IEEE Transactions on Biomedical Engineering最新文献_第4页

Spatio-Temporal Progressive Attention Model for EEG Classification in Rapid Serial Visual Presentation Task. 快速序列视觉呈现任务脑电分类的时空递进注意模型。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-06-12 DOI: 10.1109/TBME.2025.3579491

Yang Li, Wei Liu, Tianzhi Feng, Fu Li, Chennan Wu, Boxun Fu, Zhifu Zhao, Xiaotian Wang, Guangming Shi

{"title":"Spatio-Temporal Progressive Attention Model for EEG Classification in Rapid Serial Visual Presentation Task.","authors":"Yang Li, Wei Liu, Tianzhi Feng, Fu Li, Chennan Wu, Boxun Fu, Zhifu Zhao, Xiaotian Wang, Guangming Shi","doi":"10.1109/TBME.2025.3579491","DOIUrl":"https://doi.org/10.1109/TBME.2025.3579491","url":null,"abstract":"As a type of multi-dimensional sequential data, the spatial and temporal dependencies of electroencephalogram (EEG) signals should be further investigated. Thus, in this paper, we propose a novel spatial-temporal progressive attention model (STPAM) to improve EEG classification in rapid serial visual presentation (RSVP) tasks. STPAM employs a progressive approach using three sequential spatial experts to learn brain region topology and mitigate interference from irrelevant areas. Each expert refines EEG electrode selection, guiding subsequent experts to focus on significant spatial information, thus enhancing signals from key regions. Subsequently, based on the above spatially-enhanced features, three temporal experts progressively capture temporal dependencies by focusing attention on crucial EEG time slices. Except for the above EEG classification method, in this paper, we build a novel Infrared RSVP Dataset (IRED) which is based on dim infrared images with small targets for the first time, and conduct extensive experiments on it. Experimental results demonstrate that STPAM outperforms all baselines, achieving 2.02% and 1.17% on the public dataset and IRED dataset, respectively.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144283749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Time-Reversal Enhanced Dynamic Causality Distribution Learning and Its Application in Identifying Dynamic ECNs in MCI Patients. 时间反转增强动态因果分布学习及其在MCI患者动态ecn识别中的应用。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-06-12 DOI: 10.1109/TBME.2025.3579378

Yiding Wang, Chao Jin, Jian Yang, Chen Qiao

{"title":"Time-Reversal Enhanced Dynamic Causality Distribution Learning and Its Application in Identifying Dynamic ECNs in MCI Patients.","authors":"Yiding Wang, Chao Jin, Jian Yang, Chen Qiao","doi":"10.1109/TBME.2025.3579378","DOIUrl":"https://doi.org/10.1109/TBME.2025.3579378","url":null,"abstract":"Objective: Dynamic causal influences between brain regions are crucial for understanding the temporal variation and fluctuation of the interaction in human brain. However, recent causal discovery approaches often focus on fixed causality under directed acyclic graph constraints, and do not infer the dynamic and fluctuating nature of causality, which commonly exists in the brain.Methods: We propose a causality learning framework with evolving distribution for non-stationary and non-linear systems. Based on this framework, a time-reversal enhanced dynamic causality distribution learning (TRDCDL) model is constructed, which integrates spatio-temporal information to identify evolving distributional sparse interactions in data.Results: TRDCDL is validated in two synthetic models, which show the accuracy in learning both linear and non-linear causality within synthetic data. We further apply TRDCDL to the Alzheimer's Disease Neuroimaging Initiative dataset and infer dynamic effective connectivity networks (dECNs) among two stages of mild cognitive impairment (MCI).Conclusion: The results reveal significant differences in dECNs between brain regions across the these stages, indicating that dECNs can serve as reliable neuromarkers for distinguishing different stages of MCI.Significance: Significant reductions in dynamic causal influences within the default mode network and bilateral limbic network, along with few increased connectivity, reflect neurodegeneration and changing patterns of dECNs as MCI progresses.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144283750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A State-Space Framework for Causal Detection of Hippocampal Ripple-Replay Events. 海马体波纹重放事件因果检测的状态空间框架。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-06-11 DOI: 10.1109/TBME.2025.3578583

Sirui Zeng, Uri T Eden

{"title":"A State-Space Framework for Causal Detection of Hippocampal Ripple-Replay Events.","authors":"Sirui Zeng, Uri T Eden","doi":"10.1109/TBME.2025.3578583","DOIUrl":"10.1109/TBME.2025.3578583","url":null,"abstract":"Hippocampal ripple-replay events are typically identified using a two-step process that at each time point uses past and future data to determine whether an event is occurring. This prevents researchers from identifying these events in real time for closed-loop experiments. It also prevents the identification of periods of non-local representation that are not accompanied by large changes in the spectral content of the local field potentials (LFPs). In this work, we present a new state-space model framework that is able to detect concurrent changes in the rhythmic structure of LFPs with non-local activity in place cells to identify ripple-replay events in a causal manner. The model combines latent factors related to neural oscillations, represented space, and switches between coding properties to simultaneously explain the spiking activity from multiple units and the rhythmic content of LFPs recorded from multiple sources. The model is temporally causal, meaning that estimates of the switching state can be made at each instant using only past information from the spikes and LFPs, or can be combined with future data to refine those estimates. We applied this model framework to simulated and real hippocampal data to demonstrate its performance in identifying ripple-replay events.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144274671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fine-tuning Myoelectric Control through Reinforcement Learning in a Game Environment. 在游戏环境中通过强化学习微调肌电控制。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-06-11 DOI: 10.1109/TBME.2025.3578855

Kilian Freitag, Yiannis Karayiannidis, Jan Zbinden, Rita Laezza

{"title":"Fine-tuning Myoelectric Control through Reinforcement Learning in a Game Environment.","authors":"Kilian Freitag, Yiannis Karayiannidis, Jan Zbinden, Rita Laezza","doi":"10.1109/TBME.2025.3578855","DOIUrl":"https://doi.org/10.1109/TBME.2025.3578855","url":null,"abstract":"Objective: Enhancing the reliability of myoelectric controllers that decode motor intent is a pressing challenge in the field of bionic prosthetics. State-of-the-art research has mostly focused on Supervised Learning (SL) techniques to tackle this problem. However, obtaining high-quality labeled data that accurately represents muscle activity during daily usage remains difficult. We investigate the potential of Reinforcement Learning (RL) to further improve the decoding of human motion intent by incorporating usage-based data.Methods: The starting point of our method is a SL control policy, pretrained on a static recording of electromyographic (EMG) ground truth data. We then apply RL to fine-tune the pretrained classifier with dynamic EMG data obtained during interaction with a game environment developed for this work. We conducted real-time experiments to evaluate our approach and achieved significant improvements in human-in-the-loop performance.Results: The method effectively predicts simultaneous finger movements, leading to a two-fold increase in decoding accuracy during gameplay and a 39% improvement in a separate motion test.Conclusion: By employing RL and incorporating usage-based EMG data during fine-tuning, our method achieves significant improvements in accuracy and robustness.Significance: These results showcase the potential of RL for enhancing the reliability of myoelectric controllers, which is of particular importance for advanced bionic limbs. See our project page for visual demonstrations: https://sites.google.com/view/bionic-limb-rl.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144274672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comparative Study of Conventional and Tripolar EEG for High-Performance Reach-to-Grasp BCI Systems. 传统脑电与三极脑电在高性能手握BCI系统中的比较研究。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-06-10 DOI: 10.1109/TBME.2025.3578235

Ali Rabiee, Sima Ghafoori, Anna Cetera, Maryam Norouzi, Walter Besio, Reza Abiri

{"title":"A Comparative Study of Conventional and Tripolar EEG for High-Performance Reach-to-Grasp BCI Systems.","authors":"Ali Rabiee, Sima Ghafoori, Anna Cetera, Maryam Norouzi, Walter Besio, Reza Abiri","doi":"10.1109/TBME.2025.3578235","DOIUrl":"https://doi.org/10.1109/TBME.2025.3578235","url":null,"abstract":"This study aims to enhance brain-computer interface (BCI) applications for individuals with motor impairments by comparing the effectiveness of noninvasive tripolar concentric ring electrode electroencephalography (tEEG) with conventional electroencephalography (EEG) technology. The goal is to determine which EEG technology is more effective in measuring and decoding different grasp-related neural signals. The approach involves experimenting on ten healthy participants who performed two distinct reach-and-grasp movements: power grasp and precision grasp, with a no-movement condition serving as the baseline. Our research compares EEG and tEEG in decoding grasping movements, focusing on signal-to-noise ratio (SNR), spatial resolution, and wavelet time-frequency analysis. Additionally, our study involved extracting and analyzing statistical features from the wavelet coefficients, and both binary and multiclass classification methods were employed. Four machine learning algorithms-Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Linear Discriminant Analysis (LDA)-were used to evaluate the decoding accuracies. Our results indicated that tEEG demonstrated higher quality performance compared to conventional EEG in various aspects. This included a higher signal-to-noise ratio and improved spatial resolution. Furthermore, wavelet timefrequency analyses validated these findings, with tEEG exhibiting increased power spectra, thus providing a more detailed and informative representation of neural dynamics. The use of tEEG led to significant improvements in decoding accuracy for differentiating grasp movement types. Specifically, tEEG achieved around 90.00% accuracy in binary and 75.97% for multiclass classification. These results exceed those from conventional EEG, which recorded a maximum of 77.85% and 61.27% in similar tasks, respectively.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144266072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GLAPAL-H: Global, Local, And Parts Aware Learner for Hydrocephalus Infection Diagnosis in Low-Field MRI. GLAPAL-H：低场MRI诊断脑积水感染的全局、局部和局部感知学习器。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-06-09 DOI: 10.1109/TBME.2025.3578541

Srijit Mukherjee, Kelsey Templeton, Starlin Tindimwebwa, Ivy Lin, Jason Sutin, Mingzhao Yu, Mallory Peterson, Chip Truwit, Steven J Schiff, Vishal Monga

{"title":"GLAPAL-H: Global, Local, And Parts Aware Learner for Hydrocephalus Infection Diagnosis in Low-Field MRI.","authors":"Srijit Mukherjee, Kelsey Templeton, Starlin Tindimwebwa, Ivy Lin, Jason Sutin, Mingzhao Yu, Mallory Peterson, Chip Truwit, Steven J Schiff, Vishal Monga","doi":"10.1109/TBME.2025.3578541","DOIUrl":"10.1109/TBME.2025.3578541","url":null,"abstract":"Objective: The study aims to develop a method for differentiating between healthy, post-infectious hydrocephalus (PIH), and non-post-infectious hydrocephalus (NPIH) in infants using low-field MRI, which is a safer, low-cost alternative to CT scans. The study develops a custom approach that captures hydrocephalic etiology while simultaneously addressing quality issues encountered in low-field MRI.Methods: Specifically, we propose GLAPAL-H, a Global, Local, And Parts Aware Learner, which develops a multi-task architecture with global, local, and parts segmentation branches. The architecture segments images into brain tissue and CSF while using a shallow CNN for local feature extraction and develops a parallel deep CNN branch for global feature extraction. Three regularized training loss functions are developed - one for each of global, local, and parts components. The global regularizer captures holistic features, the local focuses on fine details, and the parts regularizer learns soft segmentation masks that enable local features to capture hydrocephalic etiology.Results: The study's results show that GLAPAL-H outperforms state-of-the-art alternatives, including CT-based approaches, for both Two-Class (PIH vs. NPIH) and Three-Class (PIH vs. NPIH vs. Healthy) classification tasks in accuracy, interpretability, and generalizability.Conclusion/significance: GLAPAL-H highlights the potential of low-field MRI as a safer, low-cost alternative to CT imaging for pediatric hydrocephalus infection diagnosis and management. Practically, GLAPAL-H demonstrates robustness against quantity and quality of training imagery, enhancing its deployability.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144258002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Synergistic Patient-Specific Approach for Enhanced Spinal Fixation Using a Novel Flexible Pedicle Screw and a Complementary Steerable Drilling Robotic System. 采用新型柔性椎弓根螺钉和互补的可操纵钻孔机器人系统增强脊柱固定的协同患者特异性方法。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-06-09 DOI: 10.1109/TBME.2025.3578540

Yash Kulkarni, Susheela Sharma, Zeynep Yakay, Sarah Go, Jordan P Amadio, Maryam Tilton, Farshid Alambeigi

{"title":"A Synergistic Patient-Specific Approach for Enhanced Spinal Fixation Using a Novel Flexible Pedicle Screw and a Complementary Steerable Drilling Robotic System.","authors":"Yash Kulkarni, Susheela Sharma, Zeynep Yakay, Sarah Go, Jordan P Amadio, Maryam Tilton, Farshid Alambeigi","doi":"10.1109/TBME.2025.3578540","DOIUrl":"https://doi.org/10.1109/TBME.2025.3578540","url":null,"abstract":"Objective: Current spinal fixation (SF) techniques face screw loosening and pullout challenges in osteoporotic patients. This can be attributed to conventional rigid pedicle screws (RPS) being forced to fixate along a constrained linear trajectory into low bone mineral density (BMD) areas of the vertebral body. This study proposes a synergistic patient-specific approach that integrates a steerable drilling robotic system with a novel Flexible Pedicle Screw (FPS) to enhance SF procedures by enabling curved screw fixation.Methods: A patient-specific framework and synergistic design flowchart were developed to guide the synergistic design of the previously proposed Concentric Tube-Steerable Drilling Robot (CT-SDR) and the FPS. After, the novel FPS is designed based on critical design features and its design is validated using Finite Element Analysis (FEA). The FPS is then fabricated via Direct Metal Laser Sintering (DMLS). The FPS's morphability and self-tapping capability were experimentally assessed in Sawbones phantoms drilled by the CT-SDR system.Results: The FPS successfully morphed to fixate in curvilinear paths, demonstrating effective morphability and self-tapping in simulated bone.Conclusion: By enabling a flexible, patient-specific approach to pedicle screw fixation, the FPS and CT-SDR system address key limitations of current SF procedures. This method enhances screw anchorage and fixation strength in osteoporotic vertebrae.Significance: This work presents a transformative approach to SF, with potential clinical applications in improving surgical outcomes for osteoporotic patients. The integration of robotic-assisted drilling and flexible implants could significantly reduce fixation failure rates, advancing orthopedic and spinal surgical practices.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144258000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Feasibility of Camera-based Continuous Bilirubin Level Monitoring for Neonates. 基于摄像机的新生儿胆红素水平连续监测的可行性。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-06-09 DOI: 10.1109/TBME.2025.3577783

Huaijing Shu, Yonglong Ye, Xiaoyan Song, Wenjin Wang

{"title":"Feasibility of Camera-based Continuous Bilirubin Level Monitoring for Neonates.","authors":"Huaijing Shu, Yonglong Ye, Xiaoyan Song, Wenjin Wang","doi":"10.1109/TBME.2025.3577783","DOIUrl":"https://doi.org/10.1109/TBME.2025.3577783","url":null,"abstract":"This study explores the feasibility of using an RGB camera to estimate the bilirubin level of neonates with an emphasis on applications within the Neonatal Intensive Care Unit (NICU), aiming to provide a non-contact, real-time, and continuous monitoring solution for neonatal jaundice. We investigated two fundamental models for camera-based bilirubin level monitoring: blood perfusion (AC component) based and skin reflectance (DC component) based. The blood perfusion model used the ratio of AC components in the blue and green channels, while the skin reflectance model employed the ratio of DC components in these two channels. Videos of 68 neonates in the NICU were recorded using an RGB camera and custom-built dual-wavelength light sources (460 nm and 570 nm). Clinical results showed that the blood perfusion based method negatively correlated with bilirubin concentration, contrary to our modeling and expectation, likely due to the interference of concentration in arterial blood. In contrast, the skin reflectance model demonstrated an expected strong negative correlation between DC ratio and bilirubin (i.e., r=-0.652 and p $< $ 0.005) and better consistency with the reference of transcutaneous bilirubin meter (agreement limits range = -5.72 mg/dL to 4.06 mg/dL) in intermittent bilirubin level estimation experiments. Additionally, camera-based continuous bilirubin level monitoring of resting neonates shows high potential (MAE = 4.57 mg/dL) in the NICU.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144258001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Kurtosis Transformation for Multi-Source Domain Generalization Segmentation of OCT Images From Multi-Manufacturers' Devices. 多厂商设备OCT图像多源域泛化分割的峰度变换。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-06-06 DOI: 10.1109/TBME.2025.3577271

Feiyu Sun, Ling Gao, Haoyu Chen, Bei Tian, Tao Peng, Weifang Zhu, Fei Shi, Ronghan Wu, Xinjian Chen, Dehui Xiang

{"title":"Kurtosis Transformation for Multi-Source Domain Generalization Segmentation of OCT Images From Multi-Manufacturers' Devices.","authors":"Feiyu Sun, Ling Gao, Haoyu Chen, Bei Tian, Tao Peng, Weifang Zhu, Fei Shi, Ronghan Wu, Xinjian Chen, Dehui Xiang","doi":"10.1109/TBME.2025.3577271","DOIUrl":"https://doi.org/10.1109/TBME.2025.3577271","url":null,"abstract":"Objective: Optical coherence tomography (OCT) images can visualize retinal layers and fundus lesions. Retinal structure segmentation is of great significance in early lesion detection and treatment guidance. However, devices from different OCT manufacturers are largely different from each other, which often leads to degraded results in image segmentation.Methods: To enrich the diversity of multi-source domains in intensity distributions and image contrasts of multi-manufacturers' OCT images, a kurtosis transformation method is proposed to generate a kurtosis-transformed image. To make the generated potential T-styles as different as possible from the source domain styles, a mean style contrastive learning method is proposed to maximize the distance between style features of a kurtosis-transformed image and multi-source domain images. To improve the diversity and independence of potential T-styles in a high-level orthogonal space, variance style orthogonalization is proposed to impose an orthogonal constraint on the reparametrized variance styles. Mean style features and variance style features are combined to modulate an input image for the training of the segmentation network.Results: Comprehensive experiments have been performed on two OCT image datasets. Compared to state-of-the-art methods, the proposed method can achieve better segmentation.Conclusion: The proposed segmentation method can be trained on labeled OCT images from multi-manufacturers' devices and can be tested on unseen manufacturer's device, and has good domain generalization performance in both retinal layer and lesions segmentation tasks.Significance: The proposed method can be used in routine clinical settings, when OCT images from multi-manufacturers' devices are available.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144247676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Continuous Wrist Angle Estimation Under Different Resistance Based on Dynamic EMG Decomposition. 基于动态肌电分解的不同阻力下腕关节角度连续估计。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-06-05 DOI: 10.1109/TBME.2025.3577002

Xinhao Yang, Baoguo Xu, Zelin Gao, Shipeng Ren, Huijun Li, Aiguo Song

{"title":"Continuous Wrist Angle Estimation Under Different Resistance Based on Dynamic EMG Decomposition.","authors":"Xinhao Yang, Baoguo Xu, Zelin Gao, Shipeng Ren, Huijun Li, Aiguo Song","doi":"10.1109/TBME.2025.3577002","DOIUrl":"https://doi.org/10.1109/TBME.2025.3577002","url":null,"abstract":"Estimating wrist movements through neural drives is crucial in human-machine interface (HMI). However, studies on wrist movements mostly focused on isometric contractions, while research on dynamic EMG decomposition during non-stationary movements is notably scarce. Moreover, the impact of different resistance on the motor unit (MU) decomposition and wrist angle estimation remains unexplored. To address these gaps, this paper proposed a novel framework to decode neural drives from EMG signals during dynamic wrist movements. Specifically, the EMG signals were divided into short segments firstly. Next, progressive FastICA peel-off (PFP) algorithm was utilized to decompose each EMG segment into motor unit spike trains (MUST). Then, a linear window function was applied to track the motor units (MU) to obtain complete MUSTs. Three resistance levels were investigated during wrist flexion and extension: 20%, 40%, and 60% maximum voluntary contraction (MVC). Multiple linear regression (LR) and convolutional neural network (CNN) were used to estimate wrist angles within a range of ± 20° based on neural drives. Results showed the proposed framework could effectively identify MUs at these three resistance levels, with an average global pulse-to-noise ratio (PNR) above 20 dB. The determination coefficients of LR model were 0.92 ± 0.06, 0.91 ± 0.07, and 0.85 ± 0.13 at the three resistance levels, respectively, while those of CNN were 0.88 ± 0.10, 0.88 ± 0.11, and 0.81 ± 0.17. This study demonstrates it is feasible to estimate wrist angles based on decomposed neural drives at different resistance levels, and has significant implications for HMI development.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144233995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0