Journal of NeuroEngineering and Rehabilitation最新文献

Background: Sensor-based technologies have been widely used in fall risk assessment. To enhance the model's robustness and reliability, it is crucial to analyze and discuss the factors contributing to the misclassification of certain individuals, enabling purposeful and interpretable refinement.

Methods: This study identified an abnormal gait pattern termed "Recessive weak foot (RWF)," characterized by a discontinuous high-risk gait on the weak foot side, observed through weak foot feature space. This condition negatively affected the training and performance of fall risk assessment models. To address this, we proposed a trainable threshold method to discriminate individuals with this pattern, thereby enhancing the model's generalization performance. We conducted feasibility and ablation studies on two self-established datasets and tested the compatibility on two published gait-related Parkinson's disease (PD) datasets.

Results: Guided by a customized index and the optimized adaptive thresholds, our method effectively screened out the RWF individuals. Specifically, after fine adaptation, the individual-specific models could achieve accuracies of 87.5% and 73.6% on an enhanced dataset. Compared to the baseline, the proposed two-stage model demonstrated improved performance, with an accuracy of 85.4% and sensitivity of 87.5%. In PD dataset, our method mitigated potential overfitting from low feature dimensions, increasing accuracy by 4.7%.

Conclusions: Our results indicate the proposed method enhanced model generalization by allowing the model to account for individual differences in gait patterns and served as an effective tool for quality control, helping to reduce misdiagnosis. The identification of the RWF gait pattern prompted connections to related studies and theories, suggesting avenues for further research. Future investigations are needed to further explore the implications of this gait pattern and verify the method's compatibility.

Background: In recent years, functional electrical stimulation (FES) has become a common intervention for stroke survivors to correct foot drop and improve gait biomechanics. While the orthotic effects of adaptive FES systems were well-documented, the center of pressure (COP) symmetry has been largely neglected. Furthermore, the long-term therapeutic effects of adaptive FES systems on gait biomechanics have received less attention. METHODS  : This study applied a timing- and intensity-adaptive functional electrical stimulation system for evaluation and training tests to address these limitations. In the evaluation test, eight participants with chronic stroke walked under three FES conditions: no stimulation (NS), adaptive FES to the tibialis anterior (SA-ILC SCS), and hybrid adaptive FES to the tibialis anterior and the gastrocnemius (SA-ILC DCS). Nine healthy subjects walked under the NS condition as the control group. In the training test, two participants with stroke took part in a 21-day training session under the SA-ILC DCS condition.

Results: The results showed that the COP symmetry of participants with stroke in the SA-ILC SCS condition tended to improve compared to the NS condition, while the SA-ILC DCS condition showed significant improvement, approaching that of healthy subjects. After the 21-day treatment period, there was a tendency for improvement in the knee-ankle angle, anterior ground reaction force, and COP symmetry of both participants with stroke without assistance.

Conclusion: The observed improvements can be attributed to the hybrid adaptive FES targeting the tibialis anterior and gastrocnemius muscles. This study demonstrates that the adaptive FES system offers promising walking assistance capabilities and significant clinical therapeutic potential.

Trial registration: Ethics Committee of Zhujiang Hospital, Southern Medical University, 2022-KY-149-01. Registered 29 September 2022.

Duchenne muscular dystrophy (DMD) and spinal muscular atrophy (SMA) are neuromuscular diseases that lead to progressive muscle degeneration and weakness. Recent therapeutic advances for DMD and SMA highlight the need for accurate clinical evaluation. Traditionally, motor function of the upper limbs is assessed using motor function scales. However, these scales are influenced by clinician's interpretation and may lack accuracy. For this reason, clinicians are becoming interested in finding alternative solutions. In this context, Inertial Measurement Units (IMUs) have gained popularity, offering the possibility to quantitatively and objectively analyze motor function of patients to support clinicians' assessments. We analyzed upper limb kinematics of two groups of children with neuromuscular diseases, seventeen DMD patients and fifteen SMA patients, while performing the corresponding clinical assessment. These two groups were further subdivided into two categories (Category A and Category B), according to disease severity (Brooke scores $\le 2$ and Brooke scores $>2$ , respectively). The results were compared against a group of ten healthy children. The metrics showing the strongest correlation with the clinical score were the workspace area in the frontal and transverse plane (DMD: $\rho$ = 0.94 and $\rho$ = 0.90; SMA: $\rho$ = 0.78 and $\rho$ = 0.81) and the workspace volume (DMD: $\rho$ = 0.92; SMA $\rho$ = 0.81). Additionally, statistically significant differences were found not only between healthy children and those with neuromuscular disease, but also across severity levels within the patient group. These results represent a first step toward validating IMU-based systems to helping clinicians to accurately quantify the motor status of children with neuromuscular diseases. Furthermore, data collected with inertial sensors can provide clinicians with additional information not available through subjective observation.

Background: Motor imagery-based brain-computer interface (MI-BCI) is a promising solution for neurorehabilitation. Many studies proposed that reducing false positive (FP) feedback is crucial for inducing neural plasticity by BCI technology. However, the effect of FP feedback on cortical plasticity induction during MI-BCI training is yet to be investigated.

Objective: This study aims to validate the hypothesis that FP feedback affects the cortical plasticity of the user's MI during MI-BCI training by first comparing two different asynchronous MI-BCI paradigms (with and without FP feedback), and then comparing its effectiveness with that of conventional motor learning methods (passive and active training).

Methods: Twelve healthy volunteers and four patients with stroke participated in the study. We implemented two electroencephalogram-driven asynchronous MI-BCI systems with different feedback conditions. The feedback was provided by a hand exoskeleton robot performing hand open/close task. We assessed the hemodynamic responses in two different feedback conditions and compared them with two conventional motor learning methods using functional near-infrared spectroscopy with an event-related design. The cortical effects of FP feedback were analyzed in different paradigms, as well as in the same paradigm via statistical analysis.

Results: The MI-BCI without FP feedback paradigm induced higher cortical activation in MI, focusing on the contralateral motor area, compared to the paradigm with FP feedback. Additionally, within the same paradigm providing FP feedback, the task period immediately following FP feedback elicited a lower hemodynamic response in the channel located over the contralateral motor area compared to the MI-BCI paradigm without FP feedback (p = 0.021 for healthy people; p = 0.079 for people with stroke). In contrast, task trials where there was no FP feedback just before showed a higher hemodynamic response, similar to the MI-BCI paradigm without FP feedback (p = 0.099 for healthy people, p = 0.084 for people with stroke).

Conclusions: FP feedback reduced cortical activation for the users during MI-BCI training, suggesting a potential negative effect on cortical plasticity. Therefore, minimizing FP feedback may enhance the effectiveness of rehabilitative MI-BCI training by promoting stronger cortical activation and plasticity, particularly in the contralateral motor area.

Objective: Surface electromyography (EMG) decomposition is crucial for identifying motor neuron activities by analyzing muscle-generated electrical signals. This study aims to develop and validate a novel motor unit action potential (MUAP)-based method for surface EMG decomposition, addressing the limitations of traditional blind source separation (BSS)-based techniques in computation complexity and motor unit (MU) tracking.

Methods: Within the framework of the convolution kernel compensation algorithm, we developed a MUAP-based decomposition algorithm by reconstructing the MU filters from MUAPs and evaluated its performance using both simulated and experimental datasets. A systematic analysis was conducted on various factors affecting decomposition performance, including MU filter reconstruction methods, EMG covariance matrices, MUAP extraction techniques, and extending factors. The proposed method was subsequently compared to representative BSS-based techniques, such as convolution kernel compensation.

Main results: The MUAP-based method significantly outperformed traditional BSS-based techniques in identifying more MUs and achieving better accuracy, particularly under noisy conditions. It demonstrated superior performance with increased signal complexity and effectively tracked motor units consistently across decompositions. In addition, directly applying the MU filters reconstructed from MUAPs to decomposition exhibited marked computational efficiency.

Conclusion and significance: The MUAP-based method enhances EMG decomposition accuracy, robustness, and efficiency, offering reliable motor unit tracking and real-time processing capabilities. These advancements highlight its potential for clinical diagnostics and neurorehabilitation, representing a promising step forward in non-invasive motor neuron analysis.

The Journal of NeuroEngineering and Rehabilitation (JNER) has become a major actor for the dissemination of knowledge in the scientific community, bridging the gaps between innovative neuroengineering and rehabilitation. Major fields of innovations have emerged these last 25 years, such as machine learning and the ongoing AI revolution, wearable technologies, human machine interfaces, robotics, advanced prosthetics, functional electrical stimulation and various neuromodulation techniques. With the major burden of disorders impacting on the central/peripheral nervous system and the musculoskeletal system both in adults and in children, successful tailored neurorehabilitation has become a major objective for the research and clinical community at a world scale. JNER contributes to this challenging goal, publishing groundbreaking cutting-edge research using the open access model. The multidisciplinary approaches at the crossroads of biomedical engineering, neuroscience, physical medicine and rehabilitation make of the journal a unique growing platform welcoming breakthrough discoveries to reshape the field and restore function.

Background: Mild Cognitive Impairment (MCI) is an intermediate stage between the expected cognitive decline of normal aging and Alzheimer's disease (AD). Its management is crucial for it helps intervene and slow the progression of cognitive decline to AD. However, the understanding of the MCI mechanism is not completely clear. As working memory (WM) damage is a common symptom of MCI, this study focused on the core stage of WM, i.e., the memory retrieval stage, to investigate information processing and the causality relationships among brain regions based on electroencephalogram (EEG) signals.

Method: 21 MCI and 20 normal cognitive control (NC) participants were recruited. The delayed matching sample paradigm with two different loads was employed to evaluate their WM functions. A time-varying network based on the Adaptive transfer function (ADTF) was constructed on the EEG of the memory retrieval trials.to perform the dynamic brain network analysis.

Results: Our results showed that: (a) Behavioral data analysis: there were significant differences in accuracy and accuracy / reaction time between MCI and NC in tasks with memory load capacity of low load-four and high load-six, especially in tasks with memory load capacity of four. (b) Dynamic brain network analysis: there were significant differences in the dynamic changes of brain network patterns between the two groups during the memory retrieval stage of the WM task. Specifically, in low load WM tasks, the dynamic brain network changes of NC were more regular to accommodate for efficient information processing, with important core nodes showing a transition from bottom to up, while MCI did not display a regular dynamic brain network pattern. Further, the brain functional areas associated with low load WM disorders were mainly located in the left prefrontal lobe (FC1) and right occipital lobe (PO8). Compared with low load WM task, during the high load WM task, the dynamic brain network changes of NC during the memory retrieval stage were regular, and the core nodes exhibited a consistent transition phenomenon from up to bottom to up, which were not observed in MCI.

Conclusions: Behavioral data in the low load WM task paradigm and abnormal electrophysiological signals in the left prefrontal (FC1) and right occipital lobes (PO8) could be used for MCI diagnosis. This is the first time based on large-scale dynamic network methods to investigate the dynamic network patterns of MCI memory retrieval stages under different load WM tasks, providing a new perspective on the neural mechanisms of WM deficits in MCI patients and providing some reference for the clinical intervention treatment of MCI-WM memory disorders.

Background: Prosthesis users often rely on vision to monitor the activity of their prosthesis, which can be cognitively demanding. This compensatory visual behaviour may be attributed to an absence of feedback from the prosthesis or the unreliability of myoelectric control. Unreliability can arise from the unpredictable control due to variations in electromyography signals that can occur when the arm moves through different limb positions during functional use. More robust position-aware control systems have been explored using deep learning methods, specifically ones that utilize data from different limb positions, that show promising improvements in control characteristics. However, it is unclear how these novel controllers will affect visuomotor behaviour. Specifically, the extent to which control interventions can influence gaze behaviours remain unknown, as previous studies have not yet demonstrated the sensitivity of eye metrics to these interventions. This study aims to explore how visuomotor behaviours change when individuals operate a simulated myoelectric prosthesis using a standard control strategy compared to a position-aware control strategy.

Methods: Participants without limb difference tested two control strategies in a within-subject crossover study design. They controlled a simulated myoelectric prosthesis using a standard control strategy and an advanced position-aware control strategy designed to address the limb position effect. The order in which these control strategies were evaluated was randomized. Eye tracking and motion capture data were collected during functional task execution to assess if using the position-aware control strategy changed visuomotor behaviour compared to the standard controller.

Results: There was less visual fixation on the prosthetic hand in the fully extended and cross-body arm position when using the position-aware controller compared to the standard controller. These changes were associated with shorter grasp phase duration and increased smoothness of prosthesis movements. These findings indicated that using the position-aware control strategy may have resulted in less reliance on vision to monitor the prosthesis actions in limb positions where they had better prosthesis control.

Conclusions: This research suggests that visuomotor metrics may be sensitive to prosthesis control interventions, and therefore the use of eye tracking should be considered for performance assessment of prosthesis control.

Background: Grasping and manipulating objects requires humans to adapt both grip and manipulation forces. When handling an object with both hands, the additional degrees of freedom introduce more levels to the redundancy of the object manipulation since we can distribute the contribution of the grip and manipulation forces between hands.

Methods: In this study, we investigated the forces produced by both hands during coupled bimanual manipulation of a needle object in a virtual environment. The task objective was to puncture a virtual tissue, modeled as a linear spring, and stop immediately after, with the hands arranged in front and back positions in the movement direction.

Results: We show that during tissue interaction, grip forces are modulated consistently between front and back hands across participants, but manipulation forces are not. That is, the back hand consistently produced excessive grip force compared to the front hand regardless of hand configuration, while manipulation force distribution between the two hands was variable. After the tissue puncture, we again observed consistent grip force behavior during the reactive response to the force drop following the puncture. The grip force signal exhibited a consistent temporal profile in both the front and back hands with amplitude modulation according to the tissue stiffness in the front hand.

Conclusions: Overall, our results support the idea of distinct control mechanisms for grip and manipulation forces which rely on hand position rather than hand dominance.

Background: Spinal cord injury (SCI) severely affects physical function, leading to muscle atrophy and reduced bone density. Sport-therapy, incorporating recreational and competitive activities, has shown promise in enhancing recovery for individuals with SCI. Functional Electrical Stimulation (FES)-cycling combines exercise benefits with stimulation advantages, and recent integration with mobile recumbent trikes adds further potential. This study aimed to evaluate the effects of a 6-month FES-cycling sport therapy using a recumbent trike on individuals with motor complete SCI.

Methods: Five participants engaged in bi-weekly FES-cycling sessions using an instrumented recumbent trike. A comprehensive assessment was conducted before training, at 3 and 6 months of training, and at 1-month follow-up. Outcome measures included maximal muscle Cross-Sectional Area (maxCSA) from Magnetic Resonance Images, bone mineral density, clinical scales, and questionnaires on spasticity, pain, bowel dysfunction, psychological well-being, and sport motivation. Additionally, maximal power output and cycling endurance were assessed.

Results: The FES-cycling program led to a significant increase in muscle mass of 34% after 6 months of training, correlated to an improved cycling performance (maxCSA versus peak power). A slight decrease of muscle mass was observed as expected at follow-up. Participants reported high well-being and strong motivation throughout the training program. Bone health, spasticity, bowel dysfunction, and pain levels did not significantly change overall.

Conclusions: FES-cycling on a recumbent trike shows potential as a therapeutic and recreational activity for individuals with SCI. It significantly improved muscle mass and physical performance while positively impacting psychological well-being and motivation. Further research with larger cohorts is necessary to confirm these benefits and optimize protocols, establishing FES-cycling as a valuable sport-therapy model for SCI.

Trial registration: The study protocol was retrospectively registered on clinicaltrials.gov (NCT06321172).