Accurate motor trajectory estimation from physiological signals is essential for developing advanced motor rehabilitation and bionic devices. Fusion of electroencephalography (EEG) and surface electromyography (sEMG) leverages complementary information, yet existing methods primarily target discrete intent classification. Current studies often utilize simultaneously collected EEG and sEMG, assuming temporal alignment between these signals and thereby overlooking the inherent latency between the two modalities. This oversight induces semantic misalignment and insufficient consistency representation, ultimately degrading performance in continuous motion trajectory decoding. To overcome these limitations, this paper proposes AtpFusion, an EEG-sEMG asynchronous time-frequency progressive fusion model for enhanced 3-dimensional (3D) hand trajectory decoding. Key contributions: 1) asynchronous time-frequency inputs, constructed using a physiologically-inspired long-short time window segmentation strategy for semantic alignment, comprising long-window frequency-domain EEG (amplitude/phase) and short-window time-domain sEMG signals; and 2) a progressive hierarchical fusion architecture with intra-modal and inter-modal branches, designed for effective hierarchical feature refinement and integration for regression. AtpFusion is evaluated on the public WAY-EEG-GAL dataset, performing, to our knowledge, the first EEG-sEMG-based continuous hand trajectory estimation on this benchmark. The proposed model yields state-of-the-art accuracy with a Pearson Correlation Coefficient (PCC) of 0.9278 and a Root Mean Square Error (RMSE) of 0.0916, significantly outperforming existing approaches. This work presents a novel asynchronous EEG-sEMG fusion framework, offering a high-performance solution for practical multimodal bionic interfaces.
{"title":"An EEG-sEMG Asynchronous Time–Frequency Progressive Fusion Model for Hand Trajectory Estimation","authors":"Shengcai Duan;Le Wu;Aiping Liu;Ruobing Qian;Xun Chen","doi":"10.1109/TNSRE.2025.3636906","DOIUrl":"10.1109/TNSRE.2025.3636906","url":null,"abstract":"Accurate motor trajectory estimation from physiological signals is essential for developing advanced motor rehabilitation and bionic devices. Fusion of electroencephalography (EEG) and surface electromyography (sEMG) leverages complementary information, yet existing methods primarily target discrete intent classification. Current studies often utilize simultaneously collected EEG and sEMG, assuming temporal alignment between these signals and thereby overlooking the inherent latency between the two modalities. This oversight induces semantic misalignment and insufficient consistency representation, ultimately degrading performance in continuous motion trajectory decoding. To overcome these limitations, this paper proposes AtpFusion, an EEG-sEMG asynchronous time-frequency progressive fusion model for enhanced 3-dimensional (3D) hand trajectory decoding. Key contributions: 1) asynchronous time-frequency inputs, constructed using a physiologically-inspired long-short time window segmentation strategy for semantic alignment, comprising long-window frequency-domain EEG (amplitude/phase) and short-window time-domain sEMG signals; and 2) a progressive hierarchical fusion architecture with intra-modal and inter-modal branches, designed for effective hierarchical feature refinement and integration for regression. AtpFusion is evaluated on the public WAY-EEG-GAL dataset, performing, to our knowledge, the first EEG-sEMG-based continuous hand trajectory estimation on this benchmark. The proposed model yields state-of-the-art accuracy with a Pearson Correlation Coefficient (PCC) of 0.9278 and a Root Mean Square Error (RMSE) of 0.0916, significantly outperforming existing approaches. This work presents a novel asynchronous EEG-sEMG fusion framework, offering a high-performance solution for practical multimodal bionic interfaces.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"69-80"},"PeriodicalIF":5.2,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11268383","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145603901","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}
Artificial somatosensory feedback plays a crucial role in compensating for tactile and proprioceptive loss in prosthesis users. Although modern prosthetic systems can acquire rich sensory data, effectively conveying this multimodal information to the user remains a significant challenge. This study presents a wearable somatosensory feedback armband with two configurations: a multimodal version using combined vibrotactile–electrotactile (VEC) stimulation, and a unimodal version based on vibrotactile-only (VO) stimulation. In both configurations, proprioceptive feedback is conveyed via spatiotemporal vibrotactile patterns, while tactile and proximity feedback are transmitted using electrotactile stimulation in VEC and vibrotactile cues in VO. The novel system was evaluated in ten transradial amputees in psychophysical experiments, and in seven additional participants (two amputees and five non-disabled) who performed object grasping and manipulation tasks (OGMT) under four conditions. Results showed that both configurations enabled accurate recognition of multiple sensory variables, with average accuracies exceeding 90% across all conditions, and success rates above 80% in OGMT. The success rate of the proposed system was not significantly different compared to that achieved with natural visual-auditory feedback (VA). However, VA resulted in significantly lower time to perform the task. The participants reported that VEC reduced cognitive fatigue under multi-modal feedback, and VO was linked to greater willingness for long-term use. These findings demonstrate that the proposed system offers a novel, flexible, and precise platform for prosthetic sensory feedback. By leveraging multiple stimulation modalities and spatio-temporal encoding, the VEC configuration expands the range of sensory inputs, enabling more diverse, and accurate stimulation for users requiring enhanced feedback. Meanwhile, the VO configuration effectively meets most sensory feedback needs with simpler integration, making it well-suited for broader applications.
{"title":"A Multimodal Stimulation System for Conveying Diverse Feedback in Hand Prosthetics: Preliminary Assessment","authors":"Zhikai Wei;Aiguo Song;Fengkai Guo;Strahinja Dosen;Xuhui Hu;Ziyi Zhao;Xiyuan Zhao","doi":"10.1109/TNSRE.2025.3636435","DOIUrl":"10.1109/TNSRE.2025.3636435","url":null,"abstract":"Artificial somatosensory feedback plays a crucial role in compensating for tactile and proprioceptive loss in prosthesis users. Although modern prosthetic systems can acquire rich sensory data, effectively conveying this multimodal information to the user remains a significant challenge. This study presents a wearable somatosensory feedback armband with two configurations: a multimodal version using combined vibrotactile–electrotactile (VEC) stimulation, and a unimodal version based on vibrotactile-only (VO) stimulation. In both configurations, proprioceptive feedback is conveyed via spatiotemporal vibrotactile patterns, while tactile and proximity feedback are transmitted using electrotactile stimulation in VEC and vibrotactile cues in VO. The novel system was evaluated in ten transradial amputees in psychophysical experiments, and in seven additional participants (two amputees and five non-disabled) who performed object grasping and manipulation tasks (OGMT) under four conditions. Results showed that both configurations enabled accurate recognition of multiple sensory variables, with average accuracies exceeding 90% across all conditions, and success rates above 80% in OGMT. The success rate of the proposed system was not significantly different compared to that achieved with natural visual-auditory feedback (VA). However, VA resulted in significantly lower time to perform the task. The participants reported that VEC reduced cognitive fatigue under multi-modal feedback, and VO was linked to greater willingness for long-term use. These findings demonstrate that the proposed system offers a novel, flexible, and precise platform for prosthetic sensory feedback. By leveraging multiple stimulation modalities and spatio-temporal encoding, the VEC configuration expands the range of sensory inputs, enabling more diverse, and accurate stimulation for users requiring enhanced feedback. Meanwhile, the VO configuration effectively meets most sensory feedback needs with simpler integration, making it well-suited for broader applications.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"57-68"},"PeriodicalIF":5.2,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11265762","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145596459","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 : 2025-11-21DOI: 10.1109/TNSRE.2025.3635677
Yunda Liu;Benito Lorenzo Pugliese;Gloria Vergara-Diaz;Anne O'Brien;Randie Black-Schaffer;Paolo Bonato;Sunghoon Ivan Lee
Continuous, objective, and precise upper-limb motor assessments are essential for realizing the vision of precision rehabilitation for stroke survivors. Wearable inertial sensors have emerged as a promising solution, enabling the analysis of motor performance in real-world settings. Recent studies have introduced two movement segmentation methods—anatomical segmentation and linear segmentation—for processing wearable inertial data to monitor post-stroke upper-limb motor recovery, each grounded in distinct theories of motor control and behavior. These methods differ in their practical implications for clinical use: linear segmentation requires only a single wearable device on the stroke-affected wrist, while anatomical segmentation necessitates an additional sensor on the sternum. This study seeks to systematically compare the clinimetric performance of these two approaches, taking into account their differences in practicality, to provide insights into their effective integration into clinical practice. 17 stroke survivors were equipped with inertial sensors on the trunk and the stroke-affected wrist while performing activities of daily living in a simulated apartment setting. Acceleration time-series from wrist movements were decomposed into movement segments using each movement segmentation approach. Reliable features were extracted from the movement segments, and supervised regression models were trained to establish concurrent validity against existing clinical measures. Anatomical segmentation demonstrated strong concurrent validity against existing clinical measures but may face challenges for continuous use due to the need for multiple sensors. Linear segmentation, on the other hand, provided slightly reduced but acceptable performance in motor deficit assessment while offering the advantage of requiring only a single wrist-worn sensor.
{"title":"Advancing Wearable-Based Upper-Limb Stroke Recovery Assessment to the Clinic: A Comparison of Movement Segmentation Strategies","authors":"Yunda Liu;Benito Lorenzo Pugliese;Gloria Vergara-Diaz;Anne O'Brien;Randie Black-Schaffer;Paolo Bonato;Sunghoon Ivan Lee","doi":"10.1109/TNSRE.2025.3635677","DOIUrl":"10.1109/TNSRE.2025.3635677","url":null,"abstract":"Continuous, objective, and precise upper-limb motor assessments are essential for realizing the vision of precision rehabilitation for stroke survivors. Wearable inertial sensors have emerged as a promising solution, enabling the analysis of motor performance in real-world settings. Recent studies have introduced two movement segmentation methods—anatomical segmentation and linear segmentation—for processing wearable inertial data to monitor post-stroke upper-limb motor recovery, each grounded in distinct theories of motor control and behavior. These methods differ in their practical implications for clinical use: linear segmentation requires only a single wearable device on the stroke-affected wrist, while anatomical segmentation necessitates an additional sensor on the sternum. This study seeks to systematically compare the clinimetric performance of these two approaches, taking into account their differences in practicality, to provide insights into their effective integration into clinical practice. 17 stroke survivors were equipped with inertial sensors on the trunk and the stroke-affected wrist while performing activities of daily living in a simulated apartment setting. Acceleration time-series from wrist movements were decomposed into movement segments using each movement segmentation approach. Reliable features were extracted from the movement segments, and supervised regression models were trained to establish concurrent validity against existing clinical measures. Anatomical segmentation demonstrated strong concurrent validity against existing clinical measures but may face challenges for continuous use due to the need for multiple sensors. Linear segmentation, on the other hand, provided slightly reduced but acceptable performance in motor deficit assessment while offering the advantage of requiring only a single wrist-worn sensor.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"36-45"},"PeriodicalIF":5.2,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11264038","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145573475","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}
Deep learning applied to electromyography (EMG) signals enables accurate hand gesture recognition, revolutionizing diverse applications such as human-machine interaction, neural interfaces, and rehabilitative robotics. A well-designed deep learning architecture is crucial for accurately and robustly modeling and decoding the multidimensional information embedded in the EMG data. This survey presents a comprehensive review of state-of-the-art deep learning models and, for the first time, offers a categorization of advanced architectures from the perspective of data representations. EMG, as a distinctive biosignal modality, can be characterized through multiple representational forms, including temporal waveforms, spatial images, spectral domains, and graph-based structures comprising interconnected nodes. Consequently, the optimal model architecture is closely tied to the specific data representation employed. In addition, the limited availability of EMG datasets, particularly those with high-quality labels, remains a critical bottleneck and continues to impede the translation of research advances into widespread real-world applications. We therefore examine emerging semi-supervised and self-supervised learning frameworks, which serve as complementary approaches to fully supervised paradigms. Finally, we outline promising future directions for the development of generalizable and robust deep learning for practical EMG decoding.
{"title":"Deep Feature Learning From Electromyographic Signals for Gesture Recognition Systems","authors":"Wenjuan Zhong;Xinyu Jiang;Katarzyna Szymaniak;Milad Jabbari;Chenfei Ma;Kianoush Nazarpour","doi":"10.1109/TNSRE.2025.3635419","DOIUrl":"10.1109/TNSRE.2025.3635419","url":null,"abstract":"Deep learning applied to electromyography (EMG) signals enables accurate hand gesture recognition, revolutionizing diverse applications such as human-machine interaction, neural interfaces, and rehabilitative robotics. A well-designed deep learning architecture is crucial for accurately and robustly modeling and decoding the multidimensional information embedded in the EMG data. This survey presents a comprehensive review of state-of-the-art deep learning models and, for the first time, offers a categorization of advanced architectures from the perspective of data representations. EMG, as a distinctive biosignal modality, can be characterized through multiple representational forms, including temporal waveforms, spatial images, spectral domains, and graph-based structures comprising interconnected nodes. Consequently, the optimal model architecture is closely tied to the specific data representation employed. In addition, the limited availability of EMG datasets, particularly those with high-quality labels, remains a critical bottleneck and continues to impede the translation of research advances into widespread real-world applications. We therefore examine emerging semi-supervised and self-supervised learning frameworks, which serve as complementary approaches to fully supervised paradigms. Finally, we outline promising future directions for the development of generalizable and robust deep learning for practical EMG decoding.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"34 ","pages":"20-35"},"PeriodicalIF":5.2,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11262198","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145563548","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 : 2025-11-19DOI: 10.1109/TNSRE.2025.3635012
Ibrahim Mohammed I. Hasan;Italo Belli;Ajay Seth;Elena M. Gutierrez-Farewik
Common optimization approaches for solving the muscle redundancy problem in musculoskeletal simulations can predict shoulder contact forces that either violate or barely satisfy joint stability requirements, with force directions falling outside or near the perimeter of the glenoid cavity. In this study, several glenohumeral stability formulations were tested against in vivo measurements of glenohumeral contact forces from the Orthoload dataset on one participant data in lateral, posterior, and anterior dumbbell raises. The investigated formulations either constrained the contact force direction to remain within different shapes of a stability perimeter, or added a penalty term that discouraged contact force directions from deviating from the glenoid cavity center. All stability formulations predicted contact force magnitudes that agreed relatively well to the in vivo measured forces except for the strictest formulation that constrained the joint contact force directly to the glenoid cavity center. Constraint and conditional penalty models estimated force vectors that largely lay along the perimeters. Continuous penalty models estimated relatively more accurate contact force directions within the glenoid cavity than constraint models. Our findings support the proposed penalty formulations as more reasonable and accurate than other investigated existing glenohumeral stability formulations.
{"title":"Modeling Glenohumeral Stability in Musculoskeletal Simulations: A Validation Study With In Vivo Contact Forces","authors":"Ibrahim Mohammed I. Hasan;Italo Belli;Ajay Seth;Elena M. Gutierrez-Farewik","doi":"10.1109/TNSRE.2025.3635012","DOIUrl":"10.1109/TNSRE.2025.3635012","url":null,"abstract":"Common optimization approaches for solving the muscle redundancy problem in musculoskeletal simulations can predict shoulder contact forces that either violate or barely satisfy joint stability requirements, with force directions falling outside or near the perimeter of the glenoid cavity. In this study, several glenohumeral stability formulations were tested against in vivo measurements of glenohumeral contact forces from the Orthoload dataset on one participant data in lateral, posterior, and anterior dumbbell raises. The investigated formulations either constrained the contact force direction to remain within different shapes of a stability perimeter, or added a penalty term that discouraged contact force directions from deviating from the glenoid cavity center. All stability formulations predicted contact force magnitudes that agreed relatively well to the in vivo measured forces except for the strictest formulation that constrained the joint contact force directly to the glenoid cavity center. Constraint and conditional penalty models estimated force vectors that largely lay along the perimeters. Continuous penalty models estimated relatively more accurate contact force directions within the glenoid cavity than constraint models. Our findings support the proposed penalty formulations as more reasonable and accurate than other investigated existing glenohumeral stability formulations.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"4657-4668"},"PeriodicalIF":5.2,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11260516","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145556883","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}
Lower-limb amputees worldwide have been increasing continuously in recent years. Hydraulic knees are suitable for active transfemoral amputees in developing countries due to their adaptability to various walking speeds and greater accessibility compared to high-end prosthetics. However, most hydraulic prosthetic knees operate via ground reaction force control, which exhibits a double-peak characteristic, causing slight flexion during the early stance phase, leading to unnatural and asymmetrical gait patterns for amputees. This study proposes a novel technique that expands the concept of the two-axis for application in a hydraulic prosthetic knee, utilizing the control moment to achieve stance-phase control (CMSPC knee). The control moment exhibits only one positive peak during the stance phase, allowing for adjustment of suitable stance-phase knee flexion by varying the spring stiffness. The single-subject walking experiment was conducted in the gait laboratory with one transfemoral amputee to evaluate the conceptual design. The subject walked on a treadmill at a constant velocity of 0.9 m/s, a self-selected walking speed, for 30 seconds, repeated four times for each spring stiffness. The results showed that the CMSPC knee can adjust the maximum stance-phase knee flexion from approximately 4.15° to 13.89°, which is roughly the same range observed in non-disabled individuals. Finally, most gait symmetry in temporal variables was significantly improved, with comparable results between the best condition, at a spring stiffness of 12.2 N/mm, and the condition without a spring (Mann-Whitney, p < 0.05). The condition without a spring is represented by hydraulic knees that offer slight stance-phase knee flexion.
近年来，世界范围内下肢截肢者人数持续增加。与高端假肢相比，液压膝可以适应不同的行走速度和更大的可及性，因此适合发展中国家的主动经股截肢者。然而，大多数液压假膝通过地面反作用力控制来操作，这表现出双峰特性，在站立早期引起轻微的屈曲，导致截肢者不自然和不对称的步态模式。本研究提出了一种新的技术，扩展了两轴的概念，应用于液压假膝，利用控制力矩实现姿态-相位控制（CMSPC膝关节）。在姿态阶段，控制力矩仅显示一个正峰值，允许通过改变弹簧刚度来调整合适的姿态阶段膝关节屈曲。在步态实验室进行单受试者步行实验，其中一名经股截肢者对概念设计进行评估。受试者在跑步机上以0.9 m/s的恒定速度（自行选择的行走速度）行走30秒，每种弹簧刚度重复4次。结果表明，CMSPC膝关节可以将膝关节的最大立场-阶段屈曲从大约4.15°调节到13.89°，这与非残疾个体的观察范围大致相同。最后，大多数时间变量的步态对称性都得到了显著改善，弹簧刚度为12.2 N/mm时的最佳状态与没有弹簧时的结果相当（Mann-Whitney, p < 0.05）。没有弹簧的情况是液压膝盖，提供轻微的立场阶段膝关节屈曲。
{"title":"Design of a Hydraulic Prosthetic Knee With Control Moment for Adjustable Stance-Phase Knee Flexion","authors":"Jiranut Manui;Chanyaphan Virulsri;Pattarapol Yotnuengnit;Manunchaya Samala;Pairat Tangpornprasert","doi":"10.1109/TNSRE.2025.3634670","DOIUrl":"10.1109/TNSRE.2025.3634670","url":null,"abstract":"Lower-limb amputees worldwide have been increasing continuously in recent years. Hydraulic knees are suitable for active transfemoral amputees in developing countries due to their adaptability to various walking speeds and greater accessibility compared to high-end prosthetics. However, most hydraulic prosthetic knees operate via ground reaction force control, which exhibits a double-peak characteristic, causing slight flexion during the early stance phase, leading to unnatural and asymmetrical gait patterns for amputees. This study proposes a novel technique that expands the concept of the two-axis for application in a hydraulic prosthetic knee, utilizing the control moment to achieve stance-phase control (CMSPC knee). The control moment exhibits only one positive peak during the stance phase, allowing for adjustment of suitable stance-phase knee flexion by varying the spring stiffness. The single-subject walking experiment was conducted in the gait laboratory with one transfemoral amputee to evaluate the conceptual design. The subject walked on a treadmill at a constant velocity of 0.9 m/s, a self-selected walking speed, for 30 seconds, repeated four times for each spring stiffness. The results showed that the CMSPC knee can adjust the maximum stance-phase knee flexion from approximately 4.15° to 13.89°, which is roughly the same range observed in non-disabled individuals. Finally, most gait symmetry in temporal variables was significantly improved, with comparable results between the best condition, at a spring stiffness of 12.2 N/mm, and the condition without a spring (Mann-Whitney, p < 0.05). The condition without a spring is represented by hydraulic knees that offer slight stance-phase knee flexion.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"4687-4696"},"PeriodicalIF":5.2,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11260493","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145556793","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 : 2025-11-19DOI: 10.1109/TNSRE.2025.3635018
Chengxuan Qin;Rui Yang;Longsheng Zhu;Zhige Chen;Mengjie Huang;Fuad E. Alsaadi;Zidong Wang
The distribution of electroencephalogram (EEG) data generally varies across datasets due to the huge difference between the physical structure of brain-computer interface devices, known as cross-device variability. Such variability poses great challenges in EEG decoding and hinders the standardized utilization of EEG datasets. In this study, we explore a new issue concerning the cross-device variability problem, pointing to the gap in the existing studies facing cross-device variability. To tackle this challenge, our paper is the first to model the cross-device variability problem through a “sequentially comprehensive formula” and a “spatial comprehensive formula”. Inspired by this modeling, a novel deep domain adaptation network named EEG-Infinity is proposed, incorporating replaceable EEG feature extraction backbones with a novel structure named “alignment head”. To show the effectiveness of the proposed EEG-Infinity, systematic experiments are conducted across four different EEG-based motor imagery datasets under 48 cases. The experimental results highlight the superior performance of the proposed EEG-Infinity over commonly used approaches with an average classification accuracy improvement of 1.51% across 34 cases, laying a foundation for research in large-scale EEG models. The code can be assessed at https://github.com/Baizhige/cd-infinity
{"title":"EEG-Infinity: A Mathematical Modeling-Inspired Architecture for Addressing Cross-Device Challenges in Motor Imagery","authors":"Chengxuan Qin;Rui Yang;Longsheng Zhu;Zhige Chen;Mengjie Huang;Fuad E. Alsaadi;Zidong Wang","doi":"10.1109/TNSRE.2025.3635018","DOIUrl":"10.1109/TNSRE.2025.3635018","url":null,"abstract":"The distribution of electroencephalogram (EEG) data generally varies across datasets due to the huge difference between the physical structure of brain-computer interface devices, known as cross-device variability. Such variability poses great challenges in EEG decoding and hinders the standardized utilization of EEG datasets. In this study, we explore a new issue concerning the cross-device variability problem, pointing to the gap in the existing studies facing cross-device variability. To tackle this challenge, our paper is the first to model the cross-device variability problem through a “sequentially comprehensive formula” and a “spatial comprehensive formula”. Inspired by this modeling, a novel deep domain adaptation network named EEG-Infinity is proposed, incorporating replaceable EEG feature extraction backbones with a novel structure named “alignment head”. To show the effectiveness of the proposed EEG-Infinity, systematic experiments are conducted across four different EEG-based motor imagery datasets under 48 cases. The experimental results highlight the superior performance of the proposed EEG-Infinity over commonly used approaches with an average classification accuracy improvement of 1.51% across 34 cases, laying a foundation for research in large-scale EEG models. The code can be assessed at <uri>https://github.com/Baizhige/cd-infinity</uri>","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"4669-4686"},"PeriodicalIF":5.2,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11260507","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145556839","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 : 2025-11-18DOI: 10.1109/TNSRE.2025.3634138
Liqiang Xu;Hongmei Chen;Biao Xiang;Zhong Yuan;Chuan Luo;Shi-Jinn Horng;Tianrui Li
The early diagnosis of Alzheimer’s disease (AD) is crucial because individuals may first experience mild cognitive impairment (MCI), which can then develop into AD, enabling timely intervention, slowing disease progression, and advancing the understanding of AD pathology. However, existing methods face two major challenges: first, they lack effective mechanisms to handle abnormal samples in neuroimaging data, which can distort model learning; second, they do not fully exploit complementary structural information across modalities, leading to insufficient discriminative power. To tackle these problems, we propose a model for outlier detection and cross-modal representation learning. This model leverages graph fusion for effective cross-modal information utilization and introduces multiple latent space mappings. Additionally, an outlier detection vector assigns lower learning weights to more anomalous samples, mitigating their impact. An alternating optimization algorithm ensures convergence and optimizes the objective function. Experimental comparisons with related algorithms on AD datasets demonstrate our method’s superiority. These results confirm that explicitly addressing abnormal data and enhancing cross-modal fusion are essential for improving both the robustness and accuracy of AD early diagnosis.
{"title":"Outlier Detection and Cross-Modal Representation Learning for Multimodal Alzheimer’s Disease Diagnosis","authors":"Liqiang Xu;Hongmei Chen;Biao Xiang;Zhong Yuan;Chuan Luo;Shi-Jinn Horng;Tianrui Li","doi":"10.1109/TNSRE.2025.3634138","DOIUrl":"10.1109/TNSRE.2025.3634138","url":null,"abstract":"The early diagnosis of Alzheimer’s disease (AD) is crucial because individuals may first experience mild cognitive impairment (MCI), which can then develop into AD, enabling timely intervention, slowing disease progression, and advancing the understanding of AD pathology. However, existing methods face two major challenges: first, they lack effective mechanisms to handle abnormal samples in neuroimaging data, which can distort model learning; second, they do not fully exploit complementary structural information across modalities, leading to insufficient discriminative power. To tackle these problems, we propose a model for outlier detection and cross-modal representation learning. This model leverages graph fusion for effective cross-modal information utilization and introduces multiple latent space mappings. Additionally, an outlier detection vector assigns lower learning weights to more anomalous samples, mitigating their impact. An alternating optimization algorithm ensures convergence and optimizes the objective function. Experimental comparisons with related algorithms on AD datasets demonstrate our method’s superiority. These results confirm that explicitly addressing abnormal data and enhancing cross-modal fusion are essential for improving both the robustness and accuracy of AD early diagnosis.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"4646-4656"},"PeriodicalIF":5.2,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11251324","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145549329","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 : 2025-11-14DOI: 10.1109/TNSRE.2025.3632867
Motomichi Sonobe;Naoto Miura
One approach for developing simulation models of human standing or for evaluating sensory functions and the central nervous system is to identify mathematical models by applying external perturbations to standing subjects and measuring their responses. However, a standardized approach has not yet been established. This requires a simplified model that captures the dominant dynamics. This study aimed to identify individual balance systems by focusing on the control of the center of mass (COM) in the low-frequency range below 0.7 Hz, under horizontal perturbations applied to the support surface. We modeled the human body as a single inverted pendulum and proposed a delayed-state feedback control system that accounts for shifts of the COM equilibrium position depending on the support surface velocity. Furthermore, we introduced a practical COM estimation method using measurements of ground reaction forces and support surface movement without optical motion capture systems. Twenty healthy young adults participated in the experiment over three consecutive days, and stable models were successfully identified for all subjects. The intraclass correlation coefficient for the identified models exceeded 0.5 across two consecutive days, indicating moderate reproducibility. These findings suggest that the proposed method has the potential to be a practical tool for evaluating balance function.
{"title":"Identification of Standing Balance System Considering Center of Mass Control for Support Surface Sway","authors":"Motomichi Sonobe;Naoto Miura","doi":"10.1109/TNSRE.2025.3632867","DOIUrl":"10.1109/TNSRE.2025.3632867","url":null,"abstract":"One approach for developing simulation models of human standing or for evaluating sensory functions and the central nervous system is to identify mathematical models by applying external perturbations to standing subjects and measuring their responses. However, a standardized approach has not yet been established. This requires a simplified model that captures the dominant dynamics. This study aimed to identify individual balance systems by focusing on the control of the center of mass (COM) in the low-frequency range below 0.7 Hz, under horizontal perturbations applied to the support surface. We modeled the human body as a single inverted pendulum and proposed a delayed-state feedback control system that accounts for shifts of the COM equilibrium position depending on the support surface velocity. Furthermore, we introduced a practical COM estimation method using measurements of ground reaction forces and support surface movement without optical motion capture systems. Twenty healthy young adults participated in the experiment over three consecutive days, and stable models were successfully identified for all subjects. The intraclass correlation coefficient for the identified models exceeded 0.5 across two consecutive days, indicating moderate reproducibility. These findings suggest that the proposed method has the potential to be a practical tool for evaluating balance function.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"4580-4589"},"PeriodicalIF":5.2,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11249450","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145523338","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 : 2025-11-14DOI: 10.1109/TNSRE.2025.3633082
Eleonora Fontana;Manuel G. Catalano;Giorgio Grioli;Matteo Bianchi;Antonio Bicchi
Proprioceptive feedback is essential for motor control and prosthetic embodiment, yet myoelectric prostheses lack naturalistic sensory input. Artificial skin stretch stimulation has emerged as a preferred method to convey proprioceptive cues, but current friction-based devices face limitations preventing integration into practical prostheses. This work investigates magnetically induced skin stretch as a non-invasive, potentially implantable alternative. We present MISS (Magnetically Induced Skin Stretch), a novel system that uses external coils to control magnets adhered to the skin, producing skin deformations that mimic subdermal implantation and evoke proprioceptive sensations. We conducted physical and psychophysical experiments, including Just Noticeable Difference and Point of Subjective Equality measurements. Eighteen participants, including five with transradial amputation, used the MISS device with a myoelectric prosthesis, where skin stretch was modulated in sync with prosthetic hand flexion. Results showed high object discrimination accuracy, with amputees performing comparably to non-disabled users. These findings demonstrate MISS as a promising proprioceptive feedback method, supporting its future integration into implantable systems.
本体感觉反馈对于运动控制和假体的体现是必不可少的，然而肌电假体缺乏自然的感觉输入。人工皮肤拉伸刺激已成为传递本体感觉信号的首选方法，但目前基于摩擦的设备面临着限制，无法整合到实际假肢中。这项工作研究了磁诱导皮肤拉伸作为一种非侵入性的、潜在的植入式替代方法。我们提出了MISS（磁致皮肤拉伸），这是一种新颖的系统，它使用外部线圈来控制附着在皮肤上的磁铁，产生皮肤变形，模拟皮下植入并唤起本体感觉。我们进行了生理和心理物理实验，包括Just visible Difference和Point of Subjective Equality测量。18名参与者，包括5名经桡骨截肢者，使用MISS装置和肌电假体，其中皮肤拉伸与假手弯曲同步调节。结果显示，截肢者与健全者的物体识别准确率相当。这些发现表明，MISS是一种很有前途的本体感觉反馈方法，支持其未来整合到植入式系统中。
{"title":"Magnetically Induced Skin Stretch Enhances Proprioceptive Feedback in Prosthetics","authors":"Eleonora Fontana;Manuel G. Catalano;Giorgio Grioli;Matteo Bianchi;Antonio Bicchi","doi":"10.1109/TNSRE.2025.3633082","DOIUrl":"10.1109/TNSRE.2025.3633082","url":null,"abstract":"Proprioceptive feedback is essential for motor control and prosthetic embodiment, yet myoelectric prostheses lack naturalistic sensory input. Artificial skin stretch stimulation has emerged as a preferred method to convey proprioceptive cues, but current friction-based devices face limitations preventing integration into practical prostheses. This work investigates magnetically induced skin stretch as a non-invasive, potentially implantable alternative. We present MISS (Magnetically Induced Skin Stretch), a novel system that uses external coils to control magnets adhered to the skin, producing skin deformations that mimic subdermal implantation and evoke proprioceptive sensations. We conducted physical and psychophysical experiments, including Just Noticeable Difference and Point of Subjective Equality measurements. Eighteen participants, including five with transradial amputation, used the MISS device with a myoelectric prosthesis, where skin stretch was modulated in sync with prosthetic hand flexion. Results showed high object discrimination accuracy, with amputees performing comparably to non-disabled users. These findings demonstrate MISS as a promising proprioceptive feedback method, supporting its future integration into implantable systems.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"4599-4613"},"PeriodicalIF":5.2,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11247940","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145523334","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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