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Xun Chen
Researcher at University of Science and Technology of China
Publications - 230
Citations - 7083
Xun Chen is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 27, co-authored 143 publications receiving 3549 citations. Previous affiliations of Xun Chen include University of British Columbia & Hefei University of Technology.
Papers
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A Sticky Weighted Regression Model for Time-Varying Resting-State Brain Connectivity Estimation
TL;DR: A time-varying model is presented to investigate the temporal dynamics of brain connectivity networks in Parkinson's disease subjects to provide insights into brain dynamics associated with PD and may serve as a potential biomarker in future studies.
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Adaptive Calibration of Electrode Array Shifts Enables Robust Myoelectric Control
TL;DR: The proposed method is demonstrated to be a promising solution for the automatic and adaptive calibration of electrode array shifts, which will enhance the robustness of myoelectric control systems.
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A multi-scale data fusion framework for bone age assessment with convolutional neural networks.
TL;DR: A multi-scale data fusion framework for bone age assessment with X-ray images based on non-subsampled contourlet transform (NSCT) and convolutional neural networks (CNNs) with obvious advantages over the corresponding spatial domain approaches is proposed.
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An IC-PLS Framework for Group Corticomuscular Coupling Analysis
TL;DR: This paper proposes assessing corticomuscular coupling by combining partial least squares (PLS) and independent component analysis (ICA), which addresses many of the limitations of MSC, and applies the proposed framework to concurrent EEG and EMG data collected in a Parkinson's disease study.
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Improved High-Density Myoelectric Pattern Recognition Control Against Electrode Shift Using Data Augmentation and Dilated Convolutional Neural Network
TL;DR: This work demonstrated feasibility and usability of combining data augmentation and DCNN in predicting myoelectric patterns in the context of electrode shifts, and it outperformed other common methods.