ag视讯打不开-AG全讯网puma

Faculty

中文       Go Back       Search
CHEN Yifeng
Research?Assistant Professor

Dr. Yi-Feng Chen, research assistant professor in the Department of Biomedical Engineering of Southern University of Science and Technology (2023-present). He received the M.Sc. and Ph.D. degrees from the School of Information Engineering, Wuhan University of Technology, China. Dr. Chen also came to Yuan Ze University in Taiwan and the University of Auckland in New Zealand for two years study as an exchange student in 2012 and 2015, respectively. He has completed a postdoctoral fellowship with joint appointment from the Academy for Advanced Interdisciplinary Studies and the Department of Biomedical Engineering, Southern University of Science and Technology, China. Dr. Chen has broad research interests and experience across biomedical signal processing, especially in brain monitoring during anesthesia and in the intensive care unit, brain-computer interfaces, neural networks and artificial intelligence.


Education:

2014.9 - 2017.10  Ph.D.  School of Information Engineering, Wuhan University of Technology, China

                                      Supervisor: Quan Liu

2011.9 - 2014.6  M.Sc.  School of Information Engineering, Wuhan University of Technology, China

                                     Supervisor: Zude Zhou

2007.9 - 2011.6  B. Sc.  School of Information Engineering, Wuhan University of Technology, China


Work Experience:

2020.11 - 2022.11  Postdoc  Southern University of Science and Technology, Shenzhen, China

2018.01 - 2020.10  System Engineer  Wuhan United Imaging Healthcare Co., Ltd., Wuhan, China


Research Area:                                                                    

1. Brain-computer interface

2. Machine learning

3. Signal processing

4. Depth of anesthesia monitoring based on EEG


Representative Publications:

1. Yi-Feng Chen#, Ruiqi Fu#, Junde Wu, Jongbin Song, Rui Ma, Yi-chuan Jiang, Mingming Zhang*, Continuous Bimanual Trajectory Decoding of Coordinated Movement from EEG Signals, IEEE Journal of Biomedical and Health Informatics, vol. 26: 6012-6023, 2022. (IF: 7.021)

2. Mingming Zhang#, Junde Wu#, Jongbin Song, Ruiqi Fu, Rui Ma, Yi-chuan Jiang, Yi-Feng Chen*, Decoding Coordinated Directions of Bimanual Movements from EEG Signals, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31: 248-259, 2022. (IF: 4.528)

3. Yi-Feng Chen; Shou-Zen Fan; Maysam F. Abbod; Jiann-Shing Shieh*; Mingming Zhang*, Electroencephalogram Variability Analysis for Monitoring Depth of Anesthesia, Journal of Neural Engineering, vol. 18, no. 6, 2021, Art no. 066015. (IF=5.379)

4. Yi-Feng Chen; Shou-Zen Fan; Maysam F. Abbod; Jiann-Shing Shieh*; Mingming Zhang*, Nonlinear Analysis of Electroencephalogram Variability as a Measure of the Depth of Anesthesia, IEEE Transactions on Instrumentation and Measurement, vol. 71, 2022, Art no. 4004413. (IF=5.332)

5. Ruiqi Fu, Yi-Feng Chen, Yongqi Huang, Shuping Chen, Feiyan Duan, Jiewei Li, Jianhui Wu, Dongmei Jiang, Junling Gao, Jason Gu, Mingming Zhang*, Chunqi Chang*, Symmetric Convolutional and Adversarial Neural Network Enables Improved Mental Stress Classification from EEG, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30: 1384-1400, 2022. (IF: 4.528)

6. Rui Ma; Yichuan Jiang; Yi-Feng Chen; Mingming Zhang*, A New EEG-based Paradigm for Classifying Intention of Compound-Limbs Movement, IEEE International Conference on Advanced Robotics and Mechatronics (ARM), July 7-9, 2022, Guilin, China. (Best Conference Paper Award Finalist)

7. Yi-Feng Chen; Kiran Atal; Sheng-Quan Xie; Quan Liu*, A New Multivariate Empirical Mode Decomposition Method for Improving the Performance of SSVEP-based Brain-computer Interface, Journal of Neural Engineering, vol. 14, no. 4, 2017, Art no. 046028. (IF=5.379)

8. Quan Liu; Yi-Feng Chen; Shou-Zen Fan; Maysam F. Abbod; Jiann-Shing Shieh*, Quasi-periodicities Detection Using Phase-rectified Signal Averaging in EEG Signals as a Depth of Anesthesia Monitor, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25: 1773 - 1784, 2017. (IF: 4.528)

9. Quan Liu; Yi-Feng Chen; Shou-Zen Fan; Maysam F. Abbod; Jiann-Shing Shieh*, EEG Artifacts Reduction by Multivariate Empirical Mode Decomposition and Multiscale Entropy for Monitoring Depth of Anaesthesia during Surgery, Medical & Biological Engineering & Computing, vol. 55: 1435-1450, 2017. (IF: 3.079)

10. Quan Liu; Yi-Feng Chen; Shou-Zen Fan; Maysam F. Abbod; Jiann-Shing Shieh*, Improved Spectrum Analysis in EEG for Measure of Depth of Anesthesia based on Phase-rectified Signal Averaging, Physiological Measurement, vol. 38, no.2, 2017, Art no. 116. (IF: 2.688)

11. Quan Liu; Yi-Feng Chen; Shou-Zen Fan; Maysam F. Abbod; Jiann-Shing Shieh*, A Comparison of Five Different Algorithms for EEG Signal Analysis in Artifacts Rejection for Monitoring Depth of Anesthesia, Biomedical Signal Processing and Control, vol. 25: 24-34, 2016. (IF: 5.076)

12. Yi-Feng Chen; Jiann-Shing Shieh; Shou-Zen Fan; Wan-Ting Chiang; Maysam F. Abbod; Quan Liu*, Analyzing Heart Rate Variability Using a Photoplethysmographic Signal Measuring System, IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), 29-31 Aug. 2016, Auckland, New Zealand.

13. Quan Liu; Yi-Feng Chen; Shou-Zen Fan; Maysam F. Abbod; Jiann-Shing Shieh*, EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks, Computational and Mathematical Methods in Medicine, vol. 2015, 2015, Art no. 232381. (IF: 2.809)

浠水县| 百家乐官网编单短信接收| 百家乐官网免费下| 马尼拉百家乐官网的玩法技巧和规则 | 百家乐任你博娱乐场| 赌片百家乐的玩法技巧和规则| 百家乐职业打| 大发888公司赌场| 澳门网上网址| 百家乐官网现金平台排名| 曼哈顿百家乐官网娱乐城| 澳门百家乐皇冠网| 赌博百家乐的玩法技巧和规则| 潘多拉百家乐官网的玩法技巧和规则 | 百家乐官网singapore| 南京百家乐官网的玩法技巧和规则 | 属狗与属猪能做生意吗| 百家乐凯时赌场娱乐网规则| 百家乐园首选海立方| 香港六合彩开码| 百家乐官网平台哪个比较安全| 百家乐销售视频| 德晋百家乐的玩法技巧和规则| 棋牌游戏源码| 百家乐官网在线投注顺势法| 百家乐做中介赚钱| 大发888娱乐城备用| 乌兰浩特市| 好运来百家乐官网的玩法技巧和规则 | 百家乐皇室百家乐的玩法技巧和规则| 皇冠走地网| 百家乐官网庄家怎样赚钱| 百家乐网上投注作弊| 百家乐高级技巧| 资阳市| 百家乐玩法守则| bet365备用网址器| 在线百家乐官网合作| 百家乐大天堂| 百家乐官网破解打法| 玩百家乐官网去哪个娱乐城最安全|