Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis

Abstract

Brain-computer interfaces (BCIs) can provide sensory feedback of ongoing brain oscillations, enabling stroke survivors to modulate their sensorimotor rhythms purposefully. A number of recent clinical studies indicate that repeated use of such BCIs might trigger neurological recovery and hence improvement in motor function. Here, we provide a first meta-analysis evaluating the clinical effectiveness of BCI-based post-stroke motor rehabilitation. Trials were identified using MEDLINE, CENTRAL, PEDro and by inspection of references in several review articles. We selected randomized controlled trials that used BCIs for post-stroke motor rehabilitation and provided motor impairment scores before and after the intervention. A random-effects inverse variance method was used to calculate the summary effect size. We initially identified 524 articles and, after removing duplicates, we screened titles and abstracts of 473 articles. We found 26 articles corresponding to BCI clinical trials, of these, there were nine studies that involved a total of 235 post-stroke survivors that fulfilled the inclusion criterion (randomized controlled trials that examined motor performance as an outcome measure) for the meta-analysis. Motor improvements, mostly quantified by the upper limb Fugl-Meyer Assessment (FMA-UE), exceeded the minimal clinically important difference (MCID=5.25) in six BCI studies, while such improvement was reached only in three control groups. Overall, the BCI training was associated with a standardized mean difference of 0.79 (95% CI: 0.37 to 1.20) in FMA-UE compared to control conditions, which is in the range of medium to large summary effect size. In addition, several studies indicated BCI-induced functional and structural neuroplasticity at a subclinical level. This suggests that BCI technology could be an effective intervention for post-stroke upper limb rehabilitation. However, more studies with larger sample size are required to increase the reliability of these results.

Figures

Figure 1

Illustration of typical brain‐computer interface (BCI) systems used in post‐stroke motor rehabilitation highlighting sensory feedback modalities. EEG = electroencephalography, NIRS = near‐infrared spectroscopy, ECoG = electrocorticography, SMR = sensorimotor rhythm, MRCP = motor‐related cortical potential.

Figure 2

Flow diagram of study selection.

Figure 3

Intervention effect measured as changes in upper‐extremity Fugl‐Meyer Assessment (FMA‐UE) scores between pre‐ and postintervention (standardized mean difference (SMD), Random‐Effects). The mean effect is represented as a diamond in the forest plot, whose width corresponds to the 95% CI, whereas the PI is shown as a bar superposed to the diamond. Box sizes reflect the contribution of the study toward the total intervention effect.

Figure 4

Subgroup Analysis 1: Standardized mean difference (SMD) of upper‐extremity Fugl‐Meyer Assessment (FMA‐UE) scores in the studies under the random‐effect assumption for the different interventions in the control group.

Figure 5

Subgroup Analysis 2: Standardized mean difference of upper‐extremity Fugl‐Meyer Assessment (FMA‐UE) scores in the studies under the random‐effect assumption. Studies are grouped into chronic and subacute phase.

Figure 6

Funnel plot showing the precision (standard error of standardized mean difference, SMD) against the effect size (SMD). The continuous vertical line shows the position of the overall combined effect, whereas dotted lines show pseudo 95% confidence limits.