Journal of NeuroEngineering and Rehabilitation最新文献

Background: Reliable control of rehabilitation and assistive devices using High-Density surface Electromyography (HD-sEMG) remains limited by poor robustness to electrode shifts, changes in skin condition, and variability across users.

Methods: This study evaluates the performance of the Recursive Prosthetic Control Network (RPC-Net)/High-Density Electrode Array (HDE-Array) system, defined in previous studies, under conditions that reflect real-life usage, including electrode repositioning and cross-subject generalization. The first test evaluated whether the RPC-Net/HDE-Array system maintained stable performance when trained without electrode repositioning and evaluated on data from a different session with altered electrode placement. The study further examined whether explicitly incorporating electrode repositioning during training mitigates the performance degradation typically observed when testing is performed in a separate session. Finally, the effects of inter-subject training were assessed.

Results: Experimental results demonstrate that the RPC-Net/HDE-Array system is highly sensitive to electrode repositioning and skin condition variability when trained under static conditions. However, robustness improves significantly when such variability is included during training. The results indicate that performance improves with an increasing number of subjects in the training pool, provided the training set includes only data from subjects other than the one tested, suggesting a strong dependency on subject-specific patterns CONCLUSIONS: These findings demonstrate that the RPC-Net/HDE-Array system can achieve robust performance across sessions and users when trained under realistic conditions. This work represents a key step toward practical deployment of muscle-computer interfaces.

Background: The accurate prediction of prognosis in patients with disorders of consciousness (DOC) is a significant challenge in clinical practice. Some studies based on traditional electroencephalography (EEG) features have shown potential for DOC prognosis. However, the underlying mechanisms behind the recovery of patients with DOC still lack in-depth research.

Methods: In this study, we used mathematical tools to construct digital twin brain models (DTBM) for DOC patients with different outcomes. Then, we trained a support vector machine classifier using model parameters and modal controllability features to distinguish between DOC patients with different outcomes, and assessed the importance of these features. Finally, we used a support vector machine regressor to predict the Coma Recovery Scale-Revised (CRS-R) score at 6-month follow-up.

Results: The results showed that the prognosis model based on local model parameters and modal controllability features achieved better performance (AUC = 90.22%, F-score = 86.00%, SEN = 84.31%, SPE = 91.43%) than the prognosis models based on some traditional EEG features. Additionally, a positive prognosis is associated with lower levels of inhibitory gain, higher levels of excitatory gain and modal controllability, particularly in brain regions within the frontoparietal network. In 74% and 70% of UWS and MCS patients, the MAE between the predicted CRS-R score and the actual CRS-R score was less than 5.

Conclusions: Overall, our study contributes to enriching the neuromarkers associated with DOC prognosis and further elucidates the neural mechanisms of consciousness recovery.

Background: Subject-specific musculoskeletal modeling for estimating muscle force or joint torque is challenging as the muscle model parameters are difficult to determine. This study deals with the methodological questions regarding the application of an electromyography (EMG)-driven model combined genetic algorithm with OpenSim application programming interface to estimate muscle force or knee torque, for the quadriceps muscle group performing maximal voluntary isometric tasks.

Methods: Knee torque is calculated from the Hill muscle model using filtered EMG data as input and compared with the torque measured by a dynamometer. The initial values of muscle parameters include optimal fiber length, tendon slack length, pennation angle at optimal fiber length, and Maximum isometric force from an OpenSim model. Eight participants performed knee isometric tasks and kept contraction for five seconds while recording surface EMG of rectus femoris, vastus lateralis and vastus medialis synchronously at angles 30°, 45°, 60°, 75° and 90°. A genetic-simulated annealing algorithm is used to tune parameters to reduce root mean square error between the predicted and measured torques.

Results: The proposed approach produced better results with an overall mean RMS error of 3.7 Nm and R² of 0.97. The two curves between the simulated and measured torques were very similar when the four parameters were adjusted simultaneously.

Conclusion: These results reveal that subject-specific and well-calibrated musculoskeletal model can better predict muscle force or knee joint torque. The proposed method can demonstrate the feasibility to generate personalized MTU parameters of the knee with high estimation accuracy and low error.

Background: Standing up and sitting down are important activities of daily living, but require large leg moments that often exceed the muscle strength of older adults. Some robotic rollators are designed to provide standing-up and sitting-down assistance through actuated handles or armrests to reduce the loads on the legs, but it is still unclear how they should move. There is limited information on appropriate assistance trajectories and their effects on the body during standing up and sitting down.

Methods: We designed four physiological, scalable and parameterized handle trajectories based on unassisted shoulder movement that can be readily implemented in robotic assistive devices, and evaluated their effect on leg loading, energy input, handle forces and perceived assistance in 15 healthy younger adults. We created a robotic assistance simulator device equipped with moving handles to compare the trajectories to static handles (representing a conventional rollator), and collected full-body motion, ground reaction forces, handle forces and scored perceived assistance.

Results: The proposed handle trajectories substantially decreased leg loads compared to the static handle assistance (non-moving handle), with the two best-performing trajectories reducing the peak hip extension moment by over 70% and the peak knee extension moment by over 50% during standing up and sitting down. This is associated with an increase in peak vertical handle forces of over 30%, with the total bilateral vertical forces reaching up to 60% of body weight, and a decrease in peak horizontal force of more than 50%. The subjective participants' perception reflected the lower limb mechanical load. The handle velocity was shown to play a secondary role within the investigated range.

Conclusion: The proposed support trajectories can be scaled to the person's anthropometry and readily implemented in robotic assistive devices, and were shown to substantially reduce leg loading, potentially improving life quality of individuals with difficulties in standing up. However, the large vertical handle forces and thus upper body demand during moving-handle assistance is a trade-off with relieving the lower limb load. This work provides a comprehensive foundation for the design of the necessary further experimental assessments with the target population.

Background: Brain synergy and redundancy are emerging as pivotal aspects to understand neural functions, providing insights into high-order information that traditional functional connectivity (FC) methods cannot access. Despite their significance, these aspects have not been investigated in Parkinson's disease (PD). This paper advances the understanding of synergy and redundancy by integrating them with dynamic analysis, which is essential in the investigation of PD.

Methods: Dynamic brain synergy and redundancy were developed and quantified by the constructed dynamic information decomposition framework, and was applied to walking-state functional near-infrared spectroscopy (fNIRS) signals of 63 PD patients undergoing dopaminergic treatment and 36 healthy controls.

Results: Dynamic brain synergy was restored to normal levels following dopaminergic treatment. Dynamic FC could not access high-order neural information and had insignificant variations in dopaminergic modulation among PD patients, and dynamic brain redundancy also exhibited insignificant treatment-induced variations.

Conclusion: Dynamic brain synergy offers an advancing perspective on neural dynamics and promises to uncover high-order functional biomarkers for PD early diagnosis and individualized treatment.

Trial registration: This study has been registered in Chinese Clinical Trial Registry (ChiCTR1900022655).