subplot, the straight line shows the regression line and the curves around it show the 95% confidence interval

Discussion

This meta-analysis investigated the impact of BCI-based training on cognitive recovery following a stroke. The results demonstrate that BCI interventions significantly enhance global cognitive function, as well as specific cognitive domains including attention and executive function, and overall activities of daily living. Furthermore, the therapeutic efficacy of BCI training was found to vary depending on the stroke stage, BCI paradigm, feedback modality, and intervention intensity.

The findings of this study confirm that BCI-based interventions significantly enhance the global cognitive profile of stroke patients. BCI possesses the unique capacity to transcend the boundaries of functional impairments, inducing widespread neural plastic changes [14]. The fundamental mechanism underlying this effect is that BCI effectively bridges the disconnection between intention and execution—a critical bottleneck in traditional rehabilitation. By coupling endogenous electroencephalogram (EEG) signals with real-time multimodal feedback [39], BCI reinforces the brain’s internal feedforward and feedback control mechanisms. This closed-loop interaction not only reactivates damaged motor pathways but, more crucially, tends to obligatorily recruit the frontoparietal cognitive network responsible for monitoring, error detection, and sustained attention [18]. Consequently, this leads to a “transfer effect”, translating motor-induced neural activity into broad-spectrum cognitive enhancement. As delineated in the technical configurations of our reviewed studies (Table 2), BCI training represents not merely a motor task, but a cognitive workload highly dependent on sustained attention, working memory, and executive function. To effectively drive the external devices, stroke patients must intensively engage cognitive control networks, such as the parietal association cortex and the prefrontal cortex. Notably, while most included studies designated cognition as a primary outcome, one specific trial explored [28] it as a secondary outcome following a motor-targeted intervention. The lack of rigorous statistical adjustments for multiple comparisons in this original study could potentially inflate the risk of a Type I error [40]. Furthermore, the majority of trials within the pool utilized global cognitive screening scales, such as the MoCA or MMSE, which might exhibit insufficient sensitivity to detect subtle, domain-specific cognitive changes triggered by the intervention. Consequently, future high-quality clinical trials with specific cognitive domains pre-specified as primary outcomes are warranted to further substantiate this “transfer effect”.

Subgroup analyses revealed that while attention, executive function, and activities of daily living were significantly improved, memory function showed no significant enhancement. This outcome is highly consistent with the intrinsic nature of BCI training, which requires patients to maintain sustained attention and perform complex task-switching. These cognitive demands essentially provide direct training for the Executive Control Network (ECN) [18]. As noted by Li et al. [12], the therapeutic efficacy of BCI depends heavily on the precise activation of specific target brain regions. Current BCI paradigms primarily rely on signals from the sensorimotor cortex (SMC) [41]; the anatomical location of the SMC is relatively distant from the hippocampal circuits that govern long-term memory [42]. This lack of anatomical and functional proximity may partially explain why BCI interventions in the current studies yielded non-significant improvements in memory function.

Our results indicate that BCI training yielded significant cognitive improvements in subacute stroke patients, while its effects on chronic patients did not reach statistical significance. In the early stages post-stroke (typically within 6 months), the brain remains in a highly primed neurochemical state [43]. During this period, the precise neural feedback provided by BCI can more effectively synergize with spontaneous neuroplastic processes to facilitate recovery. Furthermore, although meta-regression did not identify a statistically significant linear predictive relationship between cumulative training duration and effect size, the upward trend illustrated in the bubble plot hints at a potential dose–response relationship. This aligns with the perspective of Lohse et al. [20], who argued that rehabilitative gains often require reaching a specific threshold of training intensity and dosage before becoming clinically manifest.

The present study demonstrates that both active and passive BCI paradigms significantly improve cognitive function in stroke patients; nonetheless, the results for passive BCI were associated with higher statistical heterogeneity. Similarly, while both unimodal and multimodal feedback modalities were effective, studies utilizing unimodal feedback exhibited more pronounced inter-study heterogeneity. These discrepancies suggest that while the therapeutic potential exists across various protocols, the robustness of the evidence for passive and unimodal BCI remains limited. Therefore, the long-term efficacy and optimal configurations of these specific BCI training modes necessitate further validation through more rigorously designed, high-quality randomized-controlled trials (RCTs) with larger sample sizes.

Limitations

Several limitations of the present study should be acknowledged: (1) the intervention protocols in the control groups varied considerably across the included studies—ranging from sham BCI and functional electrical stimulation (FES) alone to conventional rehabilitation—which may have contributed to inter-study heterogeneity; (2) the small number of studies in certain subgroup analyses might have led to insufficient statistical power; thus, conclusions regarding these specific domains should be interpreted with caution; (3) in the meta-regression, the total number of sessions in most studies was clustered between 10 and 20. This restricted variance in the independent variables may have hindered the model’s ability to detect a significant relationship. Future high-quality studies are warranted to further delineate the optimal frequency and temporal thresholds for BCI-mediated cognitive recovery; (4) given that a minority of the included studies explored cognition merely as a secondary outcome for exploratory analysis without rigorous statistical adjustments for multiple comparisons, readers should remain prudent when interpreting these secondary data; and (5) since the majority of the trials utilized global cognitive screening tools such as the MoCA or MMSE, these scales might exhibit insufficient sensitivity to capture the subtle neuroplastic changes triggered by BCI interventions, thereby potentially underestimating the true therapeutic efficacy of the technology.

Conclusion

In conclusion, current evidence suggests that BCI-based training serves as a potent intervention for significantly improving cognitive function in stroke survivors. To maximize therapeutic efficacy, initiating BCI intervention during the subacute or early stages of stroke is recommended. Moreover, our findings hint at a potential dose–response relationship, where rehabilitative gains may increase alongside cumulative training duration. Future studies should prioritize large-scale randomized-controlled trials to establish standardized clinical protocols and define optimal treatment dosages. Furthermore, the development of next-generation BCI paradigms that specifically target memory-related neural circuits remains a critical direction for expanding the scope of post-stroke cognitive recovery.

Supplementary Information

Below is the link to the electronic supplementary material.

Funding

This review was supported by the National Natural Science Foundation of China (82471345), and Clinical Trials from the Affiliated Drum Tower Hospital, Medical School of Nanjing University (2024-LCYJ-MS-17), and Aid project of Jiangsu Ningai Medical Development & Medical Aid Foundation (NDYGN2025005).

Availability of data and materials

The datasets supporting the conclusions of this article are included within the article.

Declarations

Conflicts of interest

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.