IEEE Transactions on Neural Systems and Rehabilitation Engineering最新文献

{"title":"A Novel 3D Paradigm for Target Expansion of Augmented Reality SSVEP","authors":"Beining Cao;Charlie Li-Ting Tsai;Nan Zhou;Thomas Do;Chin-Teng Lin","doi":"10.1109/TNSRE.2025.3562217","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3562217","url":null,"abstract":"Steady-State Visual Evoked Potentials (SSVEP) have proven to be practical in Brain-Computer Interfaces (BCI), particularly when integrated with augmented reality (AR) for real-world application. However, unlike conventional computer screen-based SSVEP (CS-SSVEP), which benefits from stable experimental environments, AR-based SSVEP (AR-SSVEP) systems are susceptible to the interference of real-world environment and device instability. Particularly, the performance of AR-SSVEP significantly declines as the target frequency increases. Therefore, our study introduced a 3D paradigm that combines flicker frequency with rotation patterns as stimuli, enabling expansion of target sets without additional frequencies. In the proposed design, in addition to the conventional frequency-based SSVEP feature, bio-marker elicited by visual perception of rotation was investigated. An experimental comparison between this novel 3D paradigm and a traditional 2D approach, which increases targets by adding frequencies, reveals significant advantages. The 12-class 3D paradigm achieved an accuracy of 76.5% and an information transfer rate (ITR) of 70.42 bits/min using 1-second EEG segments. In contrast, the 2D paradigm exhibited a lower performance with 72.07% accuracy and 62.28 bits/min ITR. The result underscores the 3D paradigm’s superiority in enhancing the practical applications of SSVEP-based BCIs in AR settings, especially with shorter time windows, by effectively expanding target recognition without compromising system efficiency.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1562-1573"},"PeriodicalIF":4.8,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10969814","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896315","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 Graph-Based Multimodal Fusion Framework for Assessment of Freezing of Gait in Parkinson’s Disease","authors":"Ningcun Xu;Chen Wang;Liang Peng;Xiao-Hu Zhou;Jingyao Chen;Zhi Cheng;Zeng-Guang Hou","doi":"10.1109/TNSRE.2025.3561942","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3561942","url":null,"abstract":"Freezing of Gait (FOG) is a significant symptom contributing to gait dysfunction in Parkinson’s disease (PD) patients. Most current methods for assessing FOG severity often overlook the interpretability of the extracted gait features. In this study, we design a multimodal gait feature dataset with rich physical significance, including kinematics, kinetics, and spatiotemporal modalities. We also propose a graph-based multimodal fusion framework (GMFF) to accurately quantify FOG severity. GMFF employs the graph attention mechanism to extract modality-specific features and utilizes the generalized canonical correlation analysis (GCCA) algorithm as the core of the feature fusion module. We provide the double-hurdle output module to address the impact of the zero-inflation problem on the performance of GMFF. We evaluate the performance of GMFF on a public PD gait database using five-fold cross-validation. The results demonstrate that GMFF achieves an accuracy of 0.978 in identifying patients with FOG and a root mean square error of 0.449 in quantifying FOG severity. Using the interpretability of GMFF, we identify the gait feature set that effectively characterizes the gait patterns of PD patients and then explore the impact of FOG symptoms on their walking ability under both the “ON” and “OFF” medication states. Thus, this study has the potential to provide valuable insights into the clinical monitoring and management of PD patients.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1539-1549"},"PeriodicalIF":4.8,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10967365","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888391","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":"An Exploration on Aperiodic Activities and Transient Oscillations During Semantic Processing: A Study With Wearable MEG","authors":"Xiaoyu Liang;Yuyu Ma;Ruonan Wang;Huanqi Wu;Changzeng Liu;Fuzhi Cao;Nan An;Min Xiang;Yueyang Zhai;Xiaolin Ning","doi":"10.1109/TNSRE.2025.3561356","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3561356","url":null,"abstract":"The processing of semantic information is pivotal in language cognition. However, there is a scarcity of research exploring the semantic-related patterns associated with aperiodic and transient periodic brain activities. In this study, recently developed algorithms were employed to parameterize the time-frequency characteristics of neural activities captured with optically pumped magnetometers-based wearable Magnetoencephalography from participants engaged in a Chinese semantic-based task. This study elucidated the neural mechanisms during semantic processing, in relation to transient oscillations and aperiodic activity. Additionally, the results demonstrated that these parameterized features could serve as indicators for decoding semantics. These findings may offer novel contribution to analyzing the mechanism of semantic perception, which will be potential to rehabilitation of language disorders with OPM-MEG.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1473-1485"},"PeriodicalIF":4.8,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10966977","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875171","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 Multimodal Approach for Early Identification of Mild Cognitive Impairment and Alzheimer’s Disease With Fusion Network Using Eye Movements and Speech","authors":"Hasnain Ali Shah;Sami Andberg;Anne M. Koivisto;Roman Bednarik","doi":"10.1109/TNSRE.2025.3561043","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3561043","url":null,"abstract":"Detecting Alzheimer’s disease (AD) in its earliest stages, particularly during an onset of Mild Cognitive Impairment (MCI), remains challenging due to the overlap of initial symptoms with normal aging processes. Given that no cure exists and current medications only slow the disease’s progression, early identification of at-risk individuals is crucial. The combination of eye-tracking and speech analysis offers a promising diagnostic solution by providing a non-invasive method to examine differences between healthy controls and individuals with MCI, who may progress to develop AD. In this study, we analyzed a multimodal clinical eye-tracking and speech dataset collected from 78 participants (37 controls, 20 MCI, and 21 AD) during the King-Devick test and a reading task to classify and diagnose MCI/AD versus healthy controls. To that end, we developed a Fusion Neural Network, a deep learning-based classification model that integrates gaze and speech-derived features, including pupil size variations, fixation duration, saccadic movements, and speech delay, to improve MCI diagnosis performance. We achieved an average classification accuracy of 79.2% for MCI diagnosis and 82% for AD. Our findings indicate that features related to pupil size and eye-speech temporal dynamics are strong indicators for detection tasks. Moreover, the results indicate that using multimodal data (gaze + speech) significantly improves classification accuracy compared to unimodal data from speech or gaze alone.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1449-1459"},"PeriodicalIF":4.8,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10965865","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860785","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":"Test-Retest Reliability of ERP and Auditory Steady State Response in Patients With Disorders of Consciousness","authors":"Haili Wang;Ning Yin;Yuxin Zhang;Zhongzhen Li;Guobin Zhang;Shaoya Yin;Keke Feng;Guizhi Xu","doi":"10.1109/TNSRE.2025.3554536","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3554536","url":null,"abstract":"To identify the optimal auditory evoked strategy for assessing the level of consciousness in patients with disorders of consciousness (DoC) based on event related potentials (ERP) and amplitude modulation auditory steady state response (ASSR) and its test-retest reliability, this study conducted calling names and pure tone ERP evocations, as well as amplitude-modulated ASSR evocations with 440 Hz, 1000 Hz, and 2000 Hz carrier frequencies, in recruited prolonged DoC patients. The results showed that the MMN amplitude (P<0.05) and P300 (P<0.001) of calling names was higher than that of pure tones, while the P300 latency (P<0.05) was shorter. Compared to 1000 Hz and 2000 Hz, the ASSR of 440 Hz carrier frequency exhibited more pronounced early ERSP components (P=0.001), late ERSP components (P=0.011), early ITPC components (P=0.005), and late ITPC components (P=0.008). Significant differences were observed between minimally conscious state (MCS) and vegetative state (VS) patients in P300 amplitude, MMN amplitude, P300 latency, early ERSP component, late ERSP component, and early ITPC component. P300 amplitude (MCS: ICC=0.783; VS: ICC=0.750) and early ERSP component (MCS: ICC=0.780; VS: 0.759) had excellent test-retest reliability. Correlation analysis with the CRS-R scale indicated significant positive correlations between CRS-R scores and P300 amplitude (MCS: r=0.74; VS: r=0.60), early ERSP component (MCS: r=0.72; VS: r=0.52), and early ITPC component (MCS: r=0.71; VS: r=0.49) in both MCS and VS patients. The P300 amplitude and the early ERSP component of ASSR are reliable indicators that may complement each other in assessing the patients’ level of consciousness.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1328-1337"},"PeriodicalIF":4.8,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964490","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839901","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":"SMANet: A Model Combining SincNet, Multi-Branch Spatial—Temporal CNN, and Attention Mechanism for Motor Imagery BCI","authors":"Danjie Wang;Qingguo Wei","doi":"10.1109/TNSRE.2025.3560993","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3560993","url":null,"abstract":"Building a brain-computer interface (BCI) based on motor imagery (MI) requires accurately decoding MI tasks, which poses a significant challenge due to individual discrepancy among subjects and low signal-to-noise ratio of EEG signals. We propose an end-to-end deep learning model, Sinc-multibranch-attention network (SMANet), which combines a SincNet, a multibranch spatial-temporal convolutional neural network (MBSTCNN), and an attention mechanism for MI-BCI classification. Firstly, Sinc convolution is utilized as a band-pass filter bank for data filtering; Second, pointwise convolution facilitates the effective integration of feature information across different frequency ranges, thereby enhancing the overall feature expression capability; Next, the resulting data are fed into the MBSTCNN to learn a deep feature representation. Thereafter, the outputs of the MBSTCNN are concatenated and then passed through an efficient channel attention (ECA) module to enhance local channel feature extraction and calibrate feature mapping. Ultimately, the feature maps yielded by ECA are classified using a fully connected layer. This model SMANet enhances discriminative features through a multi-objective optimization scheme that integrates cross-entropy loss and central loss. The experimental outcomes reveal that our model attains an average accuracy of 80.21% on the four-class MI dataset (BCI Competition IV 2a), 84.02% on the two-class MI dataset (BCI Competition IV 2b), and 72.70% on the two-class MI dataset (OpenBMI). These results are superior to those of the current state-of-the-art methods. The SMANet is capable to effectively decoding the spatial-spectral-temporal information of EEG signals, thereby enhancing the performance of MI-BCIs.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1497-1508"},"PeriodicalIF":4.8,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10965876","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888393","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":"Efficient, Robust, and Accurate CNN Predictor for Neuronal Activation in Directional Deep Brain Stimulation","authors":"Shunjing Wang;Ru Ma;Qunran Yuan;Hongda Li;Changqing Jiang","doi":"10.1109/TNSRE.2025.3561122","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3561122","url":null,"abstract":"The programming of clinical deep brain stimulation (DBS) systems involves numerous combinations of stimulation parameters, such as stimulus amplitude, pulse width, and frequency. As more complex electrode designs, such as directional electrodes, are introduced, the traditional trial-and-error approach to manual DBS programming becomes increasingly impractical. Visualization of the volume of tissue activated (VTA) can assist in selecting stimulation parameters by showing the direct effects of DBS on neural tissue. However, the standard method for VTA calculation, which involves modeling biological nerve fibers, is highly time-consuming and limits clinical applicability. In this study, we used finite element models (FEM) of implanted DBS systems to compute electric fields and obtained a large dataset of axonal responses under electrical stimulation using multicompartment cable models. We then trained a convolutional neural network (CNN) to replace the cable models. The CNN model’s performance in calculating VTA was evaluated across various electrode configurations and stimulation parameters, and compared with existing activation function (AF) methods. The CNN model achieved a mean absolute error (MAE) of 0.032V in predicting nerve fiber activation thresholds, demonstrating greater stability and accuracy in VTA prediction compared to the AF method. Additionally, the CNN reduced computation time by five orders of magnitude compared to standard axonal modeling methods. We demonstrate that the CNN-based neural fiber predictor can quickly, accurately, and robustly predict neural activation responses to DBS, thereby improving the efficiency of DBS programming.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1685-1694"},"PeriodicalIF":4.8,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10965875","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925135","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":"SRRNet: Unseen SSVEP Response Regression From Stimulus for Cross-Stimulus Transfer in SSVEP-BCIs","authors":"Ximing Mai;Jianjun Meng;Yi Ding;Xiangyang Zhu;Cuntai Guan","doi":"10.1109/TNSRE.2025.3560434","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3560434","url":null,"abstract":"The prolonged calibration time required by steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) poses a significant challenge to real-life applications. Cross-stimulus transfer emerges as a promising solution, wherein a model trained on a subset of classes (seen classes) can predict both seen and unseen classes. Existing approaches extracted common components from SSVEP templates of seen classes to construct templates for unseen classes; however, they are limited by the class-specific activities and noise contained in these components, leading to imprecise templates that degrade classification performance. To address this issue, this study proposed an SSVEP Response Regression Network (SRRNet), which learned the regression mapping between sine-cosine reference signals and SSVEP templates using seen class data. This network reconstructed SSVEP templates for unseen classes utilizing their corresponding sine-cosine signals. Additionally, an SSVEP template regressing and spatial filtering (SRSF) framework was introduced, where both test data and SSVEP templates were projected by task-related component analysis (TRCA) spatial filters, and correlations were computed for target prediction. Comparative evaluations on two public datasets revealed that our method significantly outperformed state-of-the-art methods, elevating the information transfer rate (ITR) from 173.33 bits/min to 203.79 bits/min. By effectively modeling the regression from sine-cosine reference signals to SSVEP templates, SRRNet can construct SSVEP templates for unseen classes without training samples from those classes. By integrating regressed SSVEP templates with spatial filtering-based methods, our method enhances cross-stimulus transfer performance in SSVEP-BCIs, thus advancing their practical applicability. The code is available at <uri>https://github.com/MaiXiming/SRRNet</uri>.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1460-1472"},"PeriodicalIF":4.8,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964365","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875170","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 Adaptive Propulsion Enhancement eXperience (APEX) System: Development and Preliminary Validation for Enhancing Gait Propulsion in Stroke Survivors","authors":"Seoung Hoon Park;Hyunje Park;Jooeun Ahn;Beom-Chan Lee","doi":"10.1109/TNSRE.2025.3560324","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3560324","url":null,"abstract":"This study presents the development and preliminary validation of a novel system, called APEX (Adaptive Propulsion Enhancement eXperience), which aims to enhance gait propulsion in stroke survivors. The APEX system utilizes a dual-belt instrumented treadmill capable of measuring ground reaction forces and modulating belt speed in real time to provide visual biofeedback with dynamic propulsion promotion. We developed two propulsion promotion modes: the propulsion-facilitating mode, which extends ground contact time to elicit intrinsic propulsive effort and the propulsion-augmenting mode, which increases propulsive force by applying controlled external force. Ten chronic-stage stroke survivors (7 females and 3 males; age: <inline-formula> <tex-math>$61.40~pm ~6.96$ </tex-math></inline-formula> years) completed two experimental trials: one with the propulsion-facilitating mode, and the other with the propulsion-augmenting mode. Each trial included a baseline period without assistance (visual biofeedback and propulsion promotion) for 30 steps, a training period with assistance for 100 steps, and a post-training period without assistance for 30 steps. For each period, outcome measures (propulsive force, impulse, lower-limb kinematics, and muscle activity) were quantified. Statistical analysis revealed significant improvements in propulsive force, impulse, lower-limb kinematics, and muscle activity during both the training and post-training periods compared to the baseline period, with no significant differences between the training and post-training periods. These findings demonstrate the efficacy and reliability of the APEX system in delivering real-time, adaptive training to enhance gait propulsion. Integrating the APEX system into clinical practice has the potential to provide a scalable, patient-specific approach for post-stroke gait rehabilitation.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1486-1496"},"PeriodicalIF":4.8,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964362","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888392","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":"Temporal Evolution of Frontal Plane Center-of-Mass Transfer Asymmetry in Post-Stroke Gait","authors":"Keng-Hung Shen;Robert Lee;Hao-Yuan Hsiao","doi":"10.1109/TNSRE.2025.3559857","DOIUrl":"https://doi.org/10.1109/TNSRE.2025.3559857","url":null,"abstract":"In typical human gait, the body center-of-mass (CoM) is cyclically transferred towards and supported by each lower extremity. The magnitude of this CoM transfer can be quantified by measuring the minimum mediolateral distance between the CoM and the stance foot during each step. Individuals with hemiparesis due to stroke often show a reduced and more variable CoM transfer magnitude in paretic versus non-paretic steps, which are linked to slower walking speeds and an increased risk of falling. While the commonly observed wider and more variable paretic foot placement at initial contact likely contributes to such frontal plane CoM transfer abnormalities, other factors could continue to adjust the CoM transfer magnitude after initial contact. To understand how the CoM transfer magnitude evolves throughout the transfer process, we derived an inverted-pendulum-based equation that projects the experimentally measured instantaneous mediolateral CoM position and velocity to the CoM transfer magnitude. We first validated our derived equation by demonstrating that CoM transfer magnitude can be predicted by the CoM position and velocity at the end of the double support phase with passive inverted pendulum dynamics. We then investigated how the asymmetry of this projected CoM transfer magnitude between the paretic and non-paretic steps evolves during the transfer process. Our findings revealed that about 54% of the transfer magnitude asymmetry was established at initial contact, predominantly influenced by foot placement, while another 38% was established during the double support phase, partly due to reduced work input from the non-paretic trailing limb. Additionally, the variability in transfer magnitude was augmented during the double support phase in paretic steps. Overall, the present study introduces a physics-based method capable of predicting CoM transfer magnitude in advance of its completion, and our findings highlight the significant contribution of the double support phase, which was previously less explored, to the asymmetries in CoM transfer magnitude and variability. Our results suggest that biomechanical factors during this phase, such as trailing limb work input, could be critical targets for future research and therapeutic interventions.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1427-1438"},"PeriodicalIF":4.8,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10962267","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848802","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}