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Zhong-Ke Gao

Zhong-Ke Gao

D-Index & Metrics

Engineering and Technology

D-Index
48
Citations
6872
World Ranking
4689
National Ranking
921

Overview

Zhong-Ke Gao is affiliated with Tianjin University in China and has a substantial body of research primarily focused on engineering, neuroscience, and computer science. Their work navigates several subfields, including cognitive neuroscience, biomedical engineering, artificial intelligence, ocean engineering, and experimental and cognitive psychology. These areas reflect a multidisciplinary approach engaging with both theoretical and applied sciences.

Their research topics emphasize EEG and brain-computer interfaces, fluid dynamics and mixing, neural dynamics and brain function, advanced memory and neural computing, neuroscience and neural engineering, gaze tracking and assistive technology, and functional brain connectivity studies. This wide-ranging scope highlights an integration of computational methods with neurophysiological data to address complex scientific questions.

Zhong-Ke Gao contributes frequently to journals that focus on instrumentation, nonlinear science, sensors technology, biomedical informatics, and statistical mechanics. Notable venues where their publications appear include:

  • IEEE Transactions on Instrumentation and Measurement
  • Chaos An Interdisciplinary Journal of Nonlinear Science
  • IEEE Sensors Journal
  • IEEE Journal of Biomedical and Health Informatics
  • Physica A Statistical Mechanics and its Applications

Collaboration is a significant aspect of Gao's research environment. Frequent coauthors include Weidong Dang, Chao Ma, Xinlin Sun, Mengyu Li, and Dongmei Lv, each contributing to multiple publications with Gao. These partnerships have helped expand the breadth and depth of their research output.

Selected recent papers authored by Zhong-Ke Gao showcase their focus on EEG signal analysis and neural network applications in brain-computer interfaces and emotion recognition:

  • Complex networks and deep learning for EEG signal analysis, 2020, Cognitive Neurodynamics
  • A Channel-Fused Dense Convolutional Network for EEG-Based Emotion Recognition, 2020, IEEE Transactions on Cognitive and Developmental Systems
  • Classification of EEG Signals on VEP-Based BCI Systems With Broad Learning, 2020, IEEE Transactions on Systems Man and Cybernetics Systems

Other recent influential publications linked to their domain of study, although credited to some coauthors, include:

  • Dynamic Joint Domain Adaptation Network for Motor Imagery Classification, 2021, IEEE Transactions on Neural Systems and Rehabilitation Engineering
  • A Transformer based neural network for emotion recognition and visualizations of crucial EEG channels, 2022, Physica A Statistical Mechanics and its Applications

Best Publications

  • EEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation

    Zhongke Gao;Xinmin Wang;Yuxuan Yang;Chaoxu Mu

  • Complex network analysis of time series

    Zhong Ke Gao;Michael Small;Jürgen Kurths;Jürgen Kurths;Jürgen Kurths

  • Complex network from time series based on phase space reconstruction

    Zhongke Gao;Ningde Jin

  • Flow-pattern identification and nonlinear dynamics of gas-liquid two-phase flow in complex networks.

    Zhongke Gao;Ningde Jin

  • Multivariate weighted complex network analysis for characterizing nonlinear dynamic behavior in two-phase flow

    Zhong-Ke Gao;Peng-Cheng Fang;Mei-Shuang Ding;Ning-De Jin

  • Complex networks and deep learning for EEG signal analysis

    Zhongke Gao;Weidong Dang;Xinmin Wang;Xiaolin Hong

  • A novel convolutional neural network framework based solar irradiance prediction method

    Unknown

  • Visibility Graph from Adaptive Optimal Kernel Time-Frequency Representation for Classification of Epileptiform EEG.

    Zhong-Ke Gao;Qing Cai;Yu-Xuan Yang;Na Dong

  • Multiscale limited penetrable horizontal visibility graph for analyzing nonlinear time series

    Zhong-Ke Gao;Qing Cai;Yu-Xuan Yang;Wei-Dong Dang

  • A Channel-fused Dense Convolutional Network for EEG-based Emotion Recognition

    Zhongke Gao;Xinmin Wang;Yuxuan Yang;Yanli Li

  • A directed weighted complex network for characterizing chaotic dynamics from time series

    Zhong-Ke Gao;Ning-De Jin

  • Flow pattern and water holdup measurements of vertical upward oil–water two-phase flow in small diameter pipes

    Meng Du;Ning-De Jin;Zhong-Ke Gao;Zhen-Ya Wang

  • Multi-frequency complex network from time series for uncovering oil-water flow structure

    Zhong-Ke Gao;Yu-Xuan Yang;Peng-Cheng Fang;Ning-De Jin

  • Multiscale complex network for analyzing experimental multivariate time series

    Zhong-Ke Gao;Yu-Xuan Yang;Peng-Cheng Fang;Yong Zou

  • Dynamic Joint Domain Adaptation Network for Motor Imagery Classification

    Xiaolin Hong;Qingqing Zheng;Luyan Liu;Peiyin Chen

  • A Four-Sector Conductance Method for Measuring and Characterizing Low-Velocity Oil–Water Two-Phase Flows

    Zhongke Gao;Yuxuan Yang;Lusheng Zhai;Ningde Jin

  • Recurrence networks from multivariate signals for uncovering dynamic transitions of horizontal oil-water stratified flows

    Zhong-Ke Gao;Zhong-Ke Gao;Zhong-Ke Gao;Xin-Wang Zhang;Ning-De Jin;Reik V. Donner

  • Motif distributions in phase-space networks for characterizing experimental two-phase flow patterns with chaotic features.

    Zhong Ke Gao;Zhong Ke Gao;Ning De Jin;Wen Xu Wang;Ying-Cheng Lai

  • A Complex Network-Based Broad Learning System for Detecting Driver Fatigue From EEG Signals

    Yuxuan Yang;Zhongke Gao;Yanli Li;Qing Cai

  • A Novel Multiplex Network-Based Sensor Information Fusion Model and Its Application to Industrial Multiphase Flow System

    Zhongke Gao;Weidong Dang;Chaoxu Mu;Yuxuan Yang

  • Nonlinear dynamic analysis of large diameter inclined oil–water two phase flow pattern

    Yan-Bo Zong;Ning-De Jin;Zhen-Ya Wang;Zhong-Ke Gao

  • Multivariate recurrence network analysis for characterizing horizontal oil-water two-phase flow.

    Zhong-Ke Gao;Xin-Wang Zhang;Ning-De Jin;Norbert Marwan

  • Spatial prisoner's dilemma games with increasing neighborhood size and individual diversity on two interdependent lattices

    Xiao-Kun Meng;Cheng-Yi Xia;Zhong-Ke Gao;Li Wang

Frequent Co-Authors

Celso Grebogi
Celso Grebogi University of Aberdeen
Guanrong Chen
Guanrong Chen City University of Hong Kong
Chengyi Xia
Chengyi Xia Tianjin Polytechnic University
Jürgen Kurths
Jürgen Kurths Potsdam Institute for Climate Impact Research
Changyin Sun
Changyin Sun Southeast University
Xiong Yang
Xiong Yang Tianjin University
Ying-Cheng Lai
Ying-Cheng Lai Arizona State University
Pan Hui
Pan Hui Hong Kong University of Science and Technology
Michael Small
Michael Small University of Western Australia
Hsiao-Dong Chiang
Hsiao-Dong Chiang Cornell University

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