Query：
学者姓名：李玉榕
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-
Language
Clean All
Abstract ：
运动功能障碍是脑卒中后的主要症状,一般认为是由控制运动功能的神经网络的损伤引起的.为了探讨卒中患者的神经肌肉控制机制,本研究以皮层肌肉功能连接(FCMC)为工具,采集13例卒中患者和13例健康对照者前伸动作时的脑电信号(EEG)及肱三头肌、前三角肌前束、中束、后束、肱二头肌、胸大肌、斜方肌的肌电信号(EMG).通过脑源定位得到大脑皮层源信号,再聚类确定动作对应的活跃脑区,使用偏传递熵求取皮层肌肉功能连接.卒中患者ICsF与AD、ICsB与BIC、ICsC与PD、PM、UT在上行通道的功能连接与健康对照组相比显著增强(例如健康对照组为0.033±0.031,卒中患者为0.092±0.083,P＜0.05);卒中患者BIC与ICsA、ICsB、ICsC,TRI、UT与ICsC在下行通道的功能连接与健康对照组相比显著增强(例如健康对照组为0.113±0.092,卒中患者为0.198±0.105,P＜0.05);卒中患者存在同侧的皮层肌肉功能连接.实验结果表明,该研究从新的角度探索脑卒中后皮层肌肉功能连接效应,证明了卒中患者存在同侧的皮层肌肉功能连接,进一步有效促进对卒中后神经肌肉耦合机制的理解.
Keyword ：
偏传递熵 偏传递熵 卒中 卒中 源定位 源定位 脑肌电功能连接 脑肌电功能连接
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 施正义 , 谢秋蓉 , 王晓玲 et al. 基于偏传递熵的卒中患者皮层肌肉功能连接分析 [J]. | 中国生物医学工程学报 , 2024 , 43 (5) : 561-570 . |
| MLA | 施正义 et al. "基于偏传递熵的卒中患者皮层肌肉功能连接分析" . | 中国生物医学工程学报 43 . 5 (2024) : 561-570 . |
| APA | 施正义 , 谢秋蓉 , 王晓玲 , 李玉榕 . 基于偏传递熵的卒中患者皮层肌肉功能连接分析 . | 中国生物医学工程学报 , 2024 , 43 (5) , 561-570 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Human Kinesiology analysis is essential for understanding biomechanical loads in rehabilitation, injury prevention, and diagnosis. However, traditional marker-based motion capture systems are suboptimal due to high equipment and time costs, as well as the need for specialized expertise. Although OpenSim can perform detailed kinematic analyses using musculoskeletal models, integrating these analyses seamlessly with sparse human key points derived from computer vision remains challenging. Additionally, the current triangulation methods based on direct linear transformation (DLT) have limitations in accuracy and generalization ability. While learnable triangulation methods can extract complex features from images and significantly improve the accuracy of identifying and locating human nodes, they still lack limb length constraints and require calibration for each inference. In order to solve the above problems, in this work, we apply a triangulation method combining graph convolutional network with joint context constraints and camera pose distribution to human kinematics analysis, which fully considers the bone length constraints and joint connection relations, and improves the reasoning ability of the model under different camera parameter datasets. In addition, we fine-tuned the backbone network using relevant data with foot markers to suit the needs of kinesiology analysis. Finally, the output key points of the triangulation network is sent into the deep learning network to obtain the corresponding anatomical markers, and the results in the visual field are better combined with the musculoskeletal model provided by OpenSim, and more accurate kinematic parameters are obtained. © 2024 IEEE.
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhang, C. , Li, Y. , Ye, W. et al. Human Kinematics Analysis by Markerless Vision Based on OpenSim [未知]. |
| MLA | Zhang, C. et al. "Human Kinematics Analysis by Markerless Vision Based on OpenSim" [未知]. |
| APA | Zhang, C. , Li, Y. , Ye, W. , Huang, G. . Human Kinematics Analysis by Markerless Vision Based on OpenSim [未知]. |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
The primary aim of this study is to construct a state-space model that reduces the complexity of the human sensorimotor control system and explore motion control theory based on manifolds. Low-dimensional neural and muscle manifolds, representing the population activity of the brain and muscles, quantify the sensorimotor function of the human cortex, revealing hidden information about how the brain controls movement. This study used EEG and EMG signals from 25 participants during a card grabbing task. Preferential subspace recognition (PSID) algorithm was used to construct a state space model of subjects to study the neural computational strategies for sensorimotor control in the preparation and execution phases of motion, as well as the differences between brain and muscle population activity. The results show that from the preparation stage to the execution stage, the population activity of cortical muscles follows the orthogonal neural computation strategy in the subspace. The degree of trajectory entanglement between neural manifolds and muscle manifolds is greater than that during exercise preparation. Moreover, neural manifolds are less entangled than muscle manifolds and show a more stable state in structure. These results show that the adopted approach effectively reveals the orthogonal neural mechanisms of brain control during movement, highlighting the differences in control output and feedback input in sensorimotor control. This offers a new perspective on how the brain controls complex movements. © 2024 IEEE.
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Tan, J. , Li, Y. , Li, J. et al. Modeling and Analyzing the Manifolds of Cortical-Muscle During Card-Grabbing by Preferential Subspace Identification [未知]. |
| MLA | Tan, J. et al. "Modeling and Analyzing the Manifolds of Cortical-Muscle During Card-Grabbing by Preferential Subspace Identification" [未知]. |
| APA | Tan, J. , Li, Y. , Li, J. , Zheng, N. . Modeling and Analyzing the Manifolds of Cortical-Muscle During Card-Grabbing by Preferential Subspace Identification [未知]. |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
In this paper, a novel dynamic multiobjective optimization algorithm (DMOA) with a cascaded fuzzy system (CFS) is developed, which aims to promote objective knowledge transfer from an innovative perspective of comprehensive information characterization. This development seeks to overcome the bottleneck of negative transfer in evolutionary transfer optimization (ETO)-based algorithms. Specifically, previous Pareto solutions, center- and knee-points of multi-subpopulation are adaptively selected to establish the source domain, which are then assigned soft labels through the designed CFS, based on a thorough evaluation of both convergence and diversity. A target domain is constructed by centroid feed-forward of multi-subpopulation, enabling further estimations on learning samples with the assistance of the kernel mean matching (KMM) method. By doing so, the property of non-independently identically distributed data is considered to enhance efficient knowledge transfer. Extensive evaluation results demonstrate the reliability and superiority of the proposed CFS-DMOA in solving dynamic multiobjective optimization problems (DMOPs), showing significant competitiveness in terms of mitigating negative transfer as compared to other state-of-the-art ETO-based DMOAs. Moreover, the effectiveness of the soft labels provided by CFS in breaking the “either/or” limitation of hard labels is validated, facilitating a more flexible and comprehensive characterization of historical information, thereby promoting objective and effective knowledge transfer IEEE
Keyword ：
cascaded fuzzy system cascaded fuzzy system dynamic multiobjective optimization algorithm (DMOA) dynamic multiobjective optimization algorithm (DMOA) Evolutionary transfer optimization (ETO) Evolutionary transfer optimization (ETO) Fuzzy systems Fuzzy systems Heuristic algorithms Heuristic algorithms information characterization information characterization Knowledge transfer Knowledge transfer negative transfer negative transfer Optimization Optimization Prediction algorithms Prediction algorithms Sociology Sociology Statistics Statistics
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Li, H. , Wang, Z. , Zeng, N. et al. Promoting Objective Knowledge Transfer: A Cascaded Fuzzy System for Solving Dynamic Multiobjective Optimization Problems [J]. | IEEE Transactions on Fuzzy Systems , 2024 , 32 (11) : 1-15 . |
| MLA | Li, H. et al. "Promoting Objective Knowledge Transfer: A Cascaded Fuzzy System for Solving Dynamic Multiobjective Optimization Problems" . | IEEE Transactions on Fuzzy Systems 32 . 11 (2024) : 1-15 . |
| APA | Li, H. , Wang, Z. , Zeng, N. , Wu, P. , Li, Y. . Promoting Objective Knowledge Transfer: A Cascaded Fuzzy System for Solving Dynamic Multiobjective Optimization Problems . | IEEE Transactions on Fuzzy Systems , 2024 , 32 (11) , 1-15 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
ObjectiveThe objective of this study was to investigate the activity and connectivity of cerebral and cerebellar cortices underlying the sensory trick (ST) effects in patients with cervical dystonia (CD), using electroencephalography (EEG).MethodsWe recruited 15 CD patients who exhibited clinically effective ST and 15 healthy controls (HCs) who mimicked the ST maneuver. EEG signals and multiple-channel electromyography (EMG) were recorded simultaneously during resting and acting stages. EEG source analysis and functional connectivity were performed. To account for the effects of sensory processing, we calculated relative power changes as the difference in power spectral density between resting and the maneuver execution.ResultsST induced a decrease in low gamma (30-50 Hz) spectral power in the primary sensory and cerebellar cortices, which remained lower than in HCs during the maintenance period. Compared with HCs, patients exhibited consistently strengthened connectivity within the sensorimotor network during the maintenance period, particularly in the primary sensory-sensorimotor cerebellum connection.InterpretationThe application of ST resulted in altered cortical excitability and functional connectivity regulated by gamma oscillation in CD patients, suggesting that this effect cannot be solely attributed to motor components. The cerebellum may play important roles in mediating the ST effects.
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Cai, Nai-Qing , Shi, Wu-Xiang , Chen, Ru-Kai et al. Cerebral-Cerebellar Cortical Activity and Connectivity Underlying Sensory Trick in Cervical Dystonia [J]. | ANNALS OF CLINICAL AND TRANSLATIONAL NEUROLOGY , 2024 , 11 (10) : 2633-2644 . |
| MLA | Cai, Nai-Qing et al. "Cerebral-Cerebellar Cortical Activity and Connectivity Underlying Sensory Trick in Cervical Dystonia" . | ANNALS OF CLINICAL AND TRANSLATIONAL NEUROLOGY 11 . 10 (2024) : 2633-2644 . |
| APA | Cai, Nai-Qing , Shi, Wu-Xiang , Chen, Ru-Kai , Chen, Bo-Li , Li, Yu-Rong , Wang, Ning . Cerebral-Cerebellar Cortical Activity and Connectivity Underlying Sensory Trick in Cervical Dystonia . | ANNALS OF CLINICAL AND TRANSLATIONAL NEUROLOGY , 2024 , 11 (10) , 2633-2644 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Brain -computer interface (BCI) is an emerging technology which provides a road to control communication and external devices. Electroencephalogram (EEG) -based motor imagery (MI) tasks recognition has important research significance for stroke, disability and others in BCI fields. However, enhancing the classification performance for decoding MI -related EEG signals presents a significant challenge, primarily due to the variability across different subjects and the presence of irrelevant channels. To address this issue, a novel hybrid structure is developed in this study to classify the MI tasks via deep separable convolution network (DSCNN) and bidirectional long short-term memory (BLSTM). First, the collected time -series EEG signals are initially processed into a matrix grid. Subsequently, data segments formed using a sliding window strategy are inputted into proposed DSCNN model for feature extraction (FE) across various dimensions. And, the spatial -temporal features extracted are then fed into the BLSTM network, which further refines vital time -series features to identify five distinct types of MI -related tasks. Ultimately, the evaluation results of our method demonstrate that the developed model achieves a 98.09% accuracy rate on the EEGMMIDB physiological datasets over a 4 -second period for MI tasks by adopting full channels, outperforming other existing studies. Besides, the results of the five evaluation indexes of Recall, Precision, Test-auc, and F1 -score also achieve 97.76%, 97.98%, 98.63% and 97.86%, respectively. Moreover, a Gradient -class Activation Mapping (GRAD -CAM) visualization technique is adopted to select the vital EEG channels and reduce the irrelevant information. As a result, we also obtained a satisfying outcome of 94.52% accuracy with 36 channels selected using the Grad -CAM approach. Our study not only provides an optimal trade-off between recognition rate and number of channels with half the number of channels reduced, but also it can also advances practical application research in the field of BCI rehabilitation medicine, effectively.
Keyword ：
Data analysis Data analysis Data Processing Data Processing
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Li, Jixiang , Wang, Zhaoxuan , Li, Yurong . Recognition of EEG-based movement intention combined with channel selection adopting deep learning methods [J]. | JOURNAL OF INSTRUMENTATION , 2024 , 19 (5) . |
| MLA | Li, Jixiang et al. "Recognition of EEG-based movement intention combined with channel selection adopting deep learning methods" . | JOURNAL OF INSTRUMENTATION 19 . 5 (2024) . |
| APA | Li, Jixiang , Wang, Zhaoxuan , Li, Yurong . Recognition of EEG-based movement intention combined with channel selection adopting deep learning methods . | JOURNAL OF INSTRUMENTATION , 2024 , 19 (5) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Electroencephalography (EEG) is a non-invasive method for measuring brain activity, extensively utilized in neuroscience research. The process of reconstructing potential activation sources on the cortex from EEG signals measured at the scalp is referred to as EEG Source Imaging (ESI). Due to the requirement for ESI to address a highly ill-posed inverse problem, traditional methods often necessitate the design of neurophysiological reasonable priors to constrain the solution space. However, it is difficult to design a neural prior that accurately reflects the properties of brain sources. To overcome this limitation, this paper proposes a hybrid Long Short-Term Memory (LSTM) and Transformer network for ESI, called HLT-Net, which does not require a clear definition of neural priors. More specifically, bidirectional LSTM is introduced to capture temporal information in EEG signals. Then, we adopted the multi-head attention mechanism in the Transformer to enhance the global information perception of model. Furthermore, a mask layer has been added to the input of the model to enhance its robustness. The results from both simulated and real datasets demonstrate that HLT-Net outperforms existing technologies. © 2024 IEEE.
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Shi, W. , Li, Y. , Li, J. et al. HLT-Net: A Hybrid LSTM-Transformer Framework for EEG Source Imaging [未知]. |
| MLA | Shi, W. et al. "HLT-Net: A Hybrid LSTM-Transformer Framework for EEG Source Imaging" [未知]. |
| APA | Shi, W. , Li, Y. , Li, J. , Zheng, N. . HLT-Net: A Hybrid LSTM-Transformer Framework for EEG Source Imaging [未知]. |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Objective. The surface electromyography (EMG) signal reflects the user's intended actions and has become the important signal source for human-computer interaction. However, classification models trained on EMG signals from the same day cannot be applied for different days due to the time-varying characteristics of the EMG signal and the influence of electrodes shift caused by device wearing for different days, which hinders the application of commercial prosthetics. This type of gesture recognition for different days is usually referred to as long-term gesture recognition. Approach. To address this issue, we propose a long-term gesture recognition method by optimizing feature extraction, dimensionality reduction, and classification model calibration in EMG signal recognition. Our method extracts differential common spatial patterns features and then conduct dimensionality reduction with non-negative matrix factorization, effectively reducing the influence of the non-stationarity of the EMG signals. Based on clustering and classification self-training scheme, we select samples with high confidence from unlabeled samples to adaptively updates the model before daily formal use. Main results. We verify the feasibility of our method on a dataset consisting of 30 d of gesture data. The proposed gesture recognition scheme achieves accuracy over 90%, similar to the performance of daily calibration with labeled data. However, our method needs only one repetition of unlabeled gestures samples to update the classification model before daily formal use. Significance. From the results we can conclude that the proposed method can not only ensure superior performance, but also greatly facilitate the daily use, which is especially suitable for long-term application.
Keyword ：
adaptive update adaptive update gesture recognition gesture recognition long-term application long-term application surface electromyography surface electromyography
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Li, Yurong , Lin, Xiaofeng , Lin, Heng et al. An adaptive learning method for long-term gesture recognition based on surface electromyography [J]. | PHYSIOLOGICAL MEASUREMENT , 2024 , 45 (12) . |
| MLA | Li, Yurong et al. "An adaptive learning method for long-term gesture recognition based on surface electromyography" . | PHYSIOLOGICAL MEASUREMENT 45 . 12 (2024) . |
| APA | Li, Yurong , Lin, Xiaofeng , Lin, Heng , Zheng, Nan . An adaptive learning method for long-term gesture recognition based on surface electromyography . | PHYSIOLOGICAL MEASUREMENT , 2024 , 45 (12) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Precise time-frequency (TF) analysis of electroencephalogram (EEG) signals is critical in evaluating cortical responses of patients with cervical dystonia (CD). Traditional methods are faced with challenges of constrained time-frequency resolution and accuracy, limiting the application of EEG in CD patients. This study introduces a novel adaptive basis function-based for TF representation method to meet the challenge. The methodology begins by identifying the kernel function center through an adaptive clustering technique. Then, the optimum structures and scales of the kernel function are determined by the improved genetic algorithm, which enable more precise tracking of EEG signals. Finally, accurately estimated parameters are converted to high-resolution TF images using a parameter spectrum estimation method, providing more detailed information of the EEG data. Leveraging the insights from the TF images, a regression model correlating TF features with clinical scores was developed to assess severity of CD patients. Simulation results show that the proposed method has superior tracking capabilities and a higher time-frequency resolution than current state-of-the-art methods. In the analysis of real EEG signals, we observed a notable elevation in gamma band power within the C3 and P3 channels, significantly differing from healthy individuals (p < 0.05), however, which cannot be found by other methods. This indicates distinctive high-frequency cortical activation associated with CD. Moreover, the regression model reaches a correlation coefficient above 0.82, suggesting its potential for objectively assessing severity of CD patients. Collectively, this study provides a robust tool for EEG signal analysis, and the analysis result will contribute to clinic treatment.
Keyword ：
Adaptive Radial Basis Functions Adaptive Radial Basis Functions EEG signals EEG signals Improved genetic algorithm Improved genetic algorithm Regression analysis Regression analysis Time-frequency analysis Time-frequency analysis Time-varying parameter estimation Time-varying parameter estimation
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zheng, Nan , Li, Yurong . Adaptive radial basis functions based Time-Varying model for EEG analysis in patients with cervical dystonia [J]. | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2024 , 92 . |
| MLA | Zheng, Nan et al. "Adaptive radial basis functions based Time-Varying model for EEG analysis in patients with cervical dystonia" . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL 92 (2024) . |
| APA | Zheng, Nan , Li, Yurong . Adaptive radial basis functions based Time-Varying model for EEG analysis in patients with cervical dystonia . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2024 , 92 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Surface electromyography (sEMG) signals demonstrate how the muscles react to the control strategies of the nervous system. For stroke patients, the detection of muscle activation is crucial because it allows researchers to better understand their brain control mechanisms. However, extracting the muscular activity precisely from the sEMG signals with various complex noises is a challenge in biomedical data processing. This study presented an adaptive two-step (AdaTS) muscular activity recognition algorithm to handle this problem. The sEMG signal was first pre-extracted using the adaptive threshold approach and annexation method to identify the interval comprising active information. Then, after amplifying the difference of activity and inactivity of the interval signal through the Teager-Kaiser energy operator, the onsets and offsets were determined based on the overall change of the interval signal. The proposed algorithm was tested in semi-synthetic signals, real signals from a public database, and experimentally recorded signals. Compared with other approaches, our method can effectively handle various types of interference and produce the best detection performance. Additionally, the steps of parameter selection and adjustment are removed, which greatly simplifies the practical application.
Keyword ：
Band-pass filters Band-pass filters Electromyography Electromyography Interference Interference Muscle activity detection Muscle activity detection Muscles Muscles signal processing signal processing Signal processing algorithms Signal processing algorithms Stroke (medical condition) Stroke (medical condition) surface electromyography (sEMG) surface electromyography (sEMG) Teager-Kaiser energy operator Teager-Kaiser energy operator Turning Turning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zheng, Nan , Li, Yurong . Recognition of Muscular Activity in Complex Noise-Contaminated Myoelectric Signals: An Adaptive Two-Step Approach [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
| MLA | Zheng, Nan et al. "Recognition of Muscular Activity in Complex Noise-Contaminated Myoelectric Signals: An Adaptive Two-Step Approach" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 73 (2024) . |
| APA | Zheng, Nan , Li, Yurong . Recognition of Muscular Activity in Complex Noise-Contaminated Myoelectric Signals: An Adaptive Two-Step Approach . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
