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

Abstract

Background Technologies such as brain‐computer interfaces are able to guide mental practice, in particular motor imagery performance, to promote recovery in stroke patients, as a combined approach to conventional therapy. Objective The aim of this systematic review was to provide a status report regarding advances in brain‐computer interface, focusing in particular in upper limb motor recovery. Methods The databases PubMed, Scopus, and PEDro were systematically searched for articles published between January 2010 and December 2017. The selected studies were randomized controlled trials involving brain‐computer interface interventions in stroke patients, with upper limb assessment as primary outcome measures. Reviewers independently extracted data and assessed the methodological quality of the trials, using the PEDro methodologic rating scale. Results From 309 titles, we included nine studies with high quality (PEDro ≥ 6). We found that the most common interface used was non‐invasive electroencephalography, and the main neurofeedback, in stroke rehabilitation, was usually visual abstract or a combination with the control of an orthosis/robotic limb. Moreover, the Fugl–Meyer Assessment Scale was a major outcome measure in eight out of nine studies. In addition, the benefits of functional electric stimulation associated to an interface were found in three studies. Conclusions Neurofeedback training with brain‐computer interface systems seem to promote clinical and neurophysiologic changes in stroke patients, in particular those with long‐term efficacy.

Effect of brain-computer interface training based on non-invasive electroencephalography using motor imagery on functional recovery after stroke - a systematic review and meta-analysis

“…BCI training (BCIT) systems can use EEG signals from MI performance with sensory real-time feedback and decode these signals to enable patients to direct devices such as personal computers, wheelchairs, robots, and prosthetic devices including exoskeletons. Some studies have investigated the efficacy of applying BCIT using MI on motor recovery for patients with subacute or chronic stroke with hemiparesis [3,[11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Although these studies demonstrated a significant effect on recovery, the studies were flawed by low number of participants, low number of training sessions and/or a lack of follow-up assessments [3,[11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”

Why we should systematically assess, control and report somatosensory impairments in BCI-based motor rehabilitation after stroke studies

“…Therefore, we investigated how somatosensory abilities were taken into account and reported in the literature on BCI-based motor rehabilitation post-stroke. We assessed the papers that were cited in reviews focusing on BCI-based motor rehabilitation of the upper-limb ( Remsik et al, 2016 , Monge-Pereira et al, 2017 , Cervera et al, 2018 , Carvalho et al, 2019 , Bai et al, 2020 ). From the different papers cited we selected the 14 papers focusing on Randomized Clinical Trials (RCTs) of BCIs based on sensorimotor rhythms for post-stroke motor rehabilitation of the upper-limbs with different clinical trial registration number ( Ang et al, 2009 , Ang et al, 2010 , Ang et al, 2014 , Ang et al, 2015 , Biasiucci et al, 2018 , Frolov et al, 2017 , Li et al, 2014 , Mihara et al, 2013 , Pichiorri et al, 2015 , Ramos-Murguialday et al, 2013 , Rayegani et al, 2014 , Várkuti et al, 2013 , Wada et al, 2019 , Young et al, 2016 ).…”

Rehabilitation nach Schlaganfall: Durch Gehirn-Computer-Schnittstelle vermittelte funktionelle Elektrostimulation

“…Tiefere Einblicke in die Mechanismen, die einer Erholung zu Grunde liegen, könnten zu weiteren Fortschritten führen [27]. Die Präzision der Klassifizierung neurophysiologischer Aktivität durch das BCI könnte dabei eine wichtige Rolle spielen [28]. Eine relevante neurophysiologische Grundlage der Klassifizierung von Hirnsignalen könnte einen positiven Einfluss auf die Modulation der neuronalen Plastizität haben.…”