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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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Journal ArticleDOI
Quantitative Assessment of Traumatic Upper-Limb Peripheral Nerve Injuries Using Surface Electromyography.
TL;DR: This study offers a useful tool for PNI assessment and helps to promote extensive clinical applications of surface EMG, which is highly consistent with the clinical assessment decisions for three nerves of all 34 examined arms.
Proceedings ArticleDOI
Convolutional Neural Network with Data Augmentation for Robust Myoelectric Control
TL;DR: A self-designed convolutional neural network combined with data augmentation operation is used to learn muscular activity patterns at an original/baseline position of the electrode array to solve the electrode shift problem of surface electromyogram armband.
Journal ArticleDOI
Interpreting Bottom-Up Decision-Making of CNNs via Hierarchical Inference
TL;DR: Zhang et al. as mentioned in this paper proposed the Concept-harmonized Hierarchical Inference (CHAIN) interpretation scheme, where a network decision-making process from shallow to deep layers is interpreted by the hierarchical backward inference based on visual concepts from high to low semantic levels.
Journal ArticleDOI
A Novel SSA-CCA Framework forMuscle Artifact Removal from Ambulatory EEG
TL;DR: In this paper , the authors proposed a novel scheme that combines singular spectrum analysis (SSA) and canonical correlation analysis (CCA) algorithms for single-channel problems and then extend it to a few-channel case by adding additional combining and dividing operations to channels.
Journal ArticleDOI
Domain Adaptation With Self-Guided Adaptive Sampling Strategy: Feature Alignment for Cross-User Myoelectric Pattern Recognition
TL;DR: This study demonstrates the effectiveness of the proposed UDA framework and offers a novel tool for implementing cross-user myoelectric pattern recognition towards a multi-user and user-independent control.