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.

Exploring Self-Paced Embodiable Neurofeedback for Post-stroke Motor Rehabilitation

“…It is not clear, however, to what extent stroke patients in particular benefit from an EFS, since most paradigms have been tested only with healthy individuals. A recent meta-analysis conducted by Cervera et al (2018) showed that brain-computer interface (BCI)-based neurorehabilitation on upper-limb motor function can lead to more improvement in motor performance than other conventional therapies, supporting the general effectiveness of classical NF-MIT in stroke patients. The specific effect of EFS, however, has not yet been studied.…”

A Multi-Target Motor Imagery Training Using Bimodal EEG-fMRI Neurofeedback: A Pilot Study in Chronic Stroke Patients

“…Other recent studies have revealed the potential of NF and BCI training for stroke rehabilitation (Cervera et al, 2018), as an alternative or in addition to traditional therapies (Wang et al, 2017), to stimulate neural plasticity and support functional improvement (Grosse-Wentrup et al, 2011). Previous works have also shown that NF can enhance the efficacy of motor imagery training, in terms of eliciting brain patterns relevant to the task (Zich et al, 2015;Bagarinao et al, 2018).…”

Brain‐machine interface of upper limb recovery in stroke patients rehabilitation: A systematic review

“…The NIRS-based MI-BCI, even during a short period (six sessions), seemed to benefit acute stroke patients compared with sham intervention (Mihara et al, 2013). (Cervera et al, 2018). Besides those mentioned, several other studies with similar purposes have been conducted in stroke patients (Buch et al, 2008;Caria et al, 2011;Prasad, Herman, Coyle, McDonough, & Crosbie, 2010;Varkuti et al, 2013) and depicted some interesting findings.…”

“…The authors interpreted these findings by suggesting that BCI training strengthens neuroplasticity, leading to a better recovery, because MI through BCI involves the same neural mechanisms that control voluntary movement of the hand . Several neuroplasticity changes due to BCI such as increasing premotor area activity or enhancement of ipsilesional connectivity with changes in hemispheric EEG activity were mentioned (see Cervera et al, 2018). However, care must be considered Additionally, more updated literature stated that when relating the combination of real tDCS Kasashima-Shindo et al, 2015) or sham with 1-hr EEG-based MI-BCI to a robotic feedback, both groups improved functionality near the MCID of FMA.…”

“…Regarding visual feedback, the one used in the present review is predominantly abstract (e.g., the cue on a computer screen can be an arrow), contrary to others that have included realistic visual feedback (e.g., visual hand representation of an actor's perspective ;Cervera et al, 2018;Cincotti et al, 2012;Neuper, Scherer, Wriessnegger, & Pfurtscheller, 2009;Ono et al, 2014;Pichiorri et al, 2015). Realistic feedback, such as a virtual hand movement, might be preferable to more abstract feedback, given the possibility that its observation may itself lead to an activation of mirror neuron in the sensorimotor areas (Lucca, 2009;Pfurtscheller, Muller-Putz, Scherer, & Neuper, 2008).…”