个人简介：
刘爱萍，江苏连云港人，中国科学技术大学特任副研究员。
2009年本科毕业于中国科学技术大学电子科学与技术系，硕士和博士毕业于加拿大英属哥伦比亚大学(University of British Columbia)电子与计算机工程系。
研究方向为医学人工智能，医学影像分析，生理电信号分析，脑信息处理及其在神经退行性疾病研究中的应用。
担任Journal of Healthcare Engineering学术编委，Journal of Neuroscience Methods和Frontiers in Neuroscience客座编委。为IEEE trans. on Medical Imaging, IEEE trans. on Biomedical Engineering, IEEE Trans. on Neural Networks and Learning Systems等数多个国际期刊审稿人。发表学术论文40余篇，已主持国家级项目两项，省级项目一项。
邮箱：aipingl@ustc.edu.cn
google scholar: https://scholar.google.com/citations?user=vEDf62sAAAAJ&hl=en

代表论文：
J. Cai, Y. Wang, Aiping Liu*, M. J. McKeown, and Z. Jane Wang. "Novel Regional Activity Representation with Constrained Canonical Correlation Analysis for Brain Connectivity Network Estimation." IEEE Transactions on Medical Imaging, pp. 1-1, 2020.
H. Cui, Aiping Liu*, X. Zhang, X. Chen, K. Wang and X. Chen, “EEG-based emotion recognition using an end-to-end regional-asymmetric convolutional neural network”,Knowledge-Based Systems,205, 106243, 2020.
X. Chen, Q. Liu, W. Tao, L. Li, S. Lee, Aiping Liu*, Q. Chen, J. Cheng, M. J. McKeown, and Z. J. Wang. "ReMAE: A User-friendly Toolbox for Removing Muscle Artifacts from EEG",IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 5, pp. 2105-2119, 2020.
J. Zhang, Aiping Liu*, M. Gao, X. Chen, X. Zhang, and X. Chen*, "ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network."Artificial Intelligence in Medicine,vol. 106, pp. 101856, 2020.
TM. Mi, S. Garg, F. Ba, Aiping Liu*, P. Liang, L. Gao, Q. Jia, E. Xu, K. Li*, P. Chan* and M. J. McKeown, “Repetitive transcranial magnetic stimulation improves Parkinson’s freezing of gait via normalizing brain connectivity”,npj Parkinson's Disease,6(1), pp.1-9, 2020.
T. Mi, S. Garg, F. Ba, Aiping Liu*, T. Wu, L. Gao, K. Li, P. Chan* and M. J. McKeown, “High-frequency rTMS over the supplementary motor area improves freezing of gait in Parkinson's disease: a randomized controlled trial”.Parkinsonism & related disorders,vol. 68, pp. 85-90, 2019.
J. Cai, Aiping Liu*, T. Mi, W. Trappe, M. J. McKeown and Z. Jane Wang, “Dynamic Graph Theoretic Analysis of Functional Connectivity in Parkinson’s Disease: The Importance of Fiedler value”, IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 4, pp. 1720-1729, 2019.
S. Lee, Aiping Liu*, Z. J. Wang, and M. J. McKeown. "Abnormal phase coupling in Parkinson’s disease and normalization effects of subthreshold vestibular stimulation."Frontiers in Human Neuroscience，vol.13, pp. 118, 2019.
Q. Liu, Aiping Liu*, X. Zhang, X. Chen, R. Qian*, and X. Chen. "Removal of EMG Artifacts from Multichannel EEG Signals Using Combined Singular Spectrum Analysis and Canonical Correlation Analysis."Journal of Healthcare Engineering, vol. 2019, Article ID **, 13 pages, doi:10.1155/2019/**, 2019.
X. Chen, X. Xu, Aiping Liu, S. Lee, X. Chen, X. Zhang, M. J. McKeown, and Z. J. Wang. "Removal of muscle artifacts from the EEG: a review and recommendations",IEEE Sensors Journal, vol. 19, no. 14, pp.5353-5368, 2019.
Aiping Liu, S-J Lin, T. Mi, X. Chen, P. Chan, Z. J. Wang, and M. J. McKeown. "Decreased subregional specificity of the putamen in Parkinson's Disease revealed by dynamic connectivity-derived parcellation",NeuroImage: Clinical20: 1163-1175. 8, 2018.
J. Cai, S. Lee, F. Ba, S. Garg, L. J. Kim, Aiping Liu*, D. Kim, Z. J. Wang and M. J. McKeown, “Galvanic Vestibular Stimulation (GVS) Augments Deficient Pedunculopontine Nucleus (PPN) Connectivity in Mild Parkinson's Disease: fMRI Effects of Different Stimuli”, Frontiers in Neuroscience,vol. 12, pp. 101, 2018.
X. Chen, X. Xu, Aiping Liu*, M. J. McKeown and Z. J. Wang, “The use of multivariate EMD and CCA for denoising muscle artifacts from few-channel EEG recordings”, IEEE Transactions on Instrumentation and Measurement, vol. 67, no. 2, pp. 359-370, 2018.
X. Chen, Aiping Liu*, Q. Chen, Y. Liu, L. Zou, M. J. McKeown, “Simultaneous ocular and muscle artifact removal from EEG data by exploiting diverse statistics”, Computers in Biology and Medicine, vol. 88, pp. 1-10, 2017.
Y. Zhang, Aiping Liu^?, S. N. Tan, M. J. McKeown, Z. J. Wang, “Connectivity-based parcellation of functional SubROIs in putamen using a sparse spatially regularized regression model”, Biomedical Signal Processing and Control, Volume 27, pp. 174-183, May 2016.
Aiping Liu, X. Chen, X. Dan, M. J. McKeown and Z. J. Wang, “A Combined Static and Dynamic Model for Resting State fMRI Brain Connectivity Networks: Application to Parkinson’s Disease”, IEEE Journal of Selected Topics in Signal Processing, vol. 10, no.7, pp. 1172-1181, 2016.
X. Chen,Aiping Liu*, J. Chiang, Z. J. Wang, M. J. McKeown, R. K. Ward, “Removing Muscle Artifacts from EEG Data: Multichannel or Single-Channel Techniques?” IEEE Sensors Journal, vol. 16, no. 7, pp. 1986-1997, 2016.
Aiping Liu, X. Chen, M. J. McKeown and Z. J. Wang, “A Sticky Weighted Regression Model for Time-Varying Resting State Brain Connectivity Estimation”, IEEE Transactions on Biomedical Engineering, vol. 62, no.3, pp. 501–510, 2015.
Aiping Liu, X. H. Chen, Z. J. Wang, Q. Xu, S. Appel-Cresswell and M. J. McKeown, “A Genetically Informed, Group fMRI Connectivity Modeling Approach: Application to Schizophrenia”, IEEE Transactions on Biomedical Engineering, vol.61, no.3, pp.946-956, 2014.
X. Chen, Aiping Liu*, H. Poizner, M. J. Mckeown and Z. J. Wang, “An EEMD-IVA Framework for Concurrent Multidimensional EEG and Unidimensional Kinematic Data Analysis”, IEEE Transactions on Biomedical Engineering,vol. 61, no. 7, pp. 2187-2198, 2014.