Journal of NeuroEngineering and Rehabilitation最新文献

Background: Mirror therapy (MT) and augmented reality (AR) are gaining popularity in stroke rehabilitation. MT uses mirror visual feedback to promote bilateral brain coupling and increase primary motor cortex excitability. AR offers an interactive context of practice for promoting motor and cognitive recovery. MT and AR may complement each other for hybrid interventions in stroke rehabilitation. This study investigated the benefits of MT-primed AR (MT + AR) versus AR group, relative to conventional therapy (CT) for individuals with stroke.

Method: The study randomly assigned 45 stroke survivors to the MT + AR group, the AR, or the CT group, and 44 of them completed the experiment and were included in the analysis. Each treatment session was 90 min, 3 times a week, for 6 weeks. All assessments were administered before, immediately after treatment, and at 3 months. Primary outcome measures were the Fugl-Meyer Assessment-Upper Extremity (FMA-UE) and the Berg Balance Scale (BBS). Secondary outcome measures were the revised Nottingham Sensory Assessment (rNSA), Chedoke Arm and Hand Activity Inventory (CAHAI), Motor Activity Log (MAL), and Stroke Impact Scale Version 3.0 (SIS). Adverse events were monitored before and after each session.

Results: After 6 weeks of treatment, the three groups demonstrated significant improvements in the FMA-UE, BBS, CAHAI, MAL, and SIS. In the between-group comparisons, MT + AR and AR groups demonstrated significant advantages in the BBS, proprioception scale of rNSA and SIS, compared with the CT group. Only the MT + AR group, not the AR group, showed significantly better improvements in the FMA-UE and tactile scale of rNSA than the CT group. The MT + AR and AR alone showed differential benefits in the FMA-UE, tactile scale of rNSA, and SIS; the MT + AR rendered significantly better benefits. There were no significant differences among the three groups in the stereognosis scale of rNSA and MAL. No adverse effects were observed.

Conclusion: MT + AR and AR both effectively enhanced sensorimotor functions, balance and postural control, task performance, and life quality in patients with stroke with moderate-to-severe motor impairments. The results showed that MT + AR and AR were more beneficial for improving stroke survivors' balance, functional mobility, proprioception recovery, and quality of life than the CT group. Furthermore, the MT + AR revealed better outcomes in the upper limb motor function and tactile sensory recovery. Between the MT + AR and AR comparisons, the MT + AR was more beneficial for improving upper limb motor function, tactile sensory recovery, and quality of life. Trial registration NCT05993091.

Background: Stroke survivors often experience residual motor dysfunction in their limbs. Additional physical rehabilitation therapies may further improve patients' functional outcomes. By combining direct interventions targeting the cerebral cortex or subcortical structures with indirect approaches that promote central nervous system reorganization, a closed-loop regulatory system can be established. This integrated approach may generate synergistic effects, thereby enhancing functional recovery outcomes.

Methods: This 3-week single-center randomized, single-masked study involved participants randomly assigned to either the electroacupuncture (EA) combined with robot-assisted gait training (RAGT) group (n = 22) or the RAGT alone group (n = 23). EA treatment was administered once daily for 30 min, 5 days per week, while RAGT treatment received the same duration of daily sessions. Baseline and endpoint assessments included the Fugl-Meyer lower extremity (FMA-LE) motor function assessment, functional ambulation category (FAC) scale, Berg Balance Scale (BBS) and electroencephalogram.

Results: After a 3-week intervention period, participants in both groups showed significant improvements in FMA-LE, FAC, and BBS scores compared to baseline levels. The EA combined RAGT group exhibited a reduction in the brain symmetry index within the alpha frequency band, along with enhanced coherence between the CZ electrode and the FCZ, FC2, and C1 electrodes. Furthermore, in the theta frequency band, a shortened average path length and improved global efficiency were observed.

Conclusion: Both interventions can safely and effectively improve lower limb motor function, and EA combined with RAGT combination therapy may have an advantage in promoting neuroplasticity, which may involve reversing pathological frequency spectrum imbalance after stroke, enhancing functional connections between sensorimotor-related brain regions, and optimizing the topological properties of brain functional networks. Trial registration Chinese Clinical Trial Registry (Registration No.: ChiCTR2500102382).

Background: Severe upper limb impairment (ULI) presents a significant challenge in the rehabilitation of chronic stroke survivors and affects their quality of life. Identifying biomarkers and understanding the neural mechanisms associated with severe ULI are essential for evaluating recovery potential and enhancing rehabilitation effectiveness. This study aimed to identify resting-state electroencephalography (EEG) functional connectivity features associated with severe ULI in chronic stroke survivors using machine learning (ML) methods.

Methods: EEG data were collected from 34 chronic stroke survivors. Participants were categorized into two groups based on their Fugl-Meyer Assessment for Upper Extremity (FMA-UE) scores: a mild/moderate ULI (FMA-UE ≥ 30; n = 19) and severe ULI (FMA-UE < 30; n = 15). We employed ML algorithms to classify severe ULI, including logistic regression with L1, elastic net regularization, stochastic gradient descent, and support vector machines, along with several feature selection methods. Coherence was evaluated across six frequency bands in both the ipsilesional (affected by the lesion) and contralesional (opposite side of the lesion) hemispheres.

Results: The logistic regression model with L1 and ReliefF feature selection methods was the most effective, achieving a balanced accuracy of 0.91 (sensitivity = 0.93; specificity = 0.90). This approach identified 14 significant features for distinguishing severe ULI from mild to moderate ULI, including delta interhemispheric and intrahemispheric connectivity in the frontal, parietal, and temporal regions. Additionally, interhemispheric and intrahemispheric theta connectivity was observed in the prefrontal, frontal, temporal, and parietal regions. Low-beta intrahemispheric connectivity was also observed in the contralesional parietal regions.

Conclusions: Our research highlights the association between alterations in connectivity within low-frequency bands and severe ULI across widespread brain regions, including areas outside the sensorimotor cortex and bilateral intrahemispheric and interhemispheric regions. Further research utilizing larger longitudinal datasets from early stroke survivors employing ML approaches could contribute to the development of more accurate predictive models for motor recovery and rehabilitation responses.

Background: Individuals who experience severe speech and physical impairment face significant challenges in communication and daily interaction. Visual brain-computer interfaces (BCIs) offer a potential assistive solution, but their usability is limited when facing restrictions in eye motor control, gaze redirection and fixation. This study investigates the feasibility of a gaze-independent visual oddball BCI for use as an augmentative and alternative communication (AAC) device in the presence of limited eye motor control.

Methods: Seven participants with varying degrees of eye motor control were recruited and their conditions were thoroughly assessed. Visual oddball BCI decoding accuracy was evaluated with multiple decoders in three visuospatial attention (VSA) conditions: overt VSA, with fixation cued on the target, covert VSA, with fixation cued on the center of the screen, and free VSA without gaze cue.

Results: covert VSA with central fixation leads to decreased accuracy, whereas free VSA is comparable to overt VSA for some participants. Furthermore, cross-condition decoder training and evaluation suggests that training with overt VSA may improve performance in BCI users experiencing gaze control difficulties.

Conclusions: These findings highlight the need for adaptive decoding strategies and further validation in applied settings in the presence of gaze impairment.

Unilateral spatial neglect (USN) is a failure to respond or orient to stimuli in contralesional space, not explained by primary sensory or motor deficits. It affects up to two-thirds of right hemisphere stroke survivors and significantly impacts rehabilitation and functional outcomes. Recent advances in three-dimensional (3D) technologies, such as virtual reality (VR) and robotics, offer promising tools for assessment and treatment, providing realistic scenarios and precise clinical stimulation. This systematic review explores the current use of 3D technologies in USN, focusing on their features, level of development, and reported outcomes. A structured search of four databases using the PICO format identified 37 relevant studies out of 2891. The most frequently employed technologies were immersive and non-immersive VR, augmented and mixed reality, and robotics. However, these tools are still in early experimental phases. Among the studies, 15 addressed assessment, 17 focused on treatment, and 5 were technical in nature. Key challenges include methodological variability and the lack of standardized protocols. Due to the heterogeneity of technologies and outcomes, a meta-analysis was not feasible. Future studies should adopt rigorous designs to validate these approaches and support their integration into clinical practice.

Background: Artificial sensory feedback can improve function and user experience in lower-limb prosthesis users. Non-invasive methods like vibrotactile stimulation are clinically convenient, as they require no surgery. Most studies evaluate single feedback approaches, typically under controlled conditions promoting reliance on feedback. This study presents a flexible framework to compare multiple feedback approaches using microprocessor-controlled prosthesis (MP) sensors during daily-life activities.

Methods: Ten able-bodied participants and one prosthesis user with transfemoral amputation (TFA) tested two feedback locations (waist "Belt", or thigh/residual limb "Socket") to investigate tradeoffs between perception quality and compactness, using Sensation Thresholds (ST), Weber Fraction (WF), Spatial Discrimination (SD), and comfort. TFA then completed an out-of-the-lab walking session with the Socket configuration to evaluate the impact of four feedback approaches on spatiotemporal parameters and kinematics symmetries, cognitive load, and user experience during overground walking and stair climbing. Three approaches used embedded MP sensors, conveying (1) knee angle, (2) hybrid (gait phases overground, knee angle during stairs), and (3) damping (velocity-dependent resistance to flexion/extension) feedback. The fourth method used a sensorized insole, providing (4) force feedback (plantar pressure under the prosthetic foot).

Results: Able-bodied participants perceived the Belt configuration better-lower ST (29.09 ± 0.60% vs. 33.19 ± 0.60%, p < 0.001), lower WF (14.49 ± 7.02% vs. 17.98 ± 5.72%, p < 0.01), better SD at higher task difficulty (four choices: 99.3 ± 2.0% vs. 91.5 ± 2.0%, p < 0.01; eight choices: 96.0 ± 2.0% vs. 78.1 ± 2.0%, p < 0.001)-and found it also more comfortable (9.17 ± 0.3 vs. 8.15 ± 0.3; p < 0.05). Similar trends were observed in TFA. Feedback did not impact the kinematics symmetry but slightly affected stance time/percentage symmetry, with force feedback demonstrating the most consistent benefits. These suggest that incidental feedback provided intrinsically by the prosthesis (e.g., motion, sound, socket pressure, vibration) may already support gait in experienced users. Nevertheless, TFA preferred having feedback, especially force and damping, which reduced cognitive load.

Conclusion: Embedded MP sensors enable flexible, compact feedback solutions, combining internal signals (e.g., damping feedback) with external sensing (e.g., omnidirectional force feedback). Belt-mounted vibromotors are effective for testing complex encoding schemes. Feedback should be co-developed with users, balancing objective performance and subjective experience.

Background: Stroke is a condition marked by considerable variability in lesions, recovery trajectories, and responses to therapy. Consequently, precision medicine in rehabilitation post-stroke, which aims to deliver the "right intervention, at the right time, in the right setting, for the right person," is essential for optimizing stroke recovery. Although artificial intelligence (AI) has been effectively utilized in other medical fields, no current AI system is designed to tailor and continuously refine rehabilitation plans post-stroke.

Methods: We propose a novel AI-based decision-support system for precision rehabilitation that uses reinforcement learning (RL) to personalize the treatment plan. Specifically, our system iteratively adjusts the sequential treatment plan-timing, dosage, and intensity-to maximize long-term outcomes based on a patient model that includes covariate data (the context). The system collaborates with clinicians and people with stroke to customize the recommended plan based on clinical judgment, constraints, and preferences. To achieve this goal, we propose a contextual Markov decision process (CMDP) framework and a novel hierarchical Bayesian model-based RL algorithm, named posterior sampling for contextual RL (PSCRL), that discovers and continuously adjusts near-optimal sequential treatments by efficiently balancing exploitation and exploration while respecting constraints and preferences.

Results: We implemented and validated our precision rehabilitation system in simulations with 150 diverse, synthetic patients. Simulation results showed the system's ability to continuously learn from both upcoming data from the current patient and a database of past patients via Bayesian hierarchical modeling. Specifically, the algorithm's sequential treatment recommendations became increasingly more effective in improving functional gains for each patient over time and across the synthetic patient population. As a result, the algorithm's treatments were superior to non-adaptive, "one-size-fits-all" dosing schedules (uniform, decreasing, and increasing).

Conclusions: Our novel AI-based precision rehabilitation system, based on contextual model-based RL, has the potential to play a key role in novel learning health systems in rehabilitation.