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学者姓名：李玉榕
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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
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| 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 等. "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 . |
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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
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| 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 等. "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 . |
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Abstract ：
金线莲是一种珍贵中药材,其治疗、保健作用十分显著.金线莲培育方式主要有种植、组培等,不同培育方式的金线莲,在性状上仅表现出细微差异,但药用、市场价值差异显著,培育方式鉴别能有效保证药用疗效、维护良好市场秩序,然而由于不同品系、产地、培育时间等复合差异的影响,增加了培育方式鉴别难度与复杂度.提出一种基于改进1D-Inception-CNN模型的金线莲培育方式鉴别方法.采用近红外光谱仪采集种植、组培金线莲的光谱,首先使用合成少数类过采样技术(SMOTE)进行过采样以解决种植品、组培品样本比例不平衡问题,其次构建基于改进Inception结构的一维卷积神经网络对来自不同品系、产地、培育时间的金线莲进行种植品、组培品鉴别,最后采用贝叶斯优化方法对构建的卷积神经网络模型超参数进行优化;最终五折交叉验证平均鉴别准确率、精确率、召回率、综合评价指标高达97.95％、96.16％、100％、98.02％.研究表明,实验提出的鉴别模型为快速鉴别金线莲种植品、组培品提供一种有效方法.
Keyword ：
Inception模块 Inception模块 一维卷积神经网络 一维卷积神经网络 少数类过采样技术 少数类过采样技术 贝叶斯优化 贝叶斯优化 金线莲 金线莲
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| GB/T 7714 | 蓝艳 , 王武 , 许文 et al. 基于SMOTE和Inception-CNN的种植和组培金线莲鉴别 [J]. | 光谱学与光谱分析 , 2024 , 44 (1) : 158-163 . |
| MLA | 蓝艳 et al. "基于SMOTE和Inception-CNN的种植和组培金线莲鉴别" . | 光谱学与光谱分析 44 . 1 (2024) : 158-163 . |
| APA | 蓝艳 , 王武 , 许文 , 柴琴琴 , 李玉榕 , 张勋 . 基于SMOTE和Inception-CNN的种植和组培金线莲鉴别 . | 光谱学与光谱分析 , 2024 , 44 (1) , 158-163 . |
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Abstract ：
Currently, electroencephalogram (EEG)-based motor imagery (MI) signals have been received extensive attention, which can assist disabled subjects to control wheelchair, automatic driving and other activities. However, EEG signals are easily affected by some factors, such as muscle movements, wireless devices, power line, etc., resulting in the low signal-to-noise ratios and the worse recognition results on EEG decoding. Therefore, it is crucial to develop a stable model for decoding MI-EEG signals. To address this issue and further improve the decoding performance for MI tasks, a hybrid structure combining convolutional neural networks and bidirectional long short-term memory (BLSTM) model, namely CBLSTM, is developed in this study to handle the various EEG-based MI tasks. Besides, the attention mechanism (AM) model is further adopted to adaptively assign the weight of EEG vital features and enhance the expression which beneficial to classification for MI tasks. First of all, the spatial features and the time series features are extracted by CBLSTM from preprocessed MI-EEG data, respectively. Meanwhile, more effective features information can be mined by the AM model, and the softmax function is utilized to recognize intention categories. Ultimately, the numerical results illustrate that the model presented achieves an average accuracy of 98.40% on the public physioNet dataset and faster training process for decoding MI tasks, which is superior to some other advanced models. Ablation experiment performed also verifies the effectiveness and feasibility of the developed model. Moreover, the established network model provides a good basis for the application of brain-computer interface in rehabilitation medicine.
Keyword ：
Attention mechanism Attention mechanism Brain-computer interface Brain-computer interface Convolutional neural networks Convolutional neural networks Intention recognition Intention recognition Motor imagery Motor imagery
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| GB/T 7714 | Li, Jixiang , Shi, Wuxiang , Li, Yurong . An effective classification approach for EEG-based motor imagery tasks combined with attention mechanisms [J]. | COGNITIVE NEURODYNAMICS , 2024 . |
| MLA | Li, Jixiang et al. "An effective classification approach for EEG-based motor imagery tasks combined with attention mechanisms" . | COGNITIVE NEURODYNAMICS (2024) . |
| APA | Li, Jixiang , Shi, Wuxiang , Li, Yurong . An effective classification approach for EEG-based motor imagery tasks combined with attention mechanisms . | COGNITIVE NEURODYNAMICS , 2024 . |
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Anoectochilus roxburghii (Wall.) Lindl. (Orchidaceae) is one of the most precious Chinese medicine with extraordinary effects in medical treatment and health protection. Planting and tissue-cultured are two main cultivated methods of A. roxburghii. There are slight characteristic differences between Planting and tissue-cultured A. roxburghii, but they show significant differences in medicinal and market value. Therefore, the identification of cultivated methods plays an important role in effectively securing the medicinal efficacy of A. roxburghii and maintaining a good market order. However, due to the influence of composite differences such as different cultivars, different geographical origins and different times of cultivation, the difficulty and complexity of identification in cultivated methods increase heavily. This paper proposes an effective model to discriminative different cultivated methods of A. roxburghii based on improved 1D-inception-CNN. The experiments were conducted on two kinds of A. roxburghii, and their NIRS data were collected by a Fourier transform near-infrared spectrometer. Considering the unbalanced proportion of planting and tissue-cultured samples,the NIRS data was over sampled by using SMOTE first. Secondly, a one-dimensional convolutional neural network based on improved Inception was constructed to identify planting and tissue-cultured A. roxburghii though both include different varieties, different geographical origins and different cultivating times. Finally, Bayesian optimization was used to optimize the hyperparameters of the model. The final average identification accuracy, precision, recall, and F1-score of five-fold crossvalidation reached 97.95%, 96.16%, 100%, and 98.02%. The identification model proposed in this experiment provides a useful method to identify planting and tissue-cultured A. roxburghii effectively and rapidly and provides an idea for the identification of cultivation methods of other Chinese herbal medicines.
Keyword ：
Anoectochilus roxburghii Anoectochilus roxburghii Bayesian optimization Bayesian optimization Inception module Inception module One-dimensional convolutional neural network One-dimensional convolutional neural network SMOTE SMOTE
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| GB/T 7714 | Lan Yan , Wang Wu , Xu Wen et al. Discrimination of Planting and Tissue-Cultured Anoectochilus Roxburghii Based on SMOTE and Inception-CNN [J]. | SPECTROSCOPY AND SPECTRAL ANALYSIS , 2024 , 44 (1) : 158-163 . |
| MLA | Lan Yan et al. "Discrimination of Planting and Tissue-Cultured Anoectochilus Roxburghii Based on SMOTE and Inception-CNN" . | SPECTROSCOPY AND SPECTRAL ANALYSIS 44 . 1 (2024) : 158-163 . |
| APA | Lan Yan , Wang Wu , Xu Wen , Chai Qin-qin , Li Yu-rong , Zhang Xun . Discrimination of Planting and Tissue-Cultured Anoectochilus Roxburghii Based on SMOTE and Inception-CNN . | SPECTROSCOPY AND SPECTRAL ANALYSIS , 2024 , 44 (1) , 158-163 . |
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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. © 2024 IOP Publishing Ltd and Sissa Medialab.
Keyword ：
Biomedical signal processing Biomedical signal processing Brain computer interface Brain computer interface Data handling Data handling Deep learning Deep learning Economic and social effects Economic and social effects Electroencephalography Electroencephalography Electrophysiology Electrophysiology Learning systems Learning systems Physiological models Physiological models Time series Time series
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| 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) . |
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Abstract ：
本发明涉及一种基于自适应学习的用户无关肌电手势识别系统，包括依次连接的数据获取单元、聚类单元、自适应KNN近邻分类器和风险评估器；所述数据获取单元，获取现有用户数据，并进行数据处理；所述聚类单元，将数据处理后的信号数据，采用K‑Means聚类找到不同动作的聚类中心，提取各个用户每种动作与聚类中心距离最短的N个样本充当训练集，用于训练自适应KNN近邻分类器；所述自适应KNN近邻分类器，用于根据新用户数据得到对应的标签；所述风险评估器对新用户数据进行评估，合格的样本则用来替换训练集的偏远样本和更新训练集样本的权值。本发明解决了因肌电信号的个体差异性而导致的模型不通用问题，无需用户再训练步骤，极大的提高了用户的使用体验，且识别正确率会动态提升。
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| GB/T 7714 | 李玉榕 , 郑楠 , 张文萱 et al. 基于自适应学习的用户无关肌电手势识别系统 : CN202111376022.4[P]. | 2021-11-19 00:00:00 . |
| MLA | 李玉榕 et al. "基于自适应学习的用户无关肌电手势识别系统" : CN202111376022.4. | 2021-11-19 00:00:00 . |
| APA | 李玉榕 , 郑楠 , 张文萱 , 李吉祥 . 基于自适应学习的用户无关肌电手势识别系统 : CN202111376022.4. | 2021-11-19 00:00:00 . |
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Foot drop is the phenomenon that ankle joint cannot produce dorsiflexion and the toe lifting is incomplete or impossible due to nerve control dysfunction. The functional electrical stimulation (FES) is used as a treatment method to correct foot drop gait, which uses low-frequency pulse to stimulate tibialis anterior to cause muscle contraction and dorsiflexion of ankle joint. The FES output intensity modulation method based on EMG modulation and iterative learning control (ILC) is proposed in this article. The angular velocity signal of the lower leg is used to predict the EMG of tibialis anterior in healthy gait through the dynamic BP neural network, and the toe pitch is used as feedback signal to output the reference EMG through ILC. The reference EMG and the EMG predicted by neural network are weighted-average to obtain the modified EMG. Finally, the FES output is modulated by the muscle activation characteristics. The experimental results show that the toe pitch angle in open-loop EMG modulation mode is only about 17°. Through the closed-loop modulation mode, the maximum toe pitch angle is about 21°. By analyzing experimental data. It can be concluded that the system can help patients with foot drop to carry out rehabilitation training. © 2023 Science Press. All rights reserved.
Keyword ：
electromyography electromyography foot drop foot drop functional electrical stimulation functional electrical stimulation iterative learning control iterative learning control
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| GB/T 7714 | Wang, Z. , Li, Y. , Chen, K. . FES system for foot drop based on EMG modulation combined with iterative learning control; [肌电调制结合迭代学习控制的足下垂 FES 系统] [J]. | Chinese Journal of Scientific Instrument , 2023 , 44 (4) : 112-120 . |
| MLA | Wang, Z. et al. "FES system for foot drop based on EMG modulation combined with iterative learning control; [肌电调制结合迭代学习控制的足下垂 FES 系统]" . | Chinese Journal of Scientific Instrument 44 . 4 (2023) : 112-120 . |
| APA | Wang, Z. , Li, Y. , Chen, K. . FES system for foot drop based on EMG modulation combined with iterative learning control; [肌电调制结合迭代学习控制的足下垂 FES 系统] . | Chinese Journal of Scientific Instrument , 2023 , 44 (4) , 112-120 . |
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脊柱侧弯是一种高发于青少年群体中的脊柱疾病,为了解决X片评估脊柱侧弯所带来的辐射危害,本文设计并实现了基于形态识别的脊柱个性化建模和侧弯评估系统.首先,提取与脊柱相关的特征点,根据特征点之间的相对位置关系计算生成附加特征点;其次,设计并应用特征点校正算法和滤波算法以提高特征点的位置精度;最后,在Unity中将椎骨模型配准到由特征点拟合的脊柱线上,得到个性化三维脊柱模型,并计算Cobb角、胸椎后凸角、腰椎前凸角以评估脊柱侧弯.本文对 28 位受试者进行了实验,对比分析了系统评估结果与X片评估结果:Cobb角与实际Cobb角之间的皮尔逊相关系数为 0.82,平均绝对误差为 3.4°,均方根误差为 4.2°;胸椎后凸角与实际胸椎后凸角之间的皮尔逊相关系数为 0.80,平均绝对误差为 3.4°,均方根误差为3.8°;腰椎前凸角与实际腰椎前凸角之间的皮尔逊相关系数为0.78,平均绝对误差为3.2°,均方根误差为3.7°.实验结果表明,脊柱侧弯评估系统的精度较高,使用方便,可适用于大范围的青少年脊柱侧弯筛查.
Keyword ：
三维建模 三维建模 卡尔曼滤波 卡尔曼滤波 形态识别 形态识别 校正算法 校正算法 脊柱侧弯评估 脊柱侧弯评估
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| GB/T 7714 | 陈家瑾 , 李玉榕 , 叶王为 et al. 基于形态识别的脊柱个性化建模与侧弯评估系统 [J]. | 仪器仪表学报 , 2023 , 44 (10) : 210-218 . |
| MLA | 陈家瑾 et al. "基于形态识别的脊柱个性化建模与侧弯评估系统" . | 仪器仪表学报 44 . 10 (2023) : 210-218 . |
| APA | 陈家瑾 , 李玉榕 , 叶王为 , 苏婵娟 . 基于形态识别的脊柱个性化建模与侧弯评估系统 . | 仪器仪表学报 , 2023 , 44 (10) , 210-218 . |
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足下垂是指由于神经控制功能障碍导致踝关节无法产生背屈以致足尖上抬不完全或不能的现象。功能性电刺激(FES)作为纠正足下垂步态的治疗方法，通过低频脉冲刺激胫骨前肌引起肌肉收缩，使踝关节产生背屈动作，达到矫正足下垂的目的。本文提出了基于肌电(EMG)调制和迭代学习控制(ILC)的FES输出强度调制方法，利用小腿角速度信号通过动态BP神经网络预测健康步态胫骨前肌肌电信号，以脚尖俯仰角作为反馈信号通过ILC输出参考肌电信号，与神经网络预测的肌电信号加权平均得到修正后的肌电信号，最后利用肌肉激活特性调制FES输出。实验表明开环肌电调制模式下的脚尖俯仰角仅有17°左右，而在闭环调制模式下，脚尖俯仰角最大角度达到了21°左右。本文设计的FES控制系统可以帮助足下垂患者进行康复训练。
Keyword ：
功能性电刺激 功能性电刺激 肌电信号 肌电信号 足下垂 足下垂 迭代学习控制 迭代学习控制
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| GB/T 7714 | 王兆轩 , 李玉榕 , 陈楷 . 肌电调制结合迭代学习控制的足下垂FES系统 [J]. | 仪器仪表学报 , 2023 , 44 (04) : 112-120 . |
| MLA | 王兆轩 et al. "肌电调制结合迭代学习控制的足下垂FES系统" . | 仪器仪表学报 44 . 04 (2023) : 112-120 . |
| APA | 王兆轩 , 李玉榕 , 陈楷 . 肌电调制结合迭代学习控制的足下垂FES系统 . | 仪器仪表学报 , 2023 , 44 (04) , 112-120 . |
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