IEEE Transactions on Neural Systems and Rehabilitation Engineering最新文献

{"title":"Exploring Brain-Body Interactions in Parkinson’s Disease: A Study on Dual-Task Performance","authors":"Maryam Sousani;Raul Fernandez Rojas;Elisabeth Preston;Maryam Ghahramani","doi":"10.1109/TNSRE.2025.3546278","DOIUrl":"10.1109/TNSRE.2025.3546278","url":null,"abstract":"Parkinson’s disease (PD) leads to impairments in cortical structures, resulting in motor and cognitive symptoms. Given the connection between brain structure deficits and physical symptoms in PD, assessing objective brain activity and body motion could provide valuable insights for PD assessment and understanding its underlying mechanisms. This study aimed to explore the connection between brain activity and body movement metrics in a group of individuals with PD and an age-matched healthy control (HC) group. The goal was to evaluate the feasibility of using brain and body motion measures for assessing PD. Participants from both groups underwent the Timed Up and Go (TUG) test under three conditions: simple TUG, cognitive dual-task TUG (CDTUG), and motor dual-task TUG (MDTUG). Key findings include: Both groups exhibited similar activation patterns in the prefrontal cortex (PFC) during the simple TUG, with motor performance differences observed in cadence. During CDTUG, both groups showed the highest PFC activation with more pronounced motor impairments, such as higher stride and step time. During MDTUG, the HC group exhibited significantly higher PFC activity compared to the PD group. While both groups had similar patterns of activation in PFC area while TUG and CDTUG, they showed distinct behaviour during MDTUG. These results suggest that motor and cognitive impairments in PD are more pronounced during complex activities. While MDTUG effectively differentiated between PD and HC groups, the findings indicate that both cognitive and motor dual-tasks are essential for comprehensive PD assessment.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"984-993"},"PeriodicalIF":4.8,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10906660","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correlating Data-Driven Muscle Selection Approaches to Synergies for Gait Prediction","authors":"Annika Guez;C. Sebastian Mancero Castillo;Balint Hodossy;Dario Farina;Ravi Vaidyanathan","doi":"10.1109/TNSRE.2025.3543743","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3543743","url":null,"abstract":"Optimizing sensors for physiological input is critical to enhance performance as well as minimize the cost and complexity of assistive devices (e.g. lower-limb exoskeletons). Electromyography (EMG) data can trace muscle activation for gait kinematics prediction. However, identifying optimal muscle groups for electrode placement and the potential variance between users has not yet been established. In this study, we use data-driven channel selection techniques on EMG signals to find muscle group combinations that maximize prediction performance. We apply greedy search (Recursive Feature Elimination, RFE) and variance-based (Principal Component Analysis, PCA) methods to select muscle groups during gait, without prior knowledge of musculoskeletal inter-connectivity. The selected muscle subsets are evaluated using the normalized accuracy of a Multi-Layer Perceptron (MLP), mapping muscle activity to knee flexion angle in a one-step-ahead scheme. The RFE selection led to an average predicted knee angle validation accuracy of <inline-formula> <tex-math>$4.52pm 1.85$ </tex-math></inline-formula> % higher than the PCA approach, suggesting that dynamic search is more appropriate than a variance analysis of the signals. Whilst the RFE-selected muscle groups differed across subjects, the selected muscles were consistently spread out over more than 80% of the extracted synergy groups. This study underlines the value of incorporating synergistic information when developing gait prediction models, and reveals that maximizing the number of synergy groups could constitute the basis of muscle selection frameworks.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"945-955"},"PeriodicalIF":4.8,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10907970","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing a Multi-Directional Lower-Limb Training System Toward Aging in Place Rehabilitation: A Preliminary Feasibility Study on Healthy Individuals","authors":"Song Joo Lee;Kyung-Mi Park;Keun-Tae Kim;Eun-Young Seo;Duguma T. Gemechu;Olga Kim","doi":"10.1109/TNSRE.2025.3545845","DOIUrl":"10.1109/TNSRE.2025.3545845","url":null,"abstract":"Not only improving muscle strength but also improving muscle power and neuromuscular control are important factors in improving lower limb function. In this study, a multi-directional lower-limb training system for aging -in-place rehabilitation was developed. The training system offers four distinct modes: muscle power training, pivoting neuromuscular training, muscle strength training using eccentric contractions, and proprioception training with evaluation. The feasibility of the training system was assessed through experiments conducted on healthy adults. Parameters such as mean of electromyography (EMG) peaks and pivoting instability during stepping tasks, and proprioceptive acuity in terms of pivoting angle error were tested using the system. By incorporating these diverse training modes, the training system can potentially be used to support clinicians in delivering tailored and effective subject-specific interventions for individuals with musculoskeletal and/or neuromuscular abnormalities toward aging-in-place rehabilitation.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"975-983"},"PeriodicalIF":4.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10904870","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online Unsupervised Adaptation of Latent Representation for Myoelectric Control During User-Decoder Co-Adaptation","authors":"Hanjie Deng;Zhikai Wei;Xuhui Hu;Hong Zeng;Aiguo Song;Dingguo Zhang;Dario Farina","doi":"10.1109/TNSRE.2025.3545818","DOIUrl":"10.1109/TNSRE.2025.3545818","url":null,"abstract":"Myoelectric control interfaces, which map electromyographic (EMG) signals into control commands for external devices, have applications in active prosthesis control. However, the statistical characteristics of EMG signals change over time (e.g., because of changes in the electrode location), which makes interfaces based on static mapping unstable. Thus the user-decoder co-adaptation is needed during online operations. Nevertheless, current online decoder adaptation approaches present several practical challenges, such as expensive data labeling and slow convergence. Thus we introduce an unsupervised decoder adaptation method that converges rapidly. We use an autoencoder to extract motor intent representation in the latent manifold space rather than the sensor space, and further introduce an online unsupervised adaptation scheme based on Moore-Penrose Inverse, a noniterative approach suited for fast network re-training, to track the evolving manifold. A validation experiment first showed that the convergence time of the proposed adaptation scheme was reduced to about 50% of that for state-of-the-art methods. Online experiments further evaluated cursor and prosthetic hand control by the proposed myocontrol interface, where perturbations were representatively introduced by shifting the electrodes. Results showed that our scheme reached comparable improvements in robustness as supervised counterparts. Moreover, in a cup relocation test with a prosthetic hand, the completion time in the post-adaptation phase with electrode shift was comparable to that in the baseline phase without shift. These results suggest that our method effectively improves the accessibility and reliability of decoder adaptation, which has the potential to reduce the translational gap of myoelectric control interfaces by effective co-adaptation during operation.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1026-1037"},"PeriodicalIF":4.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10904858","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural Correlation Integrated Adaptive Point Process Filtering on Population Spike Trains","authors":"Mingdong Li;Shuhang Chen;Xiang Zhang;Yiwen Wang","doi":"10.1109/TNSRE.2025.3545206","DOIUrl":"10.1109/TNSRE.2025.3545206","url":null,"abstract":"Brain encodes information through neural spiking activities that modulate external environmental stimuli and underlying internal states. Population of neurons coordinate through functional connectivity to plan movement trajectories and accurately activate neuromuscular activities. Motor Brain-machine interface (BMI) is a platform to study the relationship between behaviors and neural ensemble activities. In BMI, point process filters model directly on spike timings to extract underlying states such as motion intents from observed multi-neuron spike trains. However, these methods assume the encoded information from individual neurons is conditionally independent, which leads to less precise estimation. It is necessary to incorporate functional neural connectivity into a point process filter to improve the state estimation. In this paper, we propose a neural correlation integrated adaptive point process filter (CIPPF) that can incorporate the information from functional neural connectivity from population spike trains in a recursive Bayesian framework. Functional neural connectivity information is approximated by an artificial neural network to provide extra updating information for the posterior estimation. Gaussian approximation is applied on the probability distribution to obtain a closed-form solution. Our proposed method is validated on both simulation and real data collected from the rat two-lever discrimination task. Due to the simultaneous modeling of functional neural connectivity and single neuronal tuning properties, the proposed method shows better decoding performance. This suggests the possibility to improve BMI performance by processing the coordinated neural population activities.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1014-1025"},"PeriodicalIF":4.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10902622","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal Freezing of Gait Detection: Analyzing the Benefits and Limitations of Physiological Data","authors":"Po-Kai Yang;Benjamin Filtjens;Pieter Ginis;Maaike Goris;Alice Nieuwboer;Moran Gilat;Peter Slaets;Bart Vanrumste","doi":"10.1109/TNSRE.2025.3545110","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3545110","url":null,"abstract":"Freezing of gait (FOG) is a debilitating symptom of Parkinson’s disease (PD), characterized by an absence or reduction in forward movement of the legs despite the intention to walk. Detecting FOG during free-living conditions presents significant challenges, particularly when using only inertial measurement unit (IMU) data, as it must be distinguished from voluntary stopping events that also feature reduced forward movement. Influences from stress and anxiety, measurable through galvanic skin response (GSR) and electrocardiogram (ECG), may assist in distinguishing FOG from normal gait and stopping. However, no study has investigated the fusion of IMU, GSR, and ECG for FOG detection. Therefore, this study introduced two methods: a two-step approach that first identified reduced forward movement segments using a Transformer-based model with IMU data, followed by an XGBoost model classifying these segments as FOG or stopping using IMU, GSR, and ECG features; and an end-to-end approach employing a multi-stage temporal convolutional network to directly classify FOG and stopping segments from IMU, GSR, and ECG data. Results showed that the two-step approach with all data modalities achieved an average F1 score of 0.728 and F1@50 of 0.725, while the end-to-end approach scored 0.771 and 0.759, respectively. However, no significant difference was found compared to using only IMU data in both approaches (p-values: 0.466 to 0.887). In conclusion, adding physiological data did not provide a statistically significant benefit in distinguishing between FOG and stopping. The limitations may be specific to GSR and ECG data, and may not generalize to other physiological modalities.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"956-965"},"PeriodicalIF":4.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10902623","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalizing Muscle Tendon Parameters of Cerebral Palsy Patient’s Digital Model","authors":"Tinghan Xu;Yuanhao Liang;Lin Feng;Li Liu;Eric Yeung;Rong He;Michael To;Yong Hu","doi":"10.1109/TNSRE.2025.3544551","DOIUrl":"10.1109/TNSRE.2025.3544551","url":null,"abstract":"As computer science progresses, neuromusculoskeletal models are increasingly applied in clinical settings, particularly when studying abnormal characteristics in patients with cerebral palsy. Digital neuromusculoskeletal models enable researchers and clinicians to gain a deeper understanding of movement mechanisms, providing additional insights for diagnosis and treatment. While biomechanical simulation platforms like OpenSim offer standardized neuromusculoskeletal models for simulation, relying on generic healthy models to simulate movements in cerebral palsy patients can lead to inaccuracies. Therefore, personalized muscle-tendon parameters are essential for cerebral palsy patient models. In this study, we collected ultrasound video data of the semitendinosus muscle from two patients with cerebral palsy during the passive knee extension process. We proposed a muscle-tendon parameter personalization method and developed the individualized OpenSim models for the patients using this data. We validated the personalized models’ output fiber length and pennation angle through a series of hip flexion movement tests. The experimental results demonstrate that using the personalized muscle model for cerebral palsy patients produces muscle fiber length and pennation angle more closely aligned with ultrasound-measured values. After personalization, the RMSE between model output and ultrasound measurement of muscle fiber length and pennation angle decreased by 96.80% and 61.80%, respectively, averaged across both subjects. This study introduces a method for determining muscle-tendon parameters in cerebral palsy patients’ digital neuromusculoskeletal models, providing researchers and clinicians with more precise biomechanical information. These insights can better inform the treatment of cerebral palsy patients, ultimately enhancing therapeutic outcomes.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1079-1087"},"PeriodicalIF":4.8,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10900608","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Signal Characteristics, Motor Cortex Engagement, and Classification Performance of Combined Action Observation, Motor Imagery and SSMVEP (CAMS) BCI","authors":"Aravind Ravi;Paul Wolfe;James Tung;Ning Jiang","doi":"10.1109/TNSRE.2025.3544479","DOIUrl":"10.1109/TNSRE.2025.3544479","url":null,"abstract":"Motor imagery (MI)-based Brain-Computer Interfaces (BCIs) have shown promise in engaging the motor cortex for recovery. However, individual responses to MI-based BCIs are highly variable and relatively weak. Conversely, combined action observation (AO) and motor imagery (MI) paradigms have demonstrated stronger responses compared to AO or MI alone, along with enhanced cortical excitability. In this study, a novel BCI called Combined AO, MI, and Steady-State Motion Visual Evoked Potential (SSMVEP) (CAMS) was proposed. CAMS was designed based on gait observation and imagination. Twenty-five healthy volunteers participated in the study with CAMS serving as the intervention and SSMVEP checkerboard as the control condition. We hypothesized the CAMS intervention can induce observable increases in the negativity of the movement-related cortical potential (MRCP) associated with ankle dorsiflexion. MRCP components, including Bereitschaftspotential, were measured pre- and post-intervention. Additionally, the signal characteristics of the visual and motor responses were quantified. Finally, a two-class visual BCI classification performance was assessed. A consistent increase in negativity was observed across all MRCP components in signals over the primary motor cortex, compared to the control condition. CAMS visual BCI achieved a median accuracy of 83.8%. These findings demonstrate the ability of CAMS BCI to enhance cortical excitability in relation to movement preparation and execution. The CAMS stimulus not only evokes SSMVEP-like activity and sensorimotor rhythm but also enhances the MRCP. These findings contribute to the understanding of CAMS paradigm in enhancing cortical excitability, consistent and reliable classification performance holding promise for motor rehabilitation outcomes and future BCI design considerations.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1004-1013"},"PeriodicalIF":4.8,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10898016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143556757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of 3D Stimuli With Frequency Ranges, Patterns, and Shapes on SSVEP-BCI Performance in Virtual Reality","authors":"Zihao Wei;Yanfei Lin;Jiayi Chen;Shuo Pan;Xiaorong Gao","doi":"10.1109/TNSRE.2025.3544308","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3544308","url":null,"abstract":"Traditional steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) systems offer stability and simplicity in evoking brain responses, but their practical utility is limited by immovable screens for visual stimuli. Virtual Reality (VR) technology provides a more natural and immersive environment to evoke SSVEP signals. However, the design methods for visual stimuli in VR environments remain to be explored, especially under the stereoscopic vision conditions. This study investigated the effects of 3D stimuli with frequency ranges, patterns, and shapes on the performance and user experiences of VR-SSVEP. There were four patterns including three-dimensional (3D) flicker, two-dimensional (2D) flicker, 3D checkerboard, and 3D quick response (QR) code with four shapes comprising cube, sphere, cylinder, and cone at low (9-15Hz), medium (18-24Hz), and high frequencies (30-36Hz). Both offline and online experiments were conducted to analyze the effects of different parameter combinations on SSVEP-BCI performance, and a questionnaire was exploited to evaluate user experiences. Compared to high frequency range, the low and medium frequency ranges had better performance and lower user experiences. 3D checkerboard and 3D QR code patterns showed significantly better user experiences than 3D and 2D flickers for all frequency ranges. With a high level of classification performance, 3D checkerboard and 3D QR code patterns in medium frequency range could synthetically enhance the system performance and user experiences. These results could provide significant value for SSVEP-BCI application in VR environments.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"890-899"},"PeriodicalIF":4.8,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10898078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simultaneous and Proportional Control Based on an Enhanced Musculoskeletal Model","authors":"Lizhi Pan;Diyi Liu;Ruyi Wang;Jinhua Li","doi":"10.1109/TNSRE.2025.3543912","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3543912","url":null,"abstract":"Recently, the musculoskeletal model (MM) has been widely studied for decoding movement intent from electromyography (EMG) signals. However, the decoding performance of the MM is impaired for the coordinated movements of multiple degrees of freedom (DoFs) due to the crosstalk between signals of multiple muscles. To address this problem, this study proposed an enhanced MM for 3-DoF motion prediction by taking the “divide and conquer” (DC) strategy and integrating the non-negative matrix factorization (NMF) algorithm, which is named as DC-NMF-MM. The control signals of wrist flexion/extension and MCP flexion/extension were obtained from four independent muscles, and the control signals of wrist pronation/supination were obtained from eight-channel surface EMG signals. Eight non-disabled subjects were recruited for offline and online experiment. For offline experiment, another two MMs were established and taken as the control groups for validation of the proposed DC-NMF-MM, including the MM totally taking the NMF algorithm (T-NMF-MM) and that partly taking the NMF algorithm (P-NMF-MM) for predicting the wrist pronation/supination only. The Pearson’s correlation coefficient and the normalized root mean square error were employed to compare the prediction performance of three models. The results showed that the proposed method performs better than the other two models. Moreover, artificial neural network and linear regression model were established to compare with the proposed model and the results showed that DC-NMF-MM is more accurate in predicting joint Angle. For online experiment, a general 3-DOF musculoskeletal model based on DC-NMF-MM was established and the completion time, the number of overshoots, and the path efficiency were taken as evaluation indexes. The results further demonstrated the feasibility of the proposed method to achieve 3-DoF motion control. The proposed enhanced MM provides a prerequisite for the realization of clinical hand myoelectric control.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"847-857"},"PeriodicalIF":4.8,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10896736","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}