Brain-computer interfaces for post-stroke motor rehabilitation

A meta-analysis

María A. Cervera, Surjo R. Soekadar, Junichi Ushiba, José del R. Millán, Meigen Liu, Niels Birbaumer, Gangadhar Garipelli

Research output: Contribution to journalArticle

19 Citations (Scopus)

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.

Original languageEnglish
JournalAnnals of Clinical and Translational Neurology
DOIs
Publication statusAccepted/In press - 2018 Jan 1

Fingerprint

Brain-Computer Interfaces
Meta-Analysis
Stroke
Upper Extremity
Randomized Controlled Trials
Stroke Rehabilitation
Sensory Feedback
Neuronal Plasticity
Reproducibility of Results
MEDLINE
Sample Size
Rehabilitation
Outcome Assessment (Health Care)
Clinical Trials
Technology
Control Groups

ASJC Scopus subject areas

  • Neuroscience(all)
  • Clinical Neurology

Cite this

Brain-computer interfaces for post-stroke motor rehabilitation : A meta-analysis. / Cervera, María A.; Soekadar, Surjo R.; Ushiba, Junichi; Millán, José del R.; Liu, Meigen; Birbaumer, Niels; Garipelli, Gangadhar.

In: Annals of Clinical and Translational Neurology, 01.01.2018.

Research output: Contribution to journalArticle

Cervera, María A. ; Soekadar, Surjo R. ; Ushiba, Junichi ; Millán, José del R. ; Liu, Meigen ; Birbaumer, Niels ; Garipelli, Gangadhar. / Brain-computer interfaces for post-stroke motor rehabilitation : A meta-analysis. In: Annals of Clinical and Translational Neurology. 2018.
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