Abstract
Background
A substantial number of clinical studies have demonstrated the functional recovery induced by the use of brain-computer interface (BCI) technology in patients after stroke. The objective of this review is to evaluate the effect sizes of clinical studies investigating the use of BCIs in restoring upper extremity function after stroke and the potentiating effect of transcranial direct current stimulation (tDCS) on BCI training for motor recovery.
Methods
The databases (PubMed, Medline, EMBASE, CINAHL, CENTRAL, PsycINFO, and PEDro) were systematically searched for eligible single-group or clinical controlled studies regarding the effects of BCIs in hemiparetic upper extremity recovery after stroke. Single-group studies were qualitatively described, but only controlled-trial studies were included in the meta-analysis. The PEDro scale was used to assess the methodological quality of the controlled studies. A meta-analysis of upper extremity function was performed by pooling the standardized mean difference (SMD). Subgroup meta-analyses regarding the use of external devices in combination with the application of BCIs were also carried out. We summarized the neural mechanism of the use of BCIs on stroke.
Results
A total of 1015 records were screened. Eighteen single-group studies and 15 controlled studies were included. The studies showed that BCIs seem to be safe for patients with stroke. The single-group studies consistently showed a trend that suggested BCIs were effective in improving upper extremity function. The meta-analysis (of 12 studies) showed a medium effect size favoring BCIs for improving upper extremity function after intervention (SMD = 0.42; 95% CI = 0.18–0.66; I2 = 48%; P < 0.001; fixed-effects model), while the long-term effect (five studies) was not significant (SMD = 0.12; 95% CI = − 0.28 – 0.52; I2 = 0%; P = 0.540; fixed-effects model). A subgroup meta-analysis indicated that using functional electrical stimulation as the external device in BCI training was more effective than using other devices (P = 0.010). Using movement attempts as the trigger task in BCI training appears to be more effective than using motor imagery (P = 0.070). The use of tDCS (two studies) could not further facilitate the effects of BCI training to restore upper extremity motor function (SMD = − 0.30; 95% CI = − 0.96 – 0.36; I2 = 0%; P = 0.370; fixed-effects model).
Conclusion
The use of BCIs has significant immediate effects on the improvement of hemiparetic upper extremity function in patients after stroke, but the limited number of studies does not support its long-term effects. BCIs combined with functional electrical stimulation may be a better combination for functional recovery than other kinds of neural feedback. The mechanism for functional recovery may be attributed to the activation of the ipsilesional premotor and sensorimotor cortical network.
Background
Motor deficit is the most common sequela after stroke, resulting in severe negative impacts on activities of daily living and social participation for patients [1]. Spontaneous recovery usually occurs within the first 3 months after the onset of stroke; however, there exists a great deal of variability in recovery across patients, particularly patients with severe deficits, who tend to recover less and more slowly [2]. With regard to the importance of motor training in facilitating motor recovery after stroke, various rehabilitation training protocols, such as task-specific training and constrained-induced motor training have been applied in regard to stroke [3, 4]. However, these protocols are limited in patients with severe motor function deficit, due to the voluntary participation of hemiparetic hands. On the other hand, brain-computer interface (BCI) technology does not involve the direct volitional control of hemiparetic hands in training; therefore, it may be promising for these patients.
The term “BCIs” refers to systems that capture the features of brain activity and translate them into computerized commands to control external devices, which can be communication devices [5], functional electrical stimulation (FES) [6], or exoskeleton robots [7], among others. To acquire brain activity signals, either invasive or non-invasive strategies can be used. Invasive BCIs can acquire spatiotemporal signals and have a great capacity to distinguish more dimensions of patients’ intent through implants in the brain cortex [8]. However, non-invasive BCIs, using signals collected from electroencephalography (EEG), magnetoencephalography (MEG), functional near-infrared spectroscopy (fNIRS), or functional magnetic resonance imaging (fMRI), may be more promising than the invasive strategy in reality, due to safety and ethical issues [9]. Among them, the EEG signal-based BCI is the most commonly used system because of its relatively simple and inexpensive equipment requirements, as well as rich sources regarding its temporal resolution (e.g., visually evoked potential, P300, slow cortical potential) and frequency (e.g., power in given frequency bands) domains, the information can be extracted as the feature for controlling external devices [10]. The EEG signal-based BCI captures the signal of the event-related and time-locked decrease or increase in the oscillatory power in given frequency bands; in other words, the event-related desynchronization (ERD) or event-related synchronization (ERS), respectively [11, 12]. At present, hybrid BCI systems that combine more than one signal can provide more efficient natural control of external devices [13].[…]
Contimue —–> https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-020-00686-2
TBI Rehabilitation 