IEEE Transactions on Neural Systems and Rehabilitation Engineering最新文献

{"title":"Predicting Attention Decline: An Integrated Beta-Band and SSVEP Approach for Visual Brain–Computer Interfaces","authors":"Siwen Wei;Yongzhi Huang;Haiqing Yu;Jiayuan Meng;Rui Xu;Tzyy-Ping Jung;Minpeng Xu;Dong Ming","doi":"10.1109/TNSRE.2026.3658740","DOIUrl":"https://doi.org/10.1109/TNSRE.2026.3658740","url":null,"abstract":"Real-time monitoring of sustained attention fluctuations during continuous complex tasks is vital for enhancing task performance and preventing accidents. Attention modulates neurons in the visual cortex in various ways to improve the visual sensitivity at an attended location. EEG-based brain-computer interfaces (BCIs) offer one of the most effective approaches for monitoring the state of human individuals. Whether transient responses evoked by brief stimuli, steady-state responses elicited by prolonged stimuli, or spontaneous neural oscillations, researchers can extract recognized electrophysiological features that reflect attention levels. However, unimodal features face inherent limitations, such as the low signal-to-noise ratio of transient responses and susceptibility of spontaneous rhythms to electrophysiological interference. Nevertheless, few studies have explored multimodal feature fusion for attention state monitoring. Here, we developed an innovative continuous go/no-go task to concurrently evoke both event-related potential (ERP) and steady-state visual evoked potential (SSVEP), while modulating spontaneous oscillatory activities through attentional engagement. To maximize the attentional modulation effect, we integrated the contrast-response functions of the modulation effect of attention on SSVEP and implemented 12 stimulus contrast levels to identify optimal visual stimulation intensity. Results from 25 subjects demonstrated that the decline in sustained attention during a continuous task was predictable before behavioral mistakes. Classification performance peaked at 31.60% stimulus contrast condition using the fused features combining spontaneous beta-band oscillations and SSVEP responses (average: 74.48%; best: 90.83%). These findings advance the development of more robust real-time attention monitoring systems based on BCI technology.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"997-1009"},"PeriodicalIF":5.2,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11367111","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175776","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":"Biphasic Electrostimulation Artifact Model for ECAP Extraction in Spinal Cord Stimulation","authors":"Haochen Zhang;Xuesong Luo;Bozhi Ma;Luming Li;Xulan Yin;Zhongwei Luo;Boyang Zhang;Xi Zhang;Hongda Li;Qingyu Yao;Junhong Lim","doi":"10.1109/TNSRE.2026.3658636","DOIUrl":"10.1109/TNSRE.2026.3658636","url":null,"abstract":"Evoked compound action potential (ECAP) is extensively used as biomarker for neural activity in closed-loop spinal cord stimulation (SCS). However, accurate monitoring of neural activity is challenged by the low signal-to-noise ratio of ECAP recording, primarily due to the interference from stimulation-induced artifacts. This study proposes biphasic electrostimulation artifact model (BEAM) for ECAP extraction and assesses its efficacy using an enhanced quantification approach. The BEAM model is built based on the principle of additivity and incorporates boundary conditions that account for the equivalent electrical environment and temporal relationships. A bilinear growth curve model is introduced to calculate the E-score, enabling a comparison of ECAP extraction results with other methods. The proposed approach is extensively validated using clinical data from nine-month SCS therapy. BEAM method achieves the highest E-scores and reduces stimulation artifacts clinically without distorting information related to neural activity. BEAM accurately characterizes the morphology of artifacts generated by stimulation across various neurostimulation scenarios. Our model may lead to unique interpretation of neural activation in ECAP signals, evoked by electrical stimulation especially in closed-loop controlling strategies that aim to maintain consistent neural excitation.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"880-893"},"PeriodicalIF":5.2,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11367103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146131818","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":"Musculoskeletal Simulation-Based Multi-Criteria Optimization Framework for Exoskeleton Design","authors":"Ali KhalilianMotamed Bonab;Volkan Patoglu","doi":"10.1109/TNSRE.2026.3658597","DOIUrl":"10.1109/TNSRE.2026.3658597","url":null,"abstract":"Robotic exoskeletons can enhance human locomotion by reducing its metabolic cost. Designing effective wearable assistive devices requires a systematic approach that accounts for the influence of device kinematics/dynamics and effects of assistance torques on human performance. While comprehensive human-subject experiments to evaluate multiple designs are often impractical, musculoskeletal simulations can serve as a powerful tool for optimizing exoskeleton designs and their corresponding assistance strategies. This paper presents a musculoskeletal simulation-based multi-criteria design optimization framework to systematically evaluate and compare various exoskeleton configurations under realistic physical constraints. In this study, the multi-criteria optimization framework is used to characterize the trade-off between metabolic efficiency and power use of mono- and bi-articular lower-limb exoskeleton configurations under optimal assistance torques. The multi-criteria optimization results provide a fair basis for rigorous comparison among various exoskeleton configurations and their corresponding optimal assistance torque profiles, considering realistic actuator saturation limits and the detrimental effects of exoskeleton reflected inertia on metabolic consumption. The results offer valuable insights to guide assistive exoskeleton designs under real-world constraints.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"868-879"},"PeriodicalIF":5.2,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11367100","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146131870","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":"EEG-Based Gait Phase Decoding From Combined Action Observation and Motor Imagery","authors":"Aravind Ravi;Ning Jiang;James Tung","doi":"10.1109/TNSRE.2026.3659011","DOIUrl":"https://doi.org/10.1109/TNSRE.2026.3659011","url":null,"abstract":"Gait recovery is a crucial component of stroke rehabilitation. While Brain-Computer Interfaces (BCIs) decoding motor intent from motor imagery (MI) have shown success, their application in the area of gait phase decoding remains limited. Combining Action Observation (AO) and MI paradigms have demonstrated enhanced motor cortex activation compared to AO or MI alone. This study investigated the feasibility of decoding swing and stance phass of gait from electroencephalogaphy (EEG), via a proposed feature extraction and classification method. A novel dataset, utilizing the Combined AO, MI, and Steady-State Motion Visual Evoked Potential (SSMVEP) (CAMS-BCI) paradigm, was collected from twenty healthy volunteers. Employing an innovative labelling technique, three different classification methods were compared. Among them, broad band EEG features with a linear classifier achieved the highest average f1-score of 0.77 in gait phase classification. Additionally, the methods achieved an overall accuracy of 70% in classifying individual Swing and Stance phases based on the CAMS stimulus responses. These findings provide valuable insights for the development of novel BCI feedback mechanisms specifically targeting different phases of gait. Implementing them in future designs can potentially enhance gait recovery outcomes in post-stroke rehabilitation.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"1031-1040"},"PeriodicalIF":5.2,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11367098","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223683","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 Sequential Dual-Target TMS on the Dynamic Characteristics of Brain Networks as Revealed by fNIRS","authors":"Lingling Chen;Yanglong Wang;Congcong Huo;Guangjian Shao;Xingshuo Dong;DongSheng Xu;Zengyong Li","doi":"10.1109/TNSRE.2026.3657761","DOIUrl":"10.1109/TNSRE.2026.3657761","url":null,"abstract":"Transcranial magnetic stimulation (TMS) modulates cortical excitability to promote neuroplasticity, but single-target TMS has limited effects on multi-region synergy. Sequential dual-target TMS may enhance brain network reorganization through temporal stimulation, though its mechanisms remain unclear. This study investigates the immediate effects of single- and dual-target TMS on brain functional networks in stroke patients using functional near-infrared spectroscopy (fNIRS) and dynamic functional connectivity (dFNC) analysis. Fifteen subacute stroke patients and fifteen healthy controls underwent three interventions: single-target SMA-rTMS, single-target LDLPFC-rTMS, and sequential dual-target stimulation (Dual-rTMS). fNIRS data were analyzed via dFNC to identify connectivity states (weak, moderate, strong) and evaluate time fraction, state transitions, global efficiency, and total signal power. Results revealed three dFNC states: State 1 (weak connectivity, 44.24%), State 2 (moderate, 39.44%), and State 3 (strong, 16.32%). Single-target TMS (especially SMA-rTMS) increased time fraction in State 1, indicating low-connectivity induction, while Dual-rTMS further prolonged State 1 and reduced inefficient transitions (e.g., State <inline-formula> <tex-math>$1to 2$ </tex-math></inline-formula>: p < 0.005). Stroke patients showed impaired transitions from State <inline-formula> <tex-math>$2to 3$ </tex-math></inline-formula> (p = 0.024) versus controls. Time fraction in State 3 correlated positively with motor function scores (FMA-LE: r = 0.52, p = 0.045; BBS: r = 0.52, p = 0.047), while State 1 correlated negatively (FMA-LE: r <inline-formula> <tex-math>$= -0.52$ </tex-math></inline-formula>, p = 0.045). Sequential dual-target TMS optimizes network dynamics by sustaining low-energy states and suppressing maladaptive transitions, outperforming single-target approaches. dFNC temporal patterns may serve as biomarkers for post-stroke motor function, supporting sequential dual-target TMS as a promising rehabilitation tool.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"906-917"},"PeriodicalIF":5.2,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11363636","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146051924","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":"FastICA-Comb: A Novel Algorithm for Extracting Voluntary Electromyography and M-Wave in Functional Electrical Stimulation Scenarios","authors":"Xingjian Li;Xiang Chen;Penghui Lin;De Wu;Xu Zhang","doi":"10.1109/TNSRE.2026.3657810","DOIUrl":"10.1109/TNSRE.2026.3657810","url":null,"abstract":"Functional electrical stimulation (FES) is commonly used in clinical practice to induce muscle contractions for rehabilitation therapy. However, the collected electromyography (EMG) under FES is a mixed signal containing voluntary electromyography (VEMG), which is generated by neural system recruited motor units, and FES response, which is composed of initial spikes generated by current passage and M-waves generated by stimulation recruited motor units. To realize close-loop control of FES system or to reveal the mechanism of FES through EMG signal, it is necessary to separate the three components from mixed signal. Based on the assumption that signal sources of initial spikes and action potentials related signals (VEMG and M-waves) are independent of each other, this study presents a novel mixed FES signal decomposition algorithm termed FastICA-Comb. Specifically, in order to achieve high-quality separation of VEMG, M-waves and initial spikes, the algorithm is designed as a unique scheme combining one-stage comb filtering and two-stage fast independent component analysis (FastICA) decomposition and classification. To evaluate the proposed algorithm’s effectiveness, FES data collection experiment was conducted on 6 healthy subjects. The experimental results confirm that the FastICA-Comb algorithm has better VEMG and M-wave extraction capabilities than the classical methods including comb filter, GS-PEF, EMD-notch and blanking window in both simulated and real mixed FES signal. Therefore, the research findings provide an effective signal analysis tool for exploring the therapeutic mechanism of FES.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"918-929"},"PeriodicalIF":5.2,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11363479","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146051945","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":"Co-Adaptive Velocity and Position Control of 3-DoFs Prosthesis via Incremental Learning","authors":"Dario Di Domenico;Fabio Egle;Andrea Marinelli;Michele Canepa;Emanuele Gruppioni;Nicoló Boccardo;Matteo Laffranchi;Claudio Castellini","doi":"10.1109/TNSRE.2026.3657400","DOIUrl":"10.1109/TNSRE.2026.3657400","url":null,"abstract":"Upper-limb prosthesis control remains challenging in achieving natural and intuitive movements, especially for devices with multiple actuated degrees of freedom (DoFs), often demanding high cognitive effort. Machine learning aids in mapping phantom limb muscle patterns to prosthetic movements, but is limited by the instability of electromyographic signals over time. This study investigates two simultaneous and proportional myocontrol strategies, based on position and velocity, using incremental learning for a 3-DoFs prosthesis, allowing co-adaptation between the system and the user. Six able-bodied and five limb-difference participants performed Target Achievement Control tests over four sessions per control strategy, assessing performance, usability, workload, simultaneity, and proportionality. Results indicate that velocity control consistently outperforms position control in both populations, yielding lower errors, higher success rates and path efficiency, and lower workload. Notably, both control strategies showed significant improvement over time in the able-bodied group, while only position control improved significantly in the limb-difference group. Interestingly, no significant difference in usability was observed between the two strategies in either group. Position control promoted greater simultaneous actuation of multi-DoFs. However, the overall findings support the use of velocity-based control as a means to improve prosthetic performance and user satisfaction.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"856-867"},"PeriodicalIF":5.2,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11363318","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146040767","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":"Modeling the Heterogeneous Movements of ASD via Fine-Grained Skeleton Representation Learning","authors":"Xuna Wang;Hongwei Gao;Yanxin Cui;Jiahui Yu;Gongfa Li;Zhaojie Ju","doi":"10.1109/TNSRE.2026.3657614","DOIUrl":"10.1109/TNSRE.2026.3657614","url":null,"abstract":"As skeletal data can be collected non-invasively while preserving patient privacy, it is widely used in public medical datasets to document patient behavior. Autism Spectrum Disorder (ASD) is characterized by significant behavioral heterogeneity, reflected in the topological structure and dynamic evolution of skeletal movements. This complexity poses substantial challenges for skeleton-based behavioral analysis. Existing methods struggle to effectively utilize behavioral evolution for subject-specific reasoning, leading to suboptimal representations that lack diagnostic relevance for autism. To address this limitation, we propose a Behavioral Evolution-based Edge Reconstruction (BER) Strategy for learning autism-related behavioral representations. By reconstructing a high-granularity adjacency matrix that spans both spatial and temporal dimensions, utilizing dynamic evolution and spatial location information, BERGCN enhances behavioral reasoning. Specifically, we first compute channel-level spatial and temporal edge reconstruction parameters by performing feature compression and targeted convolution operations on the differences between neighboring frames. Based on these, the spatial edge reconstruction module is designed by combining a generic attention map with two personalized attention maps, while the temporal edge reconstruction module is implemented using flexible frame replacement and weighted aggregation. Finally, we investigate both single-modal and multimodal network architectures under various fusion strategies. We evaluate BERGCN on three autism clinical tasks and a benchmark action recognition dataset. Experimental results demonstrate competitive performance, showing improved sensitivity to subject-specific behavioral patterns while maintaining computational efficiency.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"1021-1030"},"PeriodicalIF":5.2,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11363308","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146040754","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":"Tongue-Yoga: Precision Visual Feedback Rehabilitation Improves Tongue Agility","authors":"Andrea Scarpellini;Lorenzo Di Silverio;Anna Carroll;Leora R. Cherney;Edna M. Babbitt;James L. Patton;Hananeh Esmailbeigi","doi":"10.1109/TNSRE.2026.3657728","DOIUrl":"10.1109/TNSRE.2026.3657728","url":null,"abstract":"The tongue is a uniquely agile muscular structure essential for vital tasks of speech, breathing, chewing, and swallowing, functions commonly disrupted following neurological injury. Yet, current rehabilitation approaches lack objective measures and techniques to characterize impairment and restore the tongue’s ability. Here, we introduce a clinic-friendly method that isolates and quantifies tongue agility, defined as the ability to execute rapid and precise movements, using a wireless intraoral sensing device that provides real-time visual feedback of movement. Six participants diagnosed with dysarthria completed seven one-hour intervention sessions. Tongue movement probability distributions were generated to identify individualized deviations from neurotypical patterns. An individualized visual feedback intervention was designed to redirect movement away from over-expressed regions toward under-expressed deficient areas. Across the intervention, sensing area coverage increased significantly by 10.29 %, while over-expressed areas decreased significantly by 3.99 %, and movement velocity improved significantly by 3.85 %. This pilot study provides promising preliminary evidence that precision visual feedback rehabilitation can reshape tongue movement patterns and enhance tongue agility in individuals with oral motor disorders.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"1352-1362"},"PeriodicalIF":5.2,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11363165","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146040759","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":"Interstride Variation in EEG Power Spectra of Younger and Older Adults Walking at a Range of Gait Speeds","authors":"Jacob Salminen;Chang Liu;Erika M. Pliner;Arkaprava Roy;Natalie Richer;Jungyun Hwang;Chris J. Hass;David J. Clark;Yenisel Cruz-Almeida;Todd M. Manini;Rachael D. Seidler;Daniel P. Ferris","doi":"10.1109/TNSRE.2026.3656061","DOIUrl":"10.1109/TNSRE.2026.3656061","url":null,"abstract":"Aging alters both biomechanical and neural factors related to walking, leading to reductions in preferred gait speed with age. Biomechanical variability in human walking has been an area of great interest for aging research. Neural variability has not been well studied in this context. Electroencephalography (EEG) can measure brain activity during walking, allowing us to quantify interstride variability of electrocortical activity. We recruited younger and older adults to walk (0.25-1.0 m/s) while we measured EEG interstride variability in theta, alpha, and beta power. We hypothesized that theta, alpha, and beta variability would decrease at faster walking speeds like most gait kinematic variables. We also hypothesized that older adults would have more interstride variability compared to younger due to reduced gait automaticity. We observed sensorimotor and posterior parietal cortices for their roles in motor action and sensory processing. Interstride variability in theta power lessened with faster walking speeds in posterior parietal cortex, and Interstride variability in alpha and beta power lessened in both sensorimotor and posterior parietal cortex. Further, we found that older adults had less interstride variability than younger adults, primarily in alpha and beta. We also observed interstride phasic alignment of electrocortical activity across the gait cycle. We found broadband increases in interstride phase alignment across the gait cycle, and that older higher functioning adults had greater phase alignment in gamma (30-50 Hz) than younger adults in parietal cortex. These findings suggest that the automaticity of gait is greater at faster walking speeds, and that older adults’ reduced automaticity of gait may be unrelated to electrical brain activity.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"952-965"},"PeriodicalIF":5.2,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11358987","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146003315","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}