Pub Date : 2026-01-19DOI: 10.1109/TNSRE.2026.3654935
Hyunmi Lim;Hyoseon Choi;Bilal Ahmed;Yoonghil Park;Jeonghun Ku
Stroke frequently results in long-term motor deficits that impair quality of life. Action observation therapy (AOT) has shown promise for motor recovery through engagement of the mirror neuron system (MNS), yet its passive nature and lack of attentional tracking limit its neuroplasticity efficacy. To address these limitations, we developed a closed-loop Brain-Computer Interface-integrated AOT (BCI-AOT) system employing real-time Steady-State Visual Evoked Potential (SSVEP)-based attention monitoring to dynamically control therapy delivery. In a within-subject crossover study, 22 individuals with hemiplegic stroke completed both BCI-AOT and conventional AOT conditions, each consisting of five daily sessions and separated by a one-week washout. In BCI-AOT, video playback depended on sustained attentional engagement detected via SSVEPs. Behavioral outcomes (Box and Block Test [BBT], Action Research Arm Test [ARAT]) and physiological measures (Motor Evoked Potential [MEP] amplitude and latency, EEG power) were assessed pre- and post-intervention. Significant Condition $times $ Day interactions were found for both BBT and ARAT, indicating greater functional gains over time in the BCI-AOT condition. Both conditions showed increased corticospinal excitability over time, while BCI-AOT additionally exhibited distinct mu and theta modulation over time. Participants also reported greater motivation and attention after BCI-AOT compared to conventional AOT. These results suggest that BCI-AOT elicits stronger neuroplasticity responses and user engagement than standard AOT. This study supports the feasibility and clinical potential of closed-loop, attention-adaptive neurorehabilitation for stroke recovery.
中风经常导致长期运动障碍，从而影响生活质量。动作观察疗法（AOT）通过参与镜像神经元系统（MNS）显示出运动恢复的希望，但其被动性质和缺乏注意跟踪限制了其神经可塑性的效果。为了解决这些限制，我们开发了一种闭环脑机接口集成AOT （BCI-AOT）系统，采用基于实时稳态视觉诱发电位（SSVEP）的注意力监测来动态控制治疗递送。在一项受试者内交叉研究中，22名偏瘫中风患者完成了BCI-AOT和常规AOT条件，每个条件由每天五个疗程组成，间隔一周的洗脱期。在BCI-AOT中，视频播放依赖于通过ssvep检测到的持续注意力投入。评估干预前后的行为结果（Box and Block Test [BBT]、动作研究臂测试[ARAT]）和生理指标（运动诱发电位[MEP]振幅、潜伏期、脑电图功率）。在BBT和ARAT中发现了显著的条件×日相互作用，表明BCI-AOT条件下随着时间的推移功能增加更大。随着时间的推移，这两种情况都显示出皮质脊髓兴奋性的增加，而BCI-AOT也表现出明显的mu和theta调制。与常规AOT相比，BCI-AOT后参与者也报告了更大的动机和注意力。这些结果表明BCI-AOT比标准AOT引起更强的神经可塑性反应和用户参与。本研究支持闭环、注意适应性神经康复治疗脑卒中康复的可行性和临床潜力。
{"title":"Attention-Adaptive BCI-AOT System Enhances Motor Recovery and Neural Engagement After Stroke","authors":"Hyunmi Lim;Hyoseon Choi;Bilal Ahmed;Yoonghil Park;Jeonghun Ku","doi":"10.1109/TNSRE.2026.3654935","DOIUrl":"10.1109/TNSRE.2026.3654935","url":null,"abstract":"Stroke frequently results in long-term motor deficits that impair quality of life. Action observation therapy (AOT) has shown promise for motor recovery through engagement of the mirror neuron system (MNS), yet its passive nature and lack of attentional tracking limit its neuroplasticity efficacy. To address these limitations, we developed a closed-loop Brain-Computer Interface-integrated AOT (BCI-AOT) system employing real-time Steady-State Visual Evoked Potential (SSVEP)-based attention monitoring to dynamically control therapy delivery. In a within-subject crossover study, 22 individuals with hemiplegic stroke completed both BCI-AOT and conventional AOT conditions, each consisting of five daily sessions and separated by a one-week washout. In BCI-AOT, video playback depended on sustained attentional engagement detected via SSVEPs. Behavioral outcomes (Box and Block Test [BBT], Action Research Arm Test [ARAT]) and physiological measures (Motor Evoked Potential [MEP] amplitude and latency, EEG power) were assessed pre- and post-intervention. Significant Condition <inline-formula> <tex-math>$times $ </tex-math></inline-formula> Day interactions were found for both BBT and ARAT, indicating greater functional gains over time in the BCI-AOT condition. Both conditions showed increased corticospinal excitability over time, while BCI-AOT additionally exhibited distinct mu and theta modulation over time. Participants also reported greater motivation and attention after BCI-AOT compared to conventional AOT. These results suggest that BCI-AOT elicits stronger neuroplasticity responses and user engagement than standard AOT. This study supports the feasibility and clinical potential of closed-loop, attention-adaptive neurorehabilitation for stroke recovery.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"686-698"},"PeriodicalIF":5.2,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11358787","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146003352","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}
Pub Date : 2026-01-19DOI: 10.1109/TNSRE.2026.3655791
Michelle van Mierlo;Katrijn Smulders;Noël Keijsers
Daily life gait performance measures can provide ecologically valid gait characteristics, which are interesting for monitoring individuals with gait impairments. The first step in obtaining these gait characteristics is selecting walking periods from multiple day recordings. We developed and validated an algorithm for walking detection using inertial measurement units (IMU) on Both feet and compared the performance with two others in healthy individuals and those with neurologically impaired gait: 1) using Sacrum accelerometer data (Iluz et al. 2014); 2) using Single foot gyroscopic data (Ullrich et al. 2020). We also assessed which activities reduce the algorithms’ specificity for walking detection. The Both feet algorithm consisted of three stages, 1) selecting time periods potentially containing walking; 2) excluding periods not containing walking; 3) checking the selected periods for minimal walking bout requirements. For validation, 32 participants (12 healthy and 20 with neurologically impaired gait) performed 20-30 minutes of daily life activities, while wearing IMUs on both feet and the sacrum. Using labelled video recordings as reference, we calculated each algorithm’s specificity, sensitivity and accuracy for walking detection. Both feet outperformed the other algorithms on specificity (96.6% versus 92.1% and 72.1% for the Single foot en Sacrum respectively). Stair climbing was misclassified as walking most often by all algorithms. Sacrum outperformed the others on sensitivity (99.5%), but had low specificity and accuracy. The high specificity of the Both feet algorithm makes it suitable when spatiotemporal gait characteristics are of interest, and is applicable in populations with mild neurological conditions affecting gait.
日常生活步态性能测量可以提供生态有效的步态特征，这对监测步态障碍的个体很有意义。获得这些步态特征的第一步是从多天记录中选择步行周期。我们开发并验证了一种使用双脚惯性测量单元（IMU）进行行走检测的算法，并将其与健康个体和神经系统步态受损者的其他两种算法进行了比较：1)使用骶骨加速度计数据（Iluz et al. 2014）；2)使用单脚陀螺仪数据（Ullrich et al. 2020）。我们还评估了哪些活动降低了算法对步行检测的特异性。双脚算法包括三个阶段：1)选择可能包含步行的时间段；2)不包括散步时段；3)检查所选时段的最小步行回合要求。为了验证，32名参与者（12名健康参与者和20名神经性步态受损参与者）在双脚和骶骨上佩戴imu，进行20- 30分钟的日常生活活动。以标记视频为参考，我们计算了每种算法在行走检测中的特异性、灵敏度和准确性。两只脚的特异性优于其他算法（分别为96.6%和92.1%，单脚和骶骨分别为72.1%）。爬楼梯最常被所有算法错误地归类为步行。骶骨的敏感性为99.5%，但特异性和准确性较低。双脚算法的高特异性使其适用于对时空步态特征感兴趣的情况，并且适用于轻度神经系统疾病影响步态的人群。
{"title":"Feet-Based IMU Algorithm Yields High Specificity for Detection of Walking in Daily Life","authors":"Michelle van Mierlo;Katrijn Smulders;Noël Keijsers","doi":"10.1109/TNSRE.2026.3655791","DOIUrl":"10.1109/TNSRE.2026.3655791","url":null,"abstract":"Daily life gait performance measures can provide ecologically valid gait characteristics, which are interesting for monitoring individuals with gait impairments. The first step in obtaining these gait characteristics is selecting walking periods from multiple day recordings. We developed and validated an algorithm for walking detection using inertial measurement units (IMU) on Both feet and compared the performance with two others in healthy individuals and those with neurologically impaired gait: 1) using Sacrum accelerometer data (Iluz et al. 2014); 2) using Single foot gyroscopic data (Ullrich et al. 2020). We also assessed which activities reduce the algorithms’ specificity for walking detection. The Both feet algorithm consisted of three stages, 1) selecting time periods potentially containing walking; 2) excluding periods not containing walking; 3) checking the selected periods for minimal walking bout requirements. For validation, 32 participants (12 healthy and 20 with neurologically impaired gait) performed 20-30 minutes of daily life activities, while wearing IMUs on both feet and the sacrum. Using labelled video recordings as reference, we calculated each algorithm’s specificity, sensitivity and accuracy for walking detection. Both feet outperformed the other algorithms on specificity (96.6% versus 92.1% and 72.1% for the Single foot en Sacrum respectively). Stair climbing was misclassified as walking most often by all algorithms. Sacrum outperformed the others on sensitivity (99.5%), but had low specificity and accuracy. The high specificity of the Both feet algorithm makes it suitable when spatiotemporal gait characteristics are of interest, and is applicable in populations with mild neurological conditions affecting gait.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"931-940"},"PeriodicalIF":5.2,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11358952","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146003336","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}
Pub Date : 2026-01-15DOI: 10.1109/TNSRE.2026.3654843
Adriana Cancrini;Bruno Borghi;Naveed Reza Aghamohammadi;Arturo Ramirez;James L. Patton
Characterizing each person’s sensorimotor profile is crucial for designing precise and personalized motor rehabilitation therapies. Building on our previous work in system identification of human motor control dynamics, we now extend our parameter recovery technique developed in synthetic models to a real-world human experiment. This twin-based digital method actively guides the experimental design by selecting the most informative perturbations and movement conditions to most accurately identify (recover) sensory feedback gains. We applied this framework to 10 neurotypical participants, analyzing their performance during arm planar reaching movements. By combining the optimized experimental design with this forward–inverse modeling pipeline, we estimated individual sensory feedback gains. These gains were then used to simulate movement trajectories, achieving a movement prediction accuracy of 85% compared to withheld trajectories performed by the same subjects. These results validate the ability of our mathematical model to capture and explain individual sensorimotor dynamics through the identification of subject-specific feedback gains. This approach offers a promising tool for gaining insights into the roles of different sensory channels and identifying the most informative data required for efficient assessment.
{"title":"Defining Experimental Design for Human Motor Control Identification: A Novel Framework","authors":"Adriana Cancrini;Bruno Borghi;Naveed Reza Aghamohammadi;Arturo Ramirez;James L. Patton","doi":"10.1109/TNSRE.2026.3654843","DOIUrl":"10.1109/TNSRE.2026.3654843","url":null,"abstract":"Characterizing each person’s sensorimotor profile is crucial for designing precise and personalized motor rehabilitation therapies. Building on our previous work in system identification of human motor control dynamics, we now extend our parameter recovery technique developed in synthetic models to a real-world human experiment. This twin-based digital method actively guides the experimental design by selecting the most informative perturbations and movement conditions to most accurately identify (recover) sensory feedback gains. We applied this framework to 10 neurotypical participants, analyzing their performance during arm planar reaching movements. By combining the optimized experimental design with this forward–inverse modeling pipeline, we estimated individual sensory feedback gains. These gains were then used to simulate movement trajectories, achieving a movement prediction accuracy of 85% compared to withheld trajectories performed by the same subjects. These results validate the ability of our mathematical model to capture and explain individual sensorimotor dynamics through the identification of subject-specific feedback gains. This approach offers a promising tool for gaining insights into the roles of different sensory channels and identifying the most informative data required for efficient assessment.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"721-731"},"PeriodicalIF":5.2,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11355670","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145984652","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}
Pub Date : 2026-01-14DOI: 10.1109/TNSRE.2026.3654400
Kimji N. Pellano;Inga Strümke;Daniel Groos;Lars Adde;Pål Haugen;Espen Alexander F. Ihlen
Cerebral Palsy (CP) is a prevalent motor disability in children, for which early detection can significantly improve treatment outcomes. While skeleton-based Graph Convolutional Network (GCN) models have shown promise in automatically predicting CP risk from infant videos, their “black-box” nature raises concerns about clinical explainability. To address this, we introduce a perturbation framework tailored for infant movement features and use it to compare two explainable AI (XAI) methods: Class Activation Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM). First, we identify significant and non-significant body keypoints in very low and very high risk infant video snippets based on the XAI attribution scores. We then conduct targeted velocity and angular perturbations, both individually and in combination, on these keypoints to assess how the GCN model’s risk predictions change. Our results indicate that velocity-driven features of the arms, hips, and legs appear to have a dominant influence on CP risk predictions, while angular perturbations have a more modest impact. Furthermore, CAM and Grad-CAM show partial convergence in their explanations for both low and high CP risk groups. Our findings demonstrate the use of XAI-driven movement analysis for early CP prediction, and offer insights into potential movement-based biomarker discovery that warrant further clinical validation.
{"title":"Toward Biomarker Discovery for Early Cerebral Palsy Detection: Evaluating Explanations Through Kinematic Perturbations","authors":"Kimji N. Pellano;Inga Strümke;Daniel Groos;Lars Adde;Pål Haugen;Espen Alexander F. Ihlen","doi":"10.1109/TNSRE.2026.3654400","DOIUrl":"10.1109/TNSRE.2026.3654400","url":null,"abstract":"Cerebral Palsy (CP) is a prevalent motor disability in children, for which early detection can significantly improve treatment outcomes. While skeleton-based Graph Convolutional Network (GCN) models have shown promise in automatically predicting CP risk from infant videos, their “black-box” nature raises concerns about clinical explainability. To address this, we introduce a perturbation framework tailored for infant movement features and use it to compare two explainable AI (XAI) methods: Class Activation Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM). First, we identify significant and non-significant body keypoints in very low and very high risk infant video snippets based on the XAI attribution scores. We then conduct targeted velocity and angular perturbations, both individually and in combination, on these keypoints to assess how the GCN model’s risk predictions change. Our results indicate that velocity-driven features of the arms, hips, and legs appear to have a dominant influence on CP risk predictions, while angular perturbations have a more modest impact. Furthermore, CAM and Grad-CAM show partial convergence in their explanations for both low and high CP risk groups. Our findings demonstrate the use of XAI-driven movement analysis for early CP prediction, and offer insights into potential movement-based biomarker discovery that warrant further clinical validation.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"750-766"},"PeriodicalIF":5.2,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11352985","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145984698","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}
In multiple vision-demanding tasks, accurately controlling a prosthetic hand to approach a target object is particularly challenging for amputees, as visual attention diverted by other tasks forces them to rely heavily on peripheral vision. This study aims to initially validate that functionally effective sensory feedback can enhance the control of prosthetic hands during object approach under divided visual attention. To quantify prosthesis users’ ability to approach and manipulate objects using central and peripheral vision in real-life scenarios, we conducted two experimental tasks—APPROACHING and PINCH—under two visual feedback modes: full-vision and partial-vision. During the approaching process, we compared four feedback conditions: no supplementary sensory feedback (PURE), traditional continuous feedback (CONT), evenly distributed discrete feedback (ADIS), and a novel discrete strategy based on Weber’s law (WDIS) proposed in this study. Task performance was evaluated using metrics such as position error, dispersion, task completion time, and pinch failures, while psychological factors were assessed through a questionnaire. Results show that WDIS enabled more accurate and stable object approach, with shorter task completion times, which leads to better subsequent manipulation performance. This also provides participants with enhanced psychological experiences, including reduced workload and increased intuitiveness. WDIS improved prosthetic control and user experience in the simplified laboratory settings, providing a foundation for real-world applications.
{"title":"Discrete Tactile Feedback Based on Weber’s Law Enhances Prosthetic Hand Approaching Performance Under Divided Visual Attention","authors":"Xianwei Meng;Jianjun Meng;Guohong Chai;Xinjun Sheng;Xiangyang Zhu","doi":"10.1109/TNSRE.2026.3653788","DOIUrl":"10.1109/TNSRE.2026.3653788","url":null,"abstract":"In multiple vision-demanding tasks, accurately controlling a prosthetic hand to approach a target object is particularly challenging for amputees, as visual attention diverted by other tasks forces them to rely heavily on peripheral vision. This study aims to initially validate that functionally effective sensory feedback can enhance the control of prosthetic hands during object approach under divided visual attention. To quantify prosthesis users’ ability to approach and manipulate objects using central and peripheral vision in real-life scenarios, we conducted two experimental tasks—APPROACHING and PINCH—under two visual feedback modes: full-vision and partial-vision. During the approaching process, we compared four feedback conditions: no supplementary sensory feedback (PURE), traditional continuous feedback (CONT), evenly distributed discrete feedback (ADIS), and a novel discrete strategy based on Weber’s law (WDIS) proposed in this study. Task performance was evaluated using metrics such as position error, dispersion, task completion time, and pinch failures, while psychological factors were assessed through a questionnaire. Results show that WDIS enabled more accurate and stable object approach, with shorter task completion times, which leads to better subsequent manipulation performance. This also provides participants with enhanced psychological experiences, including reduced workload and increased intuitiveness. WDIS improved prosthetic control and user experience in the simplified laboratory settings, providing a foundation for real-world applications.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"674-685"},"PeriodicalIF":5.2,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11348986","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145966025","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}
Pub Date : 2026-01-13DOI: 10.1109/TNSRE.2026.3653761
Keping Liu;Guang Liu;Zhifei Zhai;Baozhen Nie;Xiaoqin Duan;Changxian Xu;Zhongbo Sun
Assessment of motor function is an important component of a post-stroke rehabilitation program. The traditional assessment process mainly relies on clinical experience and lacks quantitative analysis. To objectively assess the upper limb motor status of post-stroke hemiplegic patients, this study proposes a novel assessment method based on multi-modal feature fusion of the upper limb for task-oriented movement. Features are extracted from each modal data and input into the corresponding base classifiers. The kinematic and muscle synergy are quantified by singular value decomposition (SVD) and similarity metric index, and the results are integrated to construct an aggregated classifier for in-depth quantitative assessment of different movement modalities. To exploit the complementary nature of kinematic and muscular level assessment results, a multi-modal feature fusion scheme is proposed and a probability-based functional scoring mechanism is generated to comprehensively analyze upper extremity motor function. Experimental results show that integrating synergy analyses into the assessment system improves the classification accuracy by 2.39% and 2.31%, respectively, which can be further improved to 90.75% by fusing the features extracted from different modalities. Furthermore, the assessment results of multi-modal fusion framework are significantly correlated with standard clinical trial scores ($r$ =-0.81, $p$ =0.0147). These promising results suggest that it is feasible to apply the proposed method to the clinical assessment of hemiplegic patients after stroke.
{"title":"Quantitative Assessment of Upper Limb Multi-Modal Feature Fusion Under Task-Oriented Movement","authors":"Keping Liu;Guang Liu;Zhifei Zhai;Baozhen Nie;Xiaoqin Duan;Changxian Xu;Zhongbo Sun","doi":"10.1109/TNSRE.2026.3653761","DOIUrl":"10.1109/TNSRE.2026.3653761","url":null,"abstract":"Assessment of motor function is an important component of a post-stroke rehabilitation program. The traditional assessment process mainly relies on clinical experience and lacks quantitative analysis. To objectively assess the upper limb motor status of post-stroke hemiplegic patients, this study proposes a novel assessment method based on multi-modal feature fusion of the upper limb for task-oriented movement. Features are extracted from each modal data and input into the corresponding base classifiers. The kinematic and muscle synergy are quantified by singular value decomposition (SVD) and similarity metric index, and the results are integrated to construct an aggregated classifier for in-depth quantitative assessment of different movement modalities. To exploit the complementary nature of kinematic and muscular level assessment results, a multi-modal feature fusion scheme is proposed and a probability-based functional scoring mechanism is generated to comprehensively analyze upper extremity motor function. Experimental results show that integrating synergy analyses into the assessment system improves the classification accuracy by 2.39% and 2.31%, respectively, which can be further improved to 90.75% by fusing the features extracted from different modalities. Furthermore, the assessment results of multi-modal fusion framework are significantly correlated with standard clinical trial scores (<inline-formula> <tex-math>$r$ </tex-math></inline-formula>=-0.81, <inline-formula> <tex-math>$p$ </tex-math></inline-formula>=0.0147). These promising results suggest that it is feasible to apply the proposed method to the clinical assessment of hemiplegic patients after stroke.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"711-720"},"PeriodicalIF":5.2,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11348979","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145966030","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}
Pub Date : 2026-01-12DOI: 10.1109/TNSRE.2026.3652812
Shuo Guan;Yuhang Li;Yuanyuan Gao;Ran Yin;Yuxi Luo;Jiuxing Liang;Juan Zhang;Yingchun Zhang;Rihui Li
Advancing neuroimaging modalities for motor cortex analysis is critical for understanding the neural mechanisms underlying fine motor tasks and for expanding clinical applications. Functional Near-Infrared Spectroscopy (fNIRS) is widely used for measuring cortical hemodynamic activity due to its portability and accessibility, but its inherent limitations in spatial resolution and noise sensitivity reduce its utility for precise neural mapping. Diffuse Optical Tomography (DOT) has emerged as a promising alternative with superior spatial resolution and sensitivity. In this study, we performed a systematic comparison of DOT and fNIRS in detecting task-evoked neural activation during a finger-tapping paradigm including four conditions varying by finger type (thumb vs. little finger) and frequency (high vs. low). Our results demonstrated that DOT consistently captured robust activation in motor-related brain regions, even during less demanding conditions, while fNIRS exhibited limited sensitivity. Temporal trace analyses revealed that DOT achieved higher contrast-to-noise ratio (CNR) and contrast-to-background ratio (CBR), validating its enhanced signal quality and ability to distinguish subtle hemodynamic responses. Furthermore, statistical comparisons highlighted significant differences in task-related activations detected by the two modalities, particularly in low-effort conditions. These findings underscore the advantages of DOT over fNIRS, particularly in applications requiring high spatial resolution and sensitivity to subtle neural processes. The results contribute to ongoing efforts to refine optical imaging techniques for motor neuroscience and reinforce DOT’s potential for clinical translation in motor deficit diagnosis, rehabilitation monitoring, and brain-computer interface development.
{"title":"Enhanced Mapping of Finger Movement Representations Using Diffuse Optical Tomography: A Systematic Comparison With fNIRS","authors":"Shuo Guan;Yuhang Li;Yuanyuan Gao;Ran Yin;Yuxi Luo;Jiuxing Liang;Juan Zhang;Yingchun Zhang;Rihui Li","doi":"10.1109/TNSRE.2026.3652812","DOIUrl":"10.1109/TNSRE.2026.3652812","url":null,"abstract":"Advancing neuroimaging modalities for motor cortex analysis is critical for understanding the neural mechanisms underlying fine motor tasks and for expanding clinical applications. Functional Near-Infrared Spectroscopy (fNIRS) is widely used for measuring cortical hemodynamic activity due to its portability and accessibility, but its inherent limitations in spatial resolution and noise sensitivity reduce its utility for precise neural mapping. Diffuse Optical Tomography (DOT) has emerged as a promising alternative with superior spatial resolution and sensitivity. In this study, we performed a systematic comparison of DOT and fNIRS in detecting task-evoked neural activation during a finger-tapping paradigm including four conditions varying by finger type (thumb vs. little finger) and frequency (high vs. low). Our results demonstrated that DOT consistently captured robust activation in motor-related brain regions, even during less demanding conditions, while fNIRS exhibited limited sensitivity. Temporal trace analyses revealed that DOT achieved higher contrast-to-noise ratio (CNR) and contrast-to-background ratio (CBR), validating its enhanced signal quality and ability to distinguish subtle hemodynamic responses. Furthermore, statistical comparisons highlighted significant differences in task-related activations detected by the two modalities, particularly in low-effort conditions. These findings underscore the advantages of DOT over fNIRS, particularly in applications requiring high spatial resolution and sensitivity to subtle neural processes. The results contribute to ongoing efforts to refine optical imaging techniques for motor neuroscience and reinforce DOT’s potential for clinical translation in motor deficit diagnosis, rehabilitation monitoring, and brain-computer interface development.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"617-625"},"PeriodicalIF":5.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11345245","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145959425","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}
Pub Date : 2026-01-12DOI: 10.1109/TNSRE.2026.3653138
Junbiao Zhu;Kendi Li;Sicong Chen;Haiyun Huang;Yupeng Zhang;Li Hu;Yuanqing Li
For patients with severe extremity motor function impairment, traditional smart ward control methods, such as those using joysticks and touchscreens, are frequently unsuitable due to their limited physical abilities. Consequently, developing an effective brain–computer interface (BCI) suitable for their operation has become an immediate concern. This paper presents a wearable multimodal BCI system for smart ward control, which employs a self-designed wearable headband to capture head rotation and blinking movement. By wearing the headband, users can control a computer cursor on the screen only with head rotation and blinking, and further control devices in a smart ward with self-designed graphical user interfaces (GUIs). The system decodes signals from an inertial measurement unit (IMU) to map the head posture to the position of the cursor on the screen and decodes electrooculography (EOG) and electroencephalography (EEG) signals to detect valid blinks for selecting and activating function buttons. Ten participants were recruited to perform two experimental tasks that simulate the daily needs of patients with extremity motor function issues. To our satisfaction, all the participants fully accomplished the simulated tasks, and an average accuracy of $97.0pm 3.9$ % and an average response time of $2.39pm 0.53$ s were achieved. Different from traditional step-controlled BCI nursing beds, we designed a continuous-controlled nursing bed and achieved satisfactory results. Furthermore, workload evaluation using NASA Task Load Index (NASA-TLX) revealed that the participants experienced a low workload when using the system. The experimental results demonstrate the effectiveness of our proposed system, indicating significant potential for practical applications.
{"title":"Smart Ward Control Based on a Wearable Multimodal Brain–Computer Interface Mouse","authors":"Junbiao Zhu;Kendi Li;Sicong Chen;Haiyun Huang;Yupeng Zhang;Li Hu;Yuanqing Li","doi":"10.1109/TNSRE.2026.3653138","DOIUrl":"10.1109/TNSRE.2026.3653138","url":null,"abstract":"For patients with severe extremity motor function impairment, traditional smart ward control methods, such as those using joysticks and touchscreens, are frequently unsuitable due to their limited physical abilities. Consequently, developing an effective brain–computer interface (BCI) suitable for their operation has become an immediate concern. This paper presents a wearable multimodal BCI system for smart ward control, which employs a self-designed wearable headband to capture head rotation and blinking movement. By wearing the headband, users can control a computer cursor on the screen only with head rotation and blinking, and further control devices in a smart ward with self-designed graphical user interfaces (GUIs). The system decodes signals from an inertial measurement unit (IMU) to map the head posture to the position of the cursor on the screen and decodes electrooculography (EOG) and electroencephalography (EEG) signals to detect valid blinks for selecting and activating function buttons. Ten participants were recruited to perform two experimental tasks that simulate the daily needs of patients with extremity motor function issues. To our satisfaction, all the participants fully accomplished the simulated tasks, and an average accuracy of <inline-formula> <tex-math>$97.0pm 3.9$ </tex-math></inline-formula> % and an average response time of <inline-formula> <tex-math>$2.39pm 0.53$ </tex-math></inline-formula> s were achieved. Different from traditional step-controlled BCI nursing beds, we designed a continuous-controlled nursing bed and achieved satisfactory results. Furthermore, workload evaluation using NASA Task Load Index (NASA-TLX) revealed that the participants experienced a low workload when using the system. The experimental results demonstrate the effectiveness of our proposed system, indicating significant potential for practical applications.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"638-649"},"PeriodicalIF":5.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11346927","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145959445","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}
Pub Date : 2026-01-12DOI: 10.1109/TNSRE.2026.3651786
Mobeena Jamshed;Ahsan Shahzad;Kiseon Kim
Early detection of Mild Cognitive Impairment (MCI), a prodromal stage of dementia, plays a pivotal role in enabling timely clinical intervention and slowing cognitive decline. This paper presents a multi-sensor balance assessment framework designed to identify MCI-related postural instabilities using a wearable inertial measurement unit (IMU) network. The proposed system employs five synchronized IMUs placed at the waist, thighs, and shanks to capture balance dynamics across four static balance tasks: Eyes-Open, Eyes-Closed, Right-Leg Lift, and Left-Leg Lift. A three-stage feature selection strategy, comprising variance and correlation pruning, univariate filtering, and embedded model selection, is implemented within a Leave-One-Subject-Out (LOSO) cross-validation scheme to extract discriminative sway features. Classification using Support Vector Machines and tree-based ensemble models consistently yields superior results, achieving accuracies between 71.7% and 79.2%, with the highest performance observed in the Eyes-Open condition. A compact 10-feature subset demonstrates stable and robust discriminative power across all tasks. Compared to a single-sensor baseline, the multi-sensor configuration provides improved classification performance, underscoring the feasibility of compact, balance-driven, non-invasive MCI screening through wearable sensor systems.
{"title":"Early Detection of Mild Cognitive Impairment Through Balance Assessment Using Multi-Location Wearable Inertial Sensors","authors":"Mobeena Jamshed;Ahsan Shahzad;Kiseon Kim","doi":"10.1109/TNSRE.2026.3651786","DOIUrl":"10.1109/TNSRE.2026.3651786","url":null,"abstract":"Early detection of Mild Cognitive Impairment (MCI), a prodromal stage of dementia, plays a pivotal role in enabling timely clinical intervention and slowing cognitive decline. This paper presents a multi-sensor balance assessment framework designed to identify MCI-related postural instabilities using a wearable inertial measurement unit (IMU) network. The proposed system employs five synchronized IMUs placed at the waist, thighs, and shanks to capture balance dynamics across four static balance tasks: Eyes-Open, Eyes-Closed, Right-Leg Lift, and Left-Leg Lift. A three-stage feature selection strategy, comprising variance and correlation pruning, univariate filtering, and embedded model selection, is implemented within a Leave-One-Subject-Out (LOSO) cross-validation scheme to extract discriminative sway features. Classification using Support Vector Machines and tree-based ensemble models consistently yields superior results, achieving accuracies between 71.7% and 79.2%, with the highest performance observed in the Eyes-Open condition. A compact 10-feature subset demonstrates stable and robust discriminative power across all tasks. Compared to a single-sensor baseline, the multi-sensor configuration provides improved classification performance, underscoring the feasibility of compact, balance-driven, non-invasive MCI screening through wearable sensor systems.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"552-562"},"PeriodicalIF":5.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11342299","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145959431","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}
Pub Date : 2026-01-12DOI: 10.1109/TNSRE.2026.3651761
Bence Mark Halpern;Wen-Chin Huang;Lester Phillip Violeta;Tomoki Toda
The article presents a new pathological text-to-speech (TTS) synthesis system that has the ability to control speech severity using latent interpolations. Recognizing the difficulty of this task, our work uses a data augmentation technique to generate a single-speaker multi-severity training dataset required for training such a model. Furthermore, we show how x-vectors already contain information about the severity and leverage it as a conditioning variable for the synthesis. Finally, we propose modifications to the GradTTS architecture to enhance the duration modeling of pathological speech. We carry out objective and subjective evaluations to demonstrate that the proposed GradTTS system works well, and produces more natural, controllable, and stable pathological speech samples than the baseline TransformerTTS system.
{"title":"Severity-Controllable Pathological Text-to-Speech Synthesis for Clinical Applications","authors":"Bence Mark Halpern;Wen-Chin Huang;Lester Phillip Violeta;Tomoki Toda","doi":"10.1109/TNSRE.2026.3651761","DOIUrl":"10.1109/TNSRE.2026.3651761","url":null,"abstract":"The article presents a new pathological text-to-speech (TTS) synthesis system that has the ability to control speech severity using latent interpolations. Recognizing the difficulty of this task, our work uses a data augmentation technique to generate a single-speaker multi-severity training dataset required for training such a model. Furthermore, we show how x-vectors already contain information about the severity and leverage it as a conditioning variable for the synthesis. Finally, we propose modifications to the GradTTS architecture to enhance the duration modeling of pathological speech. We carry out objective and subjective evaluations to demonstrate that the proposed GradTTS system works well, and produces more natural, controllable, and stable pathological speech samples than the baseline TransformerTTS system.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"573-582"},"PeriodicalIF":5.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11342311","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145959443","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}
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