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文献清单:神经工程与智能感知 | MDPI Bioengineering |
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期刊名:Bioengineering
期刊:https://www.mdpi.com/journal/bioengineering
神经电生理信号的高精度获取与智能解析正推动脑科学、临床诊疗与人机交互的深度融合。本专题汇集 Bioengineering 系列研究,系统展示了脑电图(EEG)与表面肌电图(sEMG)两大领域的创新探索:在脑电信号方面,涵盖颅内EEG-fMRI映射、毛发区干电极设计、阿尔茨海默病熵值分析、年龄特征综述、ICU异常检测、酒精中毒识别及SSVEP解码;在肌电信号方面,涉及可拉伸传感器、下肢协同激活图谱、手写识别、手势识别、关节角度估计及气溶胶喷射打印技术。这些工作共同展现了从传感器硬件到智能算法的全链条创新,为神经疾病诊疗、运动康复及下一代人机交互奠定了重要基础。
1. Measurement of the Mapping between Intracranial EEG and fMRI Recordings in the Human Brain
测量人脑颅内脑电图与功能磁共振成像记录之间的映射关系
https://www.mdpi.com/2306-5354/11/3/224
Carmichael, D.W.; Vulliemoz, S.; Murta, T.; Chaudhary, U.; Perani, S.; Rodionov, R.; Rosa, M.J.; Friston, K.J.; Lemieux, L. Measurement of the Mapping between Intracranial EEG and fMRI Recordings in the Human Brain. Bioengineering 2024, 11, 224.
2. Active Claw-Shaped Dry Electrodes for EEG Measurement in Hair Areas
用于毛发区域脑电图测量的主动式爪形干电极
https://www.mdpi.com/2306-5354/11/3/276
Wang, Z.; Ding, Y.; Yuan, W.; Chen, H.; Chen, W.; Chen, C. Active Claw-Shaped Dry Electrodes for EEG Measurement in Hair Areas. Bioengineering 2024, 11, 276.
3. A Novel Metric for Alzheimer’s Disease Detection Based on Brain Complexity Analysis via Multiscale Fuzzy Entropy
基于多尺度模糊熵的大脑复杂性分析的阿尔茨海默病检测新指标
https://www.mdpi.com/2306-5354/11/4/324
Cataldo, A.; Criscuolo, S.; De Benedetto, E.; Masciullo, A.; Pesola, M.; Schiavoni, R. A Novel Metric for Alzheimer’s Disease Detection Based on Brain Complexity Analysis via Multiscale Fuzzy Entropy. Bioengineering 2024, 11, 324.
4. Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review
静息态脑电信号的年龄相关特征及其相应的分析方法:综述
https://www.mdpi.com/2306-5354/11/5/418
Kang, J.-H.; Bae, J.-H.; Jeon, Y.-J. Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review. Bioengineering 2024, 11, 418.
5. Deep-Learning-Based Automated Anomaly Detection of EEGs in Intensive Care Units
基于深度学习的重症监护病房脑电图异常自动检测
https://www.mdpi.com/2306-5354/11/5/421
Wu, J.C.-H.; Liao, N.-C.; Yang, T.-H.; Hsieh, C.-C.; Huang, J.-A.; Pai, Y.-W.; Huang, Y.-J.; Wu, C.-L.; Lu, H.H.-S. Deep-Learning-Based Automated Anomaly Detection of EEGs in Intensive Care Units. Bioengineering 2024, 11, 421.
6. Fast Fractional Fourier Transform-Aided Novel Graphical Approach for EEG Alcoholism Detection
基于快速分数阶傅里叶变换的新型图形化脑电图酒精中毒检测方法
https://www.mdpi.com/2306-5354/11/5/464
Sadiq, M.T.; Yousaf, A.; Siuly, S.; Almogren, A. Fast Fractional Fourier Transform-Aided Novel Graphical Approach for EEG Alcoholism Detection. Bioengineering 2024, 11, 464.
7. A Convolutional Neural Network for SSVEP Identification by Using a Few-Channel EEG
基于少通道脑电图的卷积神经网络稳态视觉诱发电位识别
https://www.mdpi.com/2306-5354/11/6/613
Li, X.; Yang, S.; Fei, N.; Wang, J.; Huang, W.; Hu, Y. A Convolutional Neural Network for SSVEP Identification by Using a Few-Channel EEG. Bioengineering 2024, 11, 613.
8. A Comparative Study on Imputation Techniques: Introducing a Transformer Model for Robust and Efficient Handling of Missing EEG Amplitude Data
插补技术比较研究:引入Transformer模型以稳健高效地处理缺失的脑电图振幅数据
https://www.mdpi.com/2306-5354/11/8/740
Khan, M.A. A Comparative Study on Imputation Techniques: Introducing a Transformer Model for Robust and Efficient Handling of Missing EEG Amplitude Data. Bioengineering 2024, 11, 740.
9. Optimizing RNNs for EMG Signal Classification: A Novel Strategy Using Grey Wolf Optimization
优化循环神经网络用于肌电信号分类:一种基于灰狼优化的新策略
https://www.mdpi.com/2306-5354/11/1/77
Aviles, M.; Alvarez-Alvarado, J.M.; Robles-Ocampo , J.-B.; Sevilla-Camacho , P.Y.; Rodríguez-Reséndiz, J. Optimizing RNNs for EMG Signal Classification: A Novel Strategy Using Grey Wolf Optimization. Bioengineering 2024, 11, 77.
10. Stretchable, Flexible, Breathable, Self-Adhesive Epidermal Hand sEMG Sensor System
可拉伸、柔韧、透气、自粘性表皮手部表面肌电信号传感器系统
https://www.mdpi.com/2306-5354/11/2/146
Yang, K.; Zhang, S.; Hu, X.; Li, J.; Zhang, Y.; Tong, Y.; Yang, H.; Guo, K. Stretchable, Flexible, Breathable, Self-Adhesive Epidermal Hand sEMG Sensor System. Bioengineering 2024, 11, 146.
11. The Lower Limb Muscle Co-Activation Map during Human Locomotion: From Slow Walking to Running
人体运动过程中下肢肌肉协同激活图谱:从慢走到跑步
https://www.mdpi.com/2306-5354/11/3/288
Fiori, L.; Castiglia, S.F.; Chini, G.; Draicchio, F.; Sacco, F.; Serrao, M.; Tatarelli, A.; Varrecchia, T.; Ranavolo, A. The Lower Limb Muscle Co-Activation Map during Human Locomotion: From Slow Walking to Running. Bioengineering 2024, 11, 288.
12. Intelligent Human–Computer Interaction: Combined Wrist and Forearm Myoelectric Signals for Handwriting Recognition
智能人机交互:结合腕部和前臂肌电信号进行手写识别
https://www.mdpi.com/2306-5354/11/5/458
Tigrini, A.; Ranaldi, S.; Verdini, F.; Mobarak, R.; Scattolini, M.; Conforto, S.; Schmid, M.; Burattini, L.; Gambi, E.; Fioretti, S.; et al. Intelligent Human–Computer Interaction: Combined Wrist and Forearm Myoelectric Signals for Handwriting Recognition. Bioengineering 2024, 11, 458.
13. Effects of Exercise on the Inter-Session Accuracy of sEMG-Based Hand Gesture Recognition
运动对基于表面肌电信号的手势识别的会间准确率的影响
https://www.mdpi.com/2306-5354/11/8/811
Liu, X.; Dai, C.; Liu, J.; Yuan, Y. Effects of Exercise on the Inter-Session Accuracy of sEMG-Based Hand Gesture Recognition. Bioengineering 2024, 11, 811.
14. Estimation of Lower Limb Joint Angles Using sEMG Signals and RGB-D Camera
利用表面肌电信号和RGB-D相机估计下肢关节角度
https://www.mdpi.com/2306-5354/11/10/1026
Du, G.; Ding, Z.; Guo, H.; Song, M.; Jiang, F. Estimation of Lower Limb Joint Angles Using sEMG Signals and RGB-D Camera. Bioengineering 2024, 11, 1026.
15. Development of a Wearable Electromyographic Sensor with Aerosol Jet Printing Technology
利用气溶胶喷射打印技术开发可穿戴肌电传感器
https://www.mdpi.com/2306-5354/11/12/1283
Perilli, S.; Di Pietro, M.; Mantini, E.; Regazzetti, M.; Kiper, P.; Galliani, F.; Panella, M.; Mantini, D. Development of a Wearable Electromyographic Sensor with Aerosol Jet Printing Technology. Bioengineering 2024, 11, 1283.
Bioengineering 期刊介绍
主编:Anthony Guiseppi-Elie, Anderson University, USA
主要发表生物医学工程及应用,生物过程和生物系统工程与应用,生物分子、细胞和组织工程及应用,以及生化工程与应用等相关领域的最新科学技术及应用。期刊已被PubMed、Scopus、SCIE (Web of Science) 等数据库收录。
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2024 Impact Factor
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3.7
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2024 CiteScore
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5.3
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Time to First Decison
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17 days
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Acceptance to Publication
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2.8 days
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