Where is the protocol located so survivors can find it and deliver it to their 'professionals'? Top down has been proven numerous times not to work, so survivors have to take charge of their own recovery!
Efficacy of brain-computer interface training with motor imagery-contingent feedback in improving upper limb function and neuroplasticity among persons with chronic stroke: a double-blinded, parallel-group, randomized controlled trial
Journal of NeuroEngineering and Rehabilitation volume 22, Article number: 1 (2025)
Abstract
Background
Brain-computer interface (BCI) technology can enhance neural plasticity and motor recovery in persons with stroke. However, the effects of BCI training with motor imagery (MI)-contingent feedback versus MI-independent feedback remain unclear. This study aimed to investigate whether the contingent connection between MI-induced brain activity and feedback influences functional and neural plasticity outcomes. We hypothesized that BCI training, with MI-contingent feedback, would result in greater improvements in upper limb function and neural plasticity compared to BCI training, with MI-independent feedback.
Methods
This randomized controlled trial included persons with chronic stroke who underwent BCI training involving functional electrical stimulation feedback on the affected wrist extensor. Primary outcomes included the Medical Research Council (MRC) scale score for muscle strength in the wrist extensor (MRC-WE) and active range of motion in wrist extension (AROM-WE). Resting-state electroencephalogram recordings were used to assess neural plasticity.
Results
Compared to the MI-independent feedback BCI group, the MI-contingent feedback BCI group showed significantly greater improvements in MRC-WE scores (mean difference = 0.52, 95% CI = 0.03–1.00, p = 0.036) and demonstrated increased AROM-WE at 4 weeks post-intervention (p = 0.019). Enhanced functional connectivity in the affected hemisphere was observed in the MI-contingent feedback BCI group, correlating with MRC-WE and Fugl-Meyer assessment-distal scores. Improvements were also observed in the unaffected hemisphere’s functional connectivity.
Conclusions
BCI training with MI-contingent feedback is more effective than MI-independent feedback in improving AROM-WE, MRC, and neural plasticity in individuals with chronic stroke. BCI technology could be a valuable addition to conventional rehabilitation for stroke survivors, enhancing recovery outcomes.
Trial registration
CRIS (KCT0009013).
Background
Upper limb impairments, which are common after stroke, have a significant impact on stroke survivors’ lives. Recent advancements in technologies, such as virtual rehabilitation, rehabilitation robots, and non-invasive brain stimulation, have enabled their use as adjunct or stand-alone therapies for upper-limb rehabilitation [1]. More recently, a brain-computer interface (BCI) system, that captures central nervous system (CNS) activity and translates it into artificial signals, has been used to substitute, restore, or enhance CNS output [2]. BCI allows direct communication between the human brain and external devices, enabling control of external devices, such as computer or robotic devices, bypassing conventional motor pathways. In upper-limb rehabilitation among persons with stroke, BCIs interpret the patient’s intention to move, aiding muscle stimulation or external device control. Through repetitive learning, BCIs can facilitate neural plasticity and fundamental motor recovery [3]. Several studies have demonstrated the beneficial effects of BCI training on motor function and neuroplasticity during stroke rehabilitation [4, 5].
A BCI system continuously monitors brain signals and provides feedback or stimulation to the user based on brain signals across various processes such as data acquisition, signal processing, feedback, adaptive training, and progress monitoring [4,5,6]. In the context of motor rehabilitation, reward feedback is provided only when the user imagines the desired movement, allowing the user to learn how to control the movement more effectively. The patient’s intention-driven feedback gradually creates a closed loop from intention to motor execution throughout BCI training, becoming an integral part of motor learning. Therefore, a contingency between the neural correlates of motor intention and consequent feedback should be established in BCIs to reorganize the targeted neural circuit, fundamentally leading to functional improvement.
Previous studies have demonstrated the favorable effects of this close connection between intention and feedback; however, there are inconsistencies in BCI systems and results of previous studies comparing motor imagery (MI)-contingent feedback (real-BCI) and BCI operated by MI-independent feedback (sham-BCI). Frolov et al. [7] employed a BCI-controlled hand exoskeleton and demonstrated within-group improvements after real-BCI without directly comparing real-BCI and sham-BCI. Ramos-Murguialday et al. [8] compared real and sham-BCI using BCI-driven finger orthosis and demonstrated significant improvement in motor function, particularly in terms of the upper limb Fugl-Meyer assessment (FMA) scores in the real-BCI group compared to those in the sham-BCI group. In addition, the improvements were associated with changes in the affected hand’s fMRI laterality index and electromyographic activity. Biasiucci et al. [9] compared real and sham-BCI using functional electrical stimulation (FES) feedback and demonstrated significant differences in the improvement of FMA, muscle strength of the wrist extensor, and functional connectivity in the affected hemisphere in the real-BCI group compared with that in the sham-BCI group.
recoveriX-PRO® (g.tec Medical Engineering GmbH, Austria) is a ready-to-use BCI system and comprises different features to strengthen closed-loops. First, it detects motor intention in different ways. recoveriX-PRO compares brain activity between hemispheres during mental rehearsal of affected or unaffected (right or left) hand movements. This approach differs from previous methods that obtained signals from MI of the affected hand versus rest. Second, calibration is conducted in every session before the BCI intervention, reflecting the variability of electrode position and electroencephalogram (EEG) electrode impedance. Third, FES is provided during calibration, aiming to align more closely with motor intentions during BCI training, as EEG signals are influenced by sensory feedback during actual BCI-FES training. Lastly, recoveriX-PRO provides visual feedback through animated upper extremities of an avatar in virtual reality and proprioceptive feedback by generating movement via FES. In contrast to traditional BCIs, our study used a virtual reality-based game task. We believe that virtual reality enhances motor performance by boosting motivation and active engagement, which facilitate BCI participation [10]. We hypothesized that close contingent connection between MI-induced brain activity and consequent sensory feedback is essential in BCI systems for functional improvement and neural plasticity and that this contingency should be confirmed for individual BCI systems, considering their unique characteristics. Therefore, this study aimed to compare the effects of the BCI system operated by MI-contingent feedback BCI group, versus the effects of BCI operated by MI-independent feedback BCI group on distal upper limb function and brain activity in persons with chronic stroke with weak wrist extensor strength.
No comments:
Post a Comment