IEEE Transactions on Neural Systems and Rehabilitation Engineering最新文献

{"title":"Differential Cortical Responses of Functional and Sensory Electrical Stimulation in Closed-Loop Tremor Suppression for Parkinson’s Disease","authors":"Xiaoqi Zhao;Tinglan Huang;Mengyue Jin;Hongbo Zhao;Yu Shi;Yanlin Wang;Xiao Shen;Zhen Li;Qingqing Shi;Xiaodong Zhu;Lin Meng","doi":"10.1109/TNSRE.2025.3591134","DOIUrl":"10.1109/TNSRE.2025.3591134","url":null,"abstract":"Functional electrical stimulation (FES) and sensory electrical stimulation (SES) are widely used in tremor suppression for Parkinson’s disease (PD), however, their therapeutic efficacy varies significantly across individuals. This study investigated the differential cortical effects of FES and SES during closed-loop tremor suppression in PD patient, aiming to identify neurophysiological biomarkers for guiding personalized neuro modulation strategies. We developed an inertial based closed-loop tremor suppression system that delivers out-of-phase FES and continuous SES based on real-time tremor detection. Fifteen PD patients were recruited in tremor suppression trials while surface electroencephalography (EEG) and inertial-based movements of hand and forearm were measured. Both FES and SES significantly reduced tremor amplitude, with FES showing overall greater suppression (hand suppression rate: 60.72% vs. 48.31%, p >0.05; forearm suppression rate: 62.25% vs. 54.41%, p >0.05) where substantial inter-individual variability was observed. EEG analysis revealed that FES induced contralateral beta-band event-related desynchronization (<inline-formula> <tex-math>$beta $ </tex-math></inline-formula>-ERD), whereas SES elicited beta-band event-related synchronization (<inline-formula> <tex-math>$beta $ </tex-math></inline-formula>-ERS). These distinct cortical response patterns were significantly correlated with tremor suppression performance (FES <inline-formula> <tex-math>$beta $ </tex-math></inline-formula>-ERD: r = -0.629, p = 0.012; SES <inline-formula> <tex-math>$beta $ </tex-math></inline-formula>-ERS: r = 0.679, p = 0.005). Resting-state spectral analysis further revealed modality-specific changes in alpha power across sensorimotor regions. These findings revealed functional neurodynamic signatures associated with individual responsiveness to stimulation. The observed <inline-formula> <tex-math>$beta $ </tex-math></inline-formula>-band oscillatory responses may serve as candidate biomarkers for predicting individual treatment outcomes, offering a potentially biomarker-guided approach for personalized neuromodulation for PD tremor.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"2814-2822"},"PeriodicalIF":5.2,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11087652","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144690086","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":"A Novel Hybrid Brain–Computer Interface Integrating Motor Imagery and Multiple Visual Stimuli","authors":"Chao Zhang;Guojing Li;Xiaopei Wu;Xiangping Gao","doi":"10.1109/TNSRE.2025.3591616","DOIUrl":"10.1109/TNSRE.2025.3591616","url":null,"abstract":"Brain-Computer Interface (BCI) that integrate Motor Imagery (MI) with Steady-State Visual Evoked Potentials (SSVEP) or Overt Spatial Attention (OSA) have demonstrated superior performance compared to MI only BCI. Nonetheless, the exploration of BCI that combine MI with visual tasks remains limited, and the synchronization between MI and visual tasks is often weak. To address this gap, our study introduces a novel BCI paradigm that combines MI with two visual tasks: SSVEP and OSA. In this paradigm, dynamic images depicting left and right arm movements flash at distinct frequencies, serving as visual stimuli positioned on both sides of the screen. Four classification methods are used for testing. The MI+SSVEP+OSA paradigm achieves higher average accuracy than the MI, MI+SSVEP, and MI+OSA paradigms. This validates the effectiveness of our novel paradigm and confirms the feasibility of simultaneously integrating MI with two visual stimuli. Moreover, we observe that the integration of SSVEP offers significant improvements, especially for participants who exhibit limited performance in the MI only paradigm. Additionally, our results indicate comparable performance between the MI+SSVEP and MI+OSA paradigms. Overall, this study offers valuable insights that can guide future research in hybrid BCI development, paving the way for more efficient and user-friendly BCI.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"2847-2857"},"PeriodicalIF":5.2,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11090001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144690085","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":"Enhanced Online Continuous Brain-Control by Deep Learning-Based EEG Decoding","authors":"Jiaheng Wang;Lin Yao;Yueming Wang","doi":"10.1109/TNSRE.2025.3591254","DOIUrl":"10.1109/TNSRE.2025.3591254","url":null,"abstract":"Objective: A growing amount of deep learning models for motor imagery (MI) decoding from electroencephalogram (EEG) have demonstrated their superiority over traditional machine learning approaches in offline dataset analysis. However, current online MI-based brain-computer interfaces (BCIs) still predominantly adopt machine learning decoders while falling short of high BCI performance. Yet, the generalization and advantages of deep learning-based EEG decoding in realistic BCI systems remain far unclear. Methods: We conduct a randomized and cross-session online MI-BCI study on 2D center-out tasks in 15 BCI-naive subjects. A newly proposed deep learning model named interactive frequency convolutional neural network (IFNet) is leveraged and rigorously compared with the prevailing benchmark namely filter-bank common spatial pattern (FBCSP) for online MI decoding. Results: Through extensive online analysis, the deep learning decoder consistently outperforms the classical counterpart across various performance metrics. In particular, IFNet significantly improves the average online task accuracy by 20% and 27% in two sessions compared with FBCSP, respectively. Moreover, a significant cross-session training effect is observed by the IFNet model (<inline-formula> <tex-math>${P}={0}.{017}$ </tex-math></inline-formula>) while not for the controlled method (<inline-formula> <tex-math>${P}={0}.{337}$ </tex-math></inline-formula>). Further offline evaluations also demonstrate the superior performance of IFNet over state-of-the-art deep learning models. Moreover, we present unique behavioral and neurophysiological insights underlying online brain-machine interaction. Conclusion: We present one of the first studies about online MI-BCIs using deep learning, achieving substantially enhanced online performance for continuous BCI control. Significance: This study suggests the good utility of deep learning in MI-BCIs and has implications for clinical applications such as stroke rehabilitation.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"2834-2846"},"PeriodicalIF":5.2,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11087643","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144682561","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":"Low-Cost Vision-Based 3-D Elbow Tracking for Post-Stroke Rehabilitation: Development and Pilot Evaluation of a Serious Game","authors":"Julia Tannus;Camille Alves;Caroline Valentini;Yann Morere;Guy Bourhis;Pierre Pino;Eduardo Naves","doi":"10.1109/TNSRE.2025.3591104","DOIUrl":"10.1109/TNSRE.2025.3591104","url":null,"abstract":"Stroke is a leading contributor to long-term disability worldwide, and rehabilitation often relies on costly devices, limited infrastructure, or labor-intensive protocols. While virtual reality-based exergames have gained popularity for promoting patient engagement, most rely on proprietary sensors or wearable electronics, limiting accessibility and clinical adaptability. This study presents the design, implementation, and pilot evaluation of a custom exergame that estimates the 3D elbow angle using a single RGB camera and two colored spheres as markers, eliminating the need for specialized hardware. The proposed system performs camera calibration, color segmentation, geometric 3D reconstruction, and real-time elbow angle estimation using low-cost equipment. Extensive technical tests revealed robust performance, with angular errors below 5° for joint amplitudes under 110°, and consistent accuracy across different lighting conditions, marker sizes, and distances. Additional tests showed that excessive sphere velocity (>20 cm/s) or proximity to image corners increased error due to motion blur and lens distortion, respectively. The system outperformed the AI-based MediaPipe framework in occluded-arm scenarios. Regression analysis showed strong correlation (r =0.70) between movement velocity and angular error. Usability testing with eight post-stroke participants yielded a mean SUS score of 92.5/100. The proposed solution is a promising alternative for home-based, sensor-free rehabilitation, supporting personalized exercise routines and remote progress monitoring.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"2882-2891"},"PeriodicalIF":5.2,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11087653","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144682581","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":"Generative Network Model Reveals Different Trajectories in Brain Networks of PSA and Stroke Patients","authors":"Kangli Dong;Yuming Zhong;Lu Zhang;Wei Liang;Yue Zhao;Jun Liu;Siya Chen;Seedahmed S. Mahmoud;Yu Sun","doi":"10.1109/TNSRE.2025.3590826","DOIUrl":"10.1109/TNSRE.2025.3590826","url":null,"abstract":"Post-stroke aphasia (PSA), induced by acute brain injury, is an acquired language disorder resulting from stroke, primarily characterized by impairments across multiple linguistic functions including spontaneous speech, auditory comprehension, repetition, naming, reading, and writing. Previous studies have demonstrated that the topological features of healthy brains align with complex networks, whereas key topological features in PSA patients (e.g., interhemispheric connectivity, functional connectivity (FC) of language networks) undergo significant alterations due to acute brain injury. However, traditional graph-theoretical approaches fail to elucidate the dynamic evolutionary patterns underlying functional reorganization in brain networks. Moreover, existing research lacks systematic exploration of trajectory characteristics and economic cost regulation mechanisms in network generation among PSA patients. To address these gaps, this study introduces a framework based on generative network modeling, integrating non-geometric rules (power-law functions of topological relationships) and geometric rules (connection distance calculations) to simulate the formation process of functional brain networks. By parametrically modulating the balance between nodal connection propensity and distance cost, and comparing the optimal matching between simulated and observed networks, we explored the evolutionary mechanism of brain networks in PSA patients. Key findings include: For the FC matrix with 10% sparsity, 1) The homogeneous model combined with geometric distance-based economic costs generates optimal simulated networks; 2) PSA patients exhibit significantly higher absolute values of parameter &lt;inline-formula&gt; &lt;tex-math&gt;$eta $ &lt;/tex-math&gt;&lt;/inline-formula&gt; compared to general stroke patients (&lt;inline-formula&gt; &lt;tex-math&gt;${p}lt {0}.{05}$ &lt;/tex-math&gt;&lt;/inline-formula&gt;), indicating increased economic costs for connections with distal nodes; 3) PSA patients show the highest &lt;inline-formula&gt; &lt;tex-math&gt;$gamma $ &lt;/tex-math&gt;&lt;/inline-formula&gt; values, with significant reduction in inter-nodal connection propensity versus healthy controls (&lt;inline-formula&gt; &lt;tex-math&gt;${p}lt {0}.{05}$ &lt;/tex-math&gt;&lt;/inline-formula&gt;), suggesting impaired network integration efficiency; 4) Trajectory analysis reveals decreased parametric values in thalamus-related regions but elevated values in occipital and cerebellar regions among PSA patients, with distance costs showing negative correlation with stroke patients (&lt;inline-formula&gt; &lt;tex-math&gt;${R}^{{2}}={0}.{86}$ &lt;/tex-math&gt;&lt;/inline-formula&gt;), uncovering region-specific trajectories of functional reorganization around lesions. By constructing a computational model incorporating economic clustering rules, this study clarifies differential network evolution patterns between PSA and general stroke patients, provides theoretical foundations for targeted neuromodulation and intervention strategy optimization, and addresses the limitations of tra","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"2870-2881"},"PeriodicalIF":5.2,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11087246","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144682562","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":"Human-in-the-Loop Optimization of the Stiffness and Alignment of a Prosthetic Foot to Reduce the Metabolic Cost of Walking","authors":"Thijs Tankink;Han Houdijk;Johnnidel Tabucol;Marco Leopaldi;Juha M. Hijmans;Raffaella Carloni","doi":"10.1109/TNSRE.2025.3590581","DOIUrl":"10.1109/TNSRE.2025.3590581","url":null,"abstract":"Improper tuning of prosthetic foot properties to the individual user limits the efficacy of current state-of-the-art prosthetic feet in terms of walking economy. This study aims to explore the potential of human-in-the-loop optimization to individually optimize prosthetic foot stiffness and alignment to decrease the metabolic cost of walking of transtibial amputees. 10 transtibial amputees underwent an optimization protocol while walking on a treadmill with an experimental prosthetic foot with tuneable stiffness and alignment. We aimed to minimize the metabolic cost of walking by optimizing the stiffness and alignment of the prosthetic foot, using an evolutionary optimization algorithm. The metabolic cost of walking during the post-test using optimal settings was compared with the pre-test using standard settings, and the post-test using standard settings. Human-in-the-loop optimization of the tuneable prosthetic foot resulted in optimal stiffness (<inline-formula> <tex-math>$4.41~pm ~0.17$ </tex-math></inline-formula> Nm/±) and alignment (<inline-formula> <tex-math>$2.40~pm ~0.97^{circ }text {)}$ </tex-math></inline-formula> settings that differ between participants. Walking on the prosthetic foot with optimized settings during the post-test resulted in a significant reduction in metabolic cost compared to the pre-test with standard settings (−10.6%). The metabolic cost during the post-test with standard settings was in between the pre-test with standard settings (−6.6%) and the post-test with optimal settings (−4.3%), indicating that part of the decrease in cost could be explained by motor adaptation of the user. Human-in-the-loop optimization can individually tune the stiffness and alignment of a prosthetic foot to lower the metabolic cost of walking for transtibial amputees and provides different optimal settings for each individual participant. Both optimization of prosthetic components and motor adaptation of the user contributed to the reduction in metabolic cost, which corroborates that human-in-the-loop optimization could enhance the efficacy of prosthetic devices.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"2823-2833"},"PeriodicalIF":5.2,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11084995","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663901","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":"Performance Enhancement of Steady-State Visual Evoked Field-Based Brain–Computer Interfaces Incorporating MEG Source Imaging","authors":"Ye-Sung Kim;Hyojeong Han;Cheong-Un Kim;Soo-In Choi;Min-Young Kim;Chang-Hwan Im","doi":"10.1109/TNSRE.2025.3590576","DOIUrl":"10.1109/TNSRE.2025.3590576","url":null,"abstract":"Recent advancements in helmet-type magneto-encephalography (MEG) systems that operate without liquid helium, such as optically pumped magnetometer (OPM)-based MEG, have increased interest in MEG-based brain–computer interfaces (BCIs). Among various BCI paradigms, steady-state visual evoked field (SSVEF)-based BCIs have been actively studied owing to their high information transfer rate (ITR) and low demand for calibration sessions. Although MEG provides excellent spatial resolution and whole-head coverage, conventional algorithms such as the filter bank-driven multivariate synchronization index (FBMSI) do not fully exploit these advantages. To overcome this limitation, this study employed MEG source imaging to utilize information from whole-head MEG recordings fully and developed a novel weighting method called the averaged source location-based weighting (ASLW). ASLW leverages the averaged source locations of SSVEF signals to enhance BCI performance. Experimental results with 20 participants demonstrated that integrating ASLW with the FBMSI algorithm (ASLW-FBMSI) significantly improved both the classification accuracy and ITR across all tested window sizes. Notably, the largest performance gains included a 13.9% accuracy improvement at a 3-s window size and a 13.1 bits/min increase in ITR at a 2.5-s window size. Additionally, the ASLW-FBMSI algorithm exhibited a short processing delay of 0.107 s at a 4-s data length and was successfully validated in online BCI experiments with 20 participants. Although tested in SQUID-MEG in this study, our findings demonstrate the effectiveness of ASLW in significantly enhancing the overall performance of SSVEF-based BCIs.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"2806-2813"},"PeriodicalIF":4.8,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11084979","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663902","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":"Brain-Shapelet: A Framework for Capturing Instantaneous Abnormalities in Brain Activity for Autism Spectrum Disorder Diagnosis","authors":"Yijie Ren;Zhengwang Xia;Yudong Zhang;Zhuqing Jiao","doi":"10.1109/TNSRE.2025.3590343","DOIUrl":"10.1109/TNSRE.2025.3590343","url":null,"abstract":"Some symptoms of Autism Spectrum Disorder (ASD), such as anxiety and depression, often manifest intermittently rather than continuously, complicating the identification of reliable pathophysiological biomarkers. Meanwhile, functional connectivity networks (FCNs) generate high-dimensional connectomes, making it difficult to accurately capture instantaneous abnormal biomarkers of neurological disorders. To address this issue, we propose a framework, called Brain-Shapelet, to extract discriminative subsequences (Shapelets) from functional magnetic resonance imaging (fMRI) data for capturing instantaneous abnormalities in brain activity. It applies random walk algorithm on group-representative brain network to obtain brain region sets, and aggregates their blood oxygen level-dependent (BOLD) signals to extract Shapelets that reflect the associations between different brain regions at the same time point. Specially, we develop a feature selection strategy to reduce redundancy in Shapelets and optimize classification performance. Brain-Shapelet places greater emphasis on short-term brain activity alterations, allowing it to capture instantaneous abnormalities more effectively. It is evaluated on the ABIDE dataset and achieves a classification accuracy of 82.8%, significantly outperforming traditional brain network modeling methods. The proposed co-occurrence rate, occurrence frequency, and Gini coefficient metrics quantify the contributions of brain regions from the perspective of Shapelets, offering valuable insights for ASD diagnosis.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"2770-2780"},"PeriodicalIF":4.8,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11085005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663900","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":"Age-Related Differences in Bimanual Isometric Force Tracking","authors":"Elisa Galofaro;Nicola Valè;Giulia Ballardini;Nicola Smania;Maura Casadio","doi":"10.1109/TNSRE.2025.3589952","DOIUrl":"10.1109/TNSRE.2025.3589952","url":null,"abstract":"Bimanual force coordination is essential for activities of daily living. Although the age-related decline in sensorimotor function has been extensively studied, the effects of aging on the bilateral control of isometric forces remain less explored. This study used an isometric force tracking task to investigate bimanual force control in young and older adults. Participants were instructed to apply equal isometric force with both hands simultaneously by pushing against two decoupled plates, simulating the lateral faces of a box. The total force had to match a profile that included time-varying and constant phases, targeting three distinct force levels. Visual feedback of the total force was provided throughout the task. Thirty-one volunteers participated in the study: 16 younger adults of age <inline-formula> <tex-math>$25pm 1$ </tex-math></inline-formula> (mean±std) years and 15 older participants of age <inline-formula> <tex-math>$77pm 7$ </tex-math></inline-formula> years. Differences between the two groups were analyzed using mixed-design ANOVA, with the group as a between-subjects factor. Results indicated that older adults exhibited reduced between-hand force correlation and reduced bilateral symmetry than the younger participants. Additionally, the older group demonstrated lower accuracy and greater force variability, with these differences being more pronounced for the time-varying phases. Notably, the percentage of total force exerted by the left hand was negatively correlated with the disparity between the left and right coefficients of variation. This study confirms previous findings on the effect of aging on bimanual force control and provides evidence suggesting that the contribution of each hand may depend on the variability in force exertion.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"2915-2925"},"PeriodicalIF":5.2,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11082393","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144649313","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":"Markerless Video-Based Gait Analysis in People With Multiple Sclerosis","authors":"Matteo Moro;Giorgia Marchesi;Maria Cellerino;Giacomo Boffa;Francesca Odone;Matilde Inglese;Maura Casadio","doi":"10.1109/TNSRE.2025.3589765","DOIUrl":"10.1109/TNSRE.2025.3589765","url":null,"abstract":"Gait analysis plays a crucial role in assessing mobility impairments and monitoring disease progression in individuals with Multiple Sclerosis (MS). Markerless, video-based methods offer a non-invasive, practical alternative to traditional marker-based systems, making them particularly suitable for clinical applications. This study employs a markerless video-based approach to extract spatio-temporal and kinematic parameters from 25 individuals with MS and 25 age- and sex-matched unimpaired controls. The MS cohort was divided into two subgroups based on the Expanded Disability Status Scale (EDSS): “high” disability (<inline-formula> <tex-math>$textit {EDSS} geq {3}$ </tex-math></inline-formula>) and “low” disability (<inline-formula> <tex-math>$textit {EDSS} lt {3}$ </tex-math></inline-formula>). Both normal and tandem gait patterns were evaluated. In normal gait, significant spatio-temporal and joint kinematic differences were observed between the high EDSS group and unimpaired controls, while the low EDSS group exhibited no notable deviations. In contrast, tandem gait analysis revealed significant differences in heel-to-toe distance between the low EDSS group and unimpaired controls, highlighting subtle changes that were undetectable in normal gait. These findings underscore the potential of video-based methods to enhance disease monitoring and guide targeted rehabilitation strategies in MS.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"2743-2749"},"PeriodicalIF":4.8,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11082420","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144649314","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}