Qing Cai

1.7k total citations · 1 hit paper
41 papers, 1.3k citations indexed

About

Qing Cai is a scholar working on Cognitive Neuroscience, Signal Processing and Experimental and Cognitive Psychology. According to data from OpenAlex, Qing Cai has authored 41 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Cognitive Neuroscience, 9 papers in Signal Processing and 8 papers in Experimental and Cognitive Psychology. Recurrent topics in Qing Cai's work include EEG and Brain-Computer Interfaces (14 papers), Neural dynamics and brain function (8 papers) and Time Series Analysis and Forecasting (6 papers). Qing Cai is often cited by papers focused on EEG and Brain-Computer Interfaces (14 papers), Neural dynamics and brain function (8 papers) and Time Series Analysis and Forecasting (6 papers). Qing Cai collaborates with scholars based in China, United States and United Kingdom. Qing Cai's co-authors include Zhongke Gao, Yuxuan Yang, Weidong Dang, Chaoxu Mu, Siyang Zuo, Xinmin Wang, Na Dong, Celso Grebogi, Jürgen Kurths and Norbert Marwan and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and PLoS Computational Biology.

In The Last Decade

Qing Cai

39 papers receiving 1.3k citations

Hit Papers

EEG-Based Spatio–Temporal... 2019 2026 2021 2023 2019 100 200 300

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Qing Cai China 17 641 348 207 180 175 41 1.3k
Weidong Dang China 20 807 1.3× 386 1.1× 220 1.1× 204 1.1× 178 1.0× 47 1.6k
Yuxuan Yang China 23 990 1.5× 575 1.7× 278 1.3× 356 2.0× 316 1.8× 47 2.1k
Mosabber Uddin Ahmed Bangladesh 14 235 0.4× 37 0.1× 202 1.0× 180 1.0× 118 0.7× 32 855
Wenbin Shi China 20 322 0.5× 64 0.2× 158 0.8× 332 1.8× 235 1.3× 82 1.1k
Bilal Fadlallah United States 6 234 0.4× 27 0.1× 97 0.5× 132 0.7× 141 0.8× 13 912
Rajeev Sharma India 18 893 1.4× 131 0.4× 360 1.7× 31 0.2× 14 0.1× 67 1.7k
Xin Zhao China 21 336 0.5× 122 0.4× 38 0.2× 23 0.1× 43 0.2× 126 1.4k
J.M. Pastor Spain 25 196 0.3× 192 0.6× 136 0.7× 19 0.1× 36 0.2× 66 2.1k

Countries citing papers authored by Qing Cai

Since Specialization
Citations

This map shows the geographic impact of Qing Cai's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Qing Cai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qing Cai more than expected).

Fields of papers citing papers by Qing Cai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Qing Cai. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Qing Cai. The network helps show where Qing Cai may publish in the future.

Co-authorship network of co-authors of Qing Cai

This figure shows the co-authorship network connecting the top 25 collaborators of Qing Cai. A scholar is included among the top collaborators of Qing Cai based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Qing Cai. Qing Cai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Qiu, Yu, Rong Liu, Shanshan Huang, et al.. (2025). OTUB2 promotes proliferation and metastasis of triple-negative breast cancer by deubiquitinating TRAF6. Oncology Research Featuring Preclinical and Clinical Cancer Therapeutics. 33(5). 1135–1147. 1 indexed citations
2.
Zhang, Shugang, et al.. (2025). SuperEdgeGO: Edge-supervised graph representation learning for enhanced protein function prediction. PLoS Computational Biology. 21(8). e1013343–e1013343.
3.
Wang, Yong, et al.. (2024). Transformer-Based Weakly Supervised Learning for Whole Slide Lung Cancer Image Classification. IEEE Journal of Biomedical and Health Informatics. 29(12). 9095–9108. 6 indexed citations
4.
Cai, Qing, et al.. (2024). Attention-based cross-frequency graph convolutional network for driver fatigue estimation. Cognitive Neurodynamics. 18(5). 3181–3194.
5.
Cai, Qing, et al.. (2022). Cross-subject emotion recognition using visibility graph and genetic algorithm-based convolution neural network. Chaos An Interdisciplinary Journal of Nonlinear Science. 32(9). 93110–93110. 4 indexed citations
6.
Cai, Qing, et al.. (2021). COVID-19 Screening in Chest X-Ray Images Using Lung Region Priors. IEEE Journal of Biomedical and Health Informatics. 25(11). 4119–4127. 14 indexed citations
7.
Gao, Zhongke, et al.. (2020). A Deep Branch-Aggregation Network for Recognition of Gas–Liquid Two-Phase Flow Structure. IEEE Transactions on Instrumentation and Measurement. 70. 1–8. 17 indexed citations
8.
Gao, Zhongke, et al.. (2020). Complex Network Analysis of Experimental EEG Signals for Decoding Brain Cognitive State. IEEE Transactions on Circuits & Systems II Express Briefs. 68(1). 531–535. 9 indexed citations
9.
Dang, Weidong, Zhongke Gao, Xinlin Sun, et al.. (2020). Multilayer brain network combined with deep convolutional neural network for detecting major depressive disorder. Nonlinear Dynamics. 102(2). 667–677. 30 indexed citations
10.
Cai, Qing, et al.. (2020). A Graph-Temporal Fused Dual-Input Convolutional Neural Network for Detecting Sleep Stages from EEG Signals. IEEE Transactions on Circuits & Systems II Express Briefs. 68(2). 777–781. 56 indexed citations
11.
Gao, Zhongke, Xinmin Wang, Yuxuan Yang, et al.. (2019). EEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation. IEEE Transactions on Neural Networks and Learning Systems. 30(9). 2755–2763. 369 indexed citations breakdown →
12.
Yang, Yuxuan, Zhongke Gao, Yanli Li, et al.. (2019). A Complex Network-Based Broad Learning System for Detecting Driver Fatigue From EEG Signals. IEEE Transactions on Systems Man and Cybernetics Systems. 51(9). 5800–5808. 81 indexed citations
13.
Cai, Qing, Zhongke Gao, Yuxuan Yang, Weidong Dang, & Celso Grebogi. (2018). Multiplex Limited Penetrable Horizontal Visibility Graph from EEG Signals for Driver Fatigue Detection. International Journal of Neural Systems. 29(5). 1850057–1850057. 48 indexed citations
14.
Gao, Zhongke, Shan Li, Qing Cai, et al.. (2018). Relative Wavelet Entropy Complex Network for Improving EEG-Based Fatigue Driving Classification. IEEE Transactions on Instrumentation and Measurement. 68(7). 2491–2497. 72 indexed citations
15.
Gao, Zhongke, et al.. (2016). Complex network inference from P300 signals: Decoding brain state under visual stimulus for able-bodied and disabled subjects. Physica A Statistical Mechanics and its Applications. 460. 294–303. 3 indexed citations
16.
Stephenson, Judith J., William Spalding, Qing Cai, et al.. (2014). Impact Of Irritable Bowel Syndrome With Constipation On Work Productivity And Daily Activity Among Commercially Insured Patients In The United States. Value in Health. 17(7). A370–A370. 5 indexed citations
17.
Lu, Jiakai, et al.. (2014). Matrix metalloproteinase-8 inhibitors mitigate sepsis-induced myocardial injury in rats. Chinese Medical Journal. 127(8). 1530–1535. 6 indexed citations
18.
Sullivan, Sean D., Qing Cai, Michelle Mocarski, et al.. (2013). Patient-Reported Quality Of Life And Dyspnea Among Copd Patients With Nighttime And/Or Early Morning Symptoms. Value in Health. 16(3). A239–A239. 1 indexed citations
20.
Zhou, Yan, Min Xu, Qing Cai, et al.. (2012). Aberrant expression of imprinted genes and their regulatory network in cloned cattle. Theriogenology. 78(4). 858–866. 13 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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