X
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.
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Remote Photoplethysmography With an EEMD-MCCA Method Robust Against Spatially Uneven Illuminations
TL;DR: A novel approach robust against spatially uneven illumination interference in rPPG is introduced by combining ensemble empirical mode decomposition (EEMD) with multiset canonical correlation analysis (MCCA).
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Muscle Force Estimation Based on Neural Drive Information From Individual Motor Units
TL;DR: A novel method is proposed to process individual motor unit (MU) activities derived from the decomposition of high density SEMG (HD-SEMG), and it is applied to muscle force estimation with improved precision, demonstrating its effectiveness.
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Complex network analysis of brain functional connectivity under a multi-step cognitive task
TL;DR: The empirical results suggest that the brain organization has the generic properties of small-worldness and scale-free characteristics, and its diverse functional connectivity emerging from activated ROIs is strongly driven by these behavioral activities via the plasticity of brain.
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High-Density Surface EMG Denoising Using Independent Vector Analysis
TL;DR: A novel method to remove PLI and WGN was proposed based on independent vector analysis (IVA), taking advantage of both ICA and CCA, which is a promising tool for denoising HD-sEMG signals while leading to a minimal distortion.
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A SEMG-Force Estimation Framework Based on a Fast Orthogonal Search Method Coupled with Factorization Algorithms.
TL;DR: The results demonstrated that, compared to the conventional AVG-ENVLP method, factorization algorithms could substantially improve the performance of force estimation and provides an effective way to estimate the combined force of multiple muscles.