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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.
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Motion Robust Imaging Ballistocardiography Through a Two-Step Canonical Correlation Analysis
TL;DR: A novel method to suppress motion artifacts in iBCG with a two-step canonical correlation analysis (CCA) is proposed, and the target HR value is determined as the one with the highest peak of power spectrums among all canonical variates (CVs).
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Upper Limb End-Effector Force Estimation During Multi-Muscle Isometric Contraction Tasks Using HD-sEMG and Deep Belief Network.
TL;DR: The experimental results demonstrated that, in multi-muscle isometric contraction tasks, the dominant muscles with the highest activation degree could track variations in the end-effector force more effectively, and are more suitable than a combination of muscles.
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EEG-based Emotion Recognition via Transformer Neural Architecture Search
TL;DR: In this article , an automatic transformer neural architectures search (TNAS) framework based on multiobjective evolution algorithm (MOEA) was proposed for the EEG-based emotion recognition, which considers both accuracy and model size to discover the optimal model from well-trained supernet for the emotion recognition.
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Decoding finger movement patterns from microscopic neural drive information based on deep learning.
TL;DR: In this paper , a progressive FastICA peel-off algorithm was first applied to decompose the high-density sEMG data to obtain microscopic neural drives in terms of MU firing sequences and their corresponding action potential waveforms.
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Visualization of boundaries in volumetric data sets through a what material you pick is what boundary you see approach
TL;DR: This paper proposes the human-centric boundary extraction criteria and the boundary model, and presents a boundary visualization method through a what material you pick is what boundary you see approach that is intuitive and flexible for the exploration of volumetric data.