AIPING
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Aiping Liu

Aiping Liu (刘爱萍) received her BS degree in electrical engineering from the University of Science and Technology of China (USTC) in 2009, and received the MS and PhD degrees in electrical and computer engineering from the University of British Columbia (UBC) in 2011 and 2016, respectively. She has been a postdoc research fellow at the Pacific Parkinson's Research Center at UBC. Currently, she is the research associate professor in the Department of Electronic Science and Technology at the University of Science and Technology of China.

Email  /  Google Scholar

Education

  • University of British Columbia (UBC)   PhD, Electrical and Computer Engineering, May 2016.

  • University of British Columbia (UBC)   MSc, Electrical and Computer Engineering, Aug 2011.

  • University of Science and Technology of China (USTC)   BS, Electrical Engineering, June 2009.

    • Research Interest

  • Biomedical signal processing and neuroimaging analysis

  • Brain connectivity modeling and analysis

  • Functional brain region parcellation

  • Noninvasive brain stimulation

    • Publications

    Journal Papers (* corresponding author)

    1. A State-dependent IVA Model for Muscle Artifacts Removal from EEG Recordings
      Aiping Liu, G. Song, S. Lee, X. Fu, and X. Chen
      IEEE Transactions on Instrumentation and Measurement, pp. 1-1, 2021.

    2. Galvanic Vestibular Stimulation: Data Analysis and Applications in Neurorehabilitation
      Aiping Liu, S. Lee, X. Chen, M. McKeown, Z. J. Wang
      IEEE Signal Processing Magazine, to appear, 2021.

    3. MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG
      J. Zhang, D. Liang, Aiping Liu*, M. Gao, X. Chen, X. Zhang, and X. Chen
      IEEE Journal of Translational Engineering in Health and Medicine vol. 9, pp. 1-11, 2021.

    4. Current perspectives on galvanic vestibular stimulation in the treatment of Parkinson’s disease.
      S. Lee*, Aiping Liu*, and M. J. McKeown
      IEEE Instrumentation & Measurement Magazine, 24.2: 45-53, 2021.

    5. Recent Advances in Sparse Representation Based Medical Image Fusion
      Y. Liu, X. Chen, Aiping Liu, R. K. Ward and Z. J. Wang
      IEEE Instrumentation & Measurement Magazine, 24.2: 45-53, 2021.

    6. Novel Regional Activity Representation with Constrained Canonical Correlation Analysis for Brain Connectivity Network Estimation.
      J. Cai, Y. Wang, Aiping Liu*, M. J. McKeown, and Z. Jane Wang
      IEEE Transactions on Medical Imaging, vol. 39, no. 7, pp. 2363-2373, 2020.

    7. EEG-based emotion recognition using an end-to-end regional-asymmetric convolutional neural network
      H. Cui, Aiping Liu*, X. Zhang, X. Chen, K. Wang and X. Chen
      Knowledge-Based Systems, 205, 106243, 2020.

    8. ReMAE: A User-friendly Toolbox for Removing Muscle Artifacts from EEG
      X. Chen, Q. Liu, W. Tao, L. Li, S. Lee, Aiping Liu*, Q. Chen, J. Cheng, M. J. McKeown, and Z. J. Wang
      IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 5, pp. 2105-2119, 2020.

    9. Repetitive transcranial magnetic stimulation improves Parkinson’s freezing of gait via normalizing brain connectivity
      TM. Mi, S. Garg, F. Ba, Aiping Liu*, P. Liang, L. Gao, Q. Jia, E. Xu, K. Li*, P. Chan* and M. J. McKeown,
      npj Parkinson's Disease, 6(1), pp.1-9, 2020.

    10. ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network
      J. Zhang, Aiping Liu*, M. Gao, X. Chen, X. Zhang, and X. Chen*
      Artificial Intelligence in Medicine, vol. 106, pp. 101856, 2020.

    11. Both Stationary and Dynamic Functional Interhemispheric Connectivity Are Strongly Associated with Performance on Cognitive Tests in Multiple Sclerosis
      SJ. Lin, S. Kolind, Aiping Liu, K. McMullen, I. Vavasour, Z. J. Wang, A. Traboulsee, and M. J. McKeown
      Frontiers in Neurology 11: 407, 2020

    12. Emotion Recognition from Multi-Channel EEG via Deep Forest
      J. Cheng, M. Chen, C. Li, Y. Liu, R. Song, Aiping Liu, and X. Chen
      IEEE Journal of Biomedical and Health Informatics, pp. 1-1, 2020.

    13. Learning Dual Transformation Networks for Image Contrast Enhancement
      Y. Zhu, X. Fu and Aiping Liu
      IEEE Signal Processing Letters, 27, 1999-2003, 2020.

    14. High-frequency rTMS over the supplementary motor area improves freezing of gait in Parkinson's disease: a randomized controlled trial
      1T. Mi, S. Garg, F. Ba, Aiping Liu*, T. Wu, L. Gao, K. Li, P. Chan* and M. J. McKeown
      Parkinsonism & related disorders, vol. 68, pp. 85-90, 2019.

    15. Dynamic Graph Theoretic Analysis of Functional Connectivity in Parkinson’s Disease: The Importance of Fiedler value
      J. Cai, Aiping Liu*, T. Mi, W. Trappe, M. J. McKeown and Z. Jane Wang
      IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 4, pp. 1720-1729, 2019.

    16. Abnormal phase coupling in Parkinson’s disease and normalization effects of subthreshold vestibular stimulation
      S. Lee, Aiping Liu*, Z. J. Wang, and M. J. McKeown
      Frontiers in Human Neuroscience，vol.13, pp. 118, 2019.

    17. Removal of EMG Artifacts from Multichannel EEG Signals Using Combined Singular Spectrum Analysis and Canonical Correlation Analysis.
      Q. Liu, Aiping Liu*, X. Zhang, X. Chen, R. Qian*, and X. Chen
      Journal of Healthcare Engineering, vol. 2019, Article ID 4159676, 13 pages

    18. Remove Diverse Artifacts Simultaneously from a Single-Channel EEG Based on SSA and ICA: A Semi-Simulated Study
      J. Cheng, L. Li, C. Li, Y. Liu, Aiping Liu, R. Qian and X. Chen
      IEEE Access7, pp. 60276-60289, 2019

    19. Removal of muscle artifacts from the EEG: a review and recommendations
      X. Chen, X. Xu, Aiping Liu, S. Lee, X. Chen, X. Zhang, M. J. McKeown, and Z. J. Wang
      IEEE Sensors Journal, vol. 19, no. 14, pp. 5353-5368, 2019.

    20. Approximate Policy-Based Accelerated Deep Reinforcement Learning
      X. Wang, Y. Gu, Y. Cheng, Aiping Liu, and CL P. Chen
      IEEE transactions on neural networks and learning systems, vol. 31, no. 6, pp. 1820-1830, 2019.

    21. Decreased subregional specificity of the putamen in Parkinson's Disease revealed by dynamic connectivity-derived parcellation
      Aiping Liu, S-J Lin, T. Mi, X. Chen, P. Chan, Z. J. Wang, and M. J. McKeown
      NeuroImage: Clinical 20: 1163-1175. 8, 2018.

    22. Galvanic Vestibular Stimulation (GVS) Augments Deficient Pedunculopontine Nucleus (PPN) Connectivity in Mild Parkinson's Disease: fMRI Effects of Different Stimuli
      J. Cai, S. Lee, F. Ba, S. Garg, L. J. Kim, Aiping Liu*, D. Kim, Z. J. Wang and M. J. McKeown
      Frontiers in Neuroscience, vol. 12, pp. 101, 2018.

    23. The use of multivariate EMD and CCA for denoising muscle artifacts from few-channel EEG recordings
      X. Chen, X. Xu, Aiping Liu*, M. J. McKeown and Z. J. Wang
      IEEE Transactions on Instrumentation and Measurement, vol. 67, no. 2, pp. 359-370, 2018.

    24. Dual Hypergraph Regularized PCA for Biclustering of Tumor Gene Expression Data
      X. Wang, J. Liu, Y. Cheng, Aiping Liu and E. Chen
      IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 12, pp. 2292-2303, 2018.

    25. Position-independent gesture recognition using sEMG signals via canonical correlation analysis
      J. Cheng, F. Wei, C. Li, Y. Liu, Aiping Liu and X. Chen
      Computers in Biology and Medicine, vol. 88, pp. 1-10, 2017.

    26. Connectivity-based parcellation of functional SubROIs in putamen using a sparse spatially regularized regression model
      Y. Zhang, Aiping Liu*, S. N. Tan, M. J. McKeown, Z. J. Wang
      Biomedical Signal Processing and Control, Volume 27, pp. 174-183, May 2016.

    27. A Combined Static and Dynamic Model for Resting State fMRI Brain Connectivity Networks: Application to Parkinson’s Disease
      Aiping Liu, X. Chen, X. Dan, M. J. McKeown and Z. J. Wang
      IEEE Journal of Selected Topics in Signal Processing, vol. 10, no.7, pp. 1172-1181, 2016.

    28. Removing Muscle Artifacts from EEG Data: Multichannel or Single-Channel Techniques?
      X. Chen, Aiping Liu*, J. Chiang, Z. J. Wang, M. J. McKeown, R. K. Ward
      IEEE Sensors Journal, vol. 16, no. 7, pp. 1986-1997, 2016.

    29. A Sticky Weighted Regression Model for Time-Varying Resting State Brain Connectivity Estimation
      Aiping Liu, X. Chen, M. J. McKeown and Z. J. Wang
      IEEE Transactions on Biomedical Engineering, vol. 62, no.3, pp. 501–510, 2015.

    30. A Genetically Informed, Group fMRI Connectivity Modeling Approach: Application to Schizophrenia
      Aiping Liu, X. H. Chen, Z. J. Wang, Q. Xu, S. Appel-Cresswell and M. J. McKeown
      IEEE Transactions on Biomedical Engineering, vol.61, no.3, pp.946-956, 2014.

    31. Network analysis of perception-action coupling in infants
      N. Rotem-Kohavi, C. G. Hilderman, Aiping Liu, N. Makan, Z. J. Wang and N. Virji-Babul
      Frontiers in human neuroscience, vol. 8, no. 209, 2014.

    32. An EEMD-IVA Framework for Concurrent Multidimensional EEG and Unidimensional Kinematic Data Analysis
      X. Chen, Aiping Liu*, H. Poizner, M. J. Mckeown and Z. J. Wang
      IEEE Transactions on Biomedical Engineering, vol. 61, no. 7, pp. 2187-2198, 2014.

    33. Changes in functional brain networks following sports related concussion in adolescents
      N. Virji-Babul, C. Hilderman, N. Makan, Aiping Liu, J. Smith-Forrester, C. Franks and Z. J. Wang
      Journal of neurotrauma, vol. 31, no. 23, pp. 1914-1919, 2014.

    34. A Preliminary Study of Muscular Artifact Cancellation in Single-Channel EEG
      X. Chen, Aiping Liu, H. Peng and R. K. Ward
      Sensors, vol. 14, no. 10, pp. 18370-18389, 2014.

    35. Corticomuscular Activity Modeling by Combining Partial Least Squares and Canonical Correlation Analysis
      X. Chen, Aiping Liu, Z. J. Wang, H. Peng
      Journal of Applied Mathematics, vol. 2013, Article ID 401976, 11 pages, 2013.

    36. Parkinson's disease rigidity: relation to brain connectivity and motor performance
      N. Baradaran, S. N. Tan, Aiping Liu, A. Ashoori, S. J. Palmer, Z. J. Wang, M. Oishi and M. J. McKeown
      Frontiers in neurology, vol. 4, no. 67, 2013.

    37. A Computationally Efficient, Exploratory Approach to Brain Connectivity Incorporating False Discov- ery Rate Control, A Priori Knowledge, and Group Inference,
      Aiping Liu, J. Li, Z. J. Wang, and M. J. McKeown
      Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 967380, 14 pages, 2012.

    38. Network modeling and analysis of lumbar muscle surface EMG signals during flexion–extension in individuals with and without low back pain
      Aiping Liu, Z. J. Wang and Y. Hu
      Journal of Electromyography and Kinesiology, volume 21, pp. 913-921, 2011.

    Academic Activities

  • Academic editor, Journal of Healthcare Engineering.

  • Guest Associate Editor, Frontiers in Aging Neuroscience/Journal of Neuroscience Methods.

  • Publicity Chair, 20th IEEE International Workshop on Multimedia Signal Processing (MMSP), Vancouver, Aug. 2018.

  • TPC, 5th IEEE Global Conference on Signal and Information Processing, Montreal, Nov. 2017.

  • Workshop presentation, "Genetic informed brain connectivity modeling using fMRI signals", 2013.

  • CMOS Emerging Technologies Emerging Technologies, Whistler, July 2013.