H
Hong Zeng
Researcher at Southeast University
Publications - 52
Citations - 1058
Hong Zeng is an academic researcher from Southeast University. The author has contributed to research in topics: Cluster analysis & Correlation clustering. The author has an hindex of 13, co-authored 52 publications receiving 781 citations. Previous affiliations of Hong Zeng include Hong Kong Baptist University & Nanjing University of Information Science and Technology.
Papers
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Journal ArticleDOI
Index finger motor imagery EEG pattern recognition in BCI applications using dictionary cleaned sparse representation-based classification for healthy people
TL;DR: It is demonstrated that single right handIndex finger MI can be decoded from the sensorimotor rhythms, and the feature patterns of index finger MI and rest state can be well recognized for robotic exoskeleton initiation.
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The Advantage of Low-Delta Electroencephalogram Phase Feature for Reconstructing the Center-Out Reaching Hand Movements
TL;DR: This study examines the extension of a slow-oscillation phase representation of EEG signals to the reconstructing two-dimensional hand movements, with the non-invasive EEG signals for the first time, and demonstrates its potential for continuous fine motion control of neuroprostheses.
Proceedings ArticleDOI
Robotic arm control using hybrid brain-machine interface and augmented reality feedback
TL;DR: A hybrid BMI based system with AR feedback to provide extra feedback information to the user for closed-loop control of objects grasping and the results reveal that the BMI user can benefit from the information provided by AR interface in the grasping task.
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Improving clustering with pairwise constraints: a discriminative approach
TL;DR: This paper puts forward a discriminative learning approach which can incorporate pairwise constraints into the recently proposed two-class maximum margin clustering framework, and proposes a set of pairwise loss functions, which features robust detection and penalization for violating the couplewise constraints.
Journal ArticleDOI
Closed-Loop Phase-Dependent Vibration Stimulation Improves Motor Imagery-Based Brain-Computer Interface Performance.
TL;DR: In this paper, an EEG phase-dependent closed-loop mechanical vibration stimulation method was proposed to improve motor imagery (MI) performance and brain oscillatory patterns under different conditions to verify the effectiveness of this method.