Xinchun Cui
Impact in
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- Emotion and Mood Recognition
- Cognitive Neuroscience top 5%
- EEG and Brain-Computer Interfaces
- Functional Brain Connectivity Studies
- Neural dynamics and brain function
Papers in
- Neurology 12
- Brain Tumor Detection and Classification 11
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- Medical Image Segmentation Techniques 3
- Face and Expression Recognition 3
- Co-authors
- Xiangwei Zheng (10 shared papers)Yongqiang Yin (1 shared paper)Bin Hu (1 shared paper)Yuang Zhang (1 shared paper)Yiquan Zhang (3 shared papers)Jie Tian (1 shared paper)Xiaomei Yu (1 shared paper)Xiaoli Liu (1 shared paper)
In The Last Decade
Xinchun Cui
26 papers receiving 574 citations
Xinchun Cui's Hit Papers
Peers
Comparison fields: 5 of 86
- Experimental and Cognitive Psychology 270
- Cognitive Neuroscience 285
- Human-Computer Interaction 71
- Neurology 44
- Signal Processing 52
Countries citing papers authored by Xinchun Cui
This map shows the geographic impact of Xinchun Cui'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 Xinchun Cui with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xinchun Cui more than expected).
Fields of papers citing papers by Xinchun Cui
This network shows the impact of papers produced by Xinchun Cui. 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 Xinchun Cui. The network helps show where Xinchun Cui may publish in the future.
Co-authors
The 25 scholars most cited alongside Xinchun Cui, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM Hit paper breakdown → | 2020 | 342 |
| 2 | 2021 | 40 | |
| 3 | 2018 | 29 | |
| 4 | 2021 | 26 | |
| 5 | 2020 | 24 | |
| 6 | 2019 | 22 | |
| 7 | 2023 | 13 | |
| 8 | 2023 | 12 | |
| 9 | 2022 | 8 | |
| 10 | 2022 | 8 | |
| 11 | 2022 | 7 | |
| 12 | 2020 | 7 | |
| 13 | 2021 | 6 | |
| 14 | 2022 | 6 | |
| 15 | 2024 | 6 | |
| 16 | 2024 | 5 | |
| 17 | 2020 | 4 | |
| 18 | 2024 | 4 | |
| 19 | 2020 | 4 | |
| 20 | 2011 | 2 |
About Xinchun Cui
Xinchun Cui is a scholar working on Neurology, Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Networks and Communications and Information Systems, having authored 31 papers that have together received 583 indexed citations. Recurring topics across this work include Brain Tumor Detection and Classification (11 papers), Dementia and Cognitive Impairment Research (4 papers), AI in cancer detection (4 papers), Medical Image Segmentation Techniques (3 papers), Cryptography and Data Security (3 papers), Face and Expression Recognition (3 papers), User Authentication and Security Systems (3 papers) and Artificial Intelligence in Healthcare (3 papers). The work is most often cited by research in Experimental and Cognitive Psychology (270 citations), Cognitive Neuroscience (285 citations), Human-Computer Interaction (71 citations), Neurology (44 citations) and Signal Processing (52 citations). Xinchun Cui has collaborated with scholars based in China, Hong Kong and Taiwan. Frequent co-authors include Xiangwei Zheng, Yongqiang Yin, Bin Hu, Yuang Zhang, Yiquan Zhang, Jie Tian, Xiaomei Yu, Xiaoli Liu, Jin‐Xing Liu and Chao Zhao. Their work appears in journals such as IEEE Journal of Biomedical and Health Informatics, International Journal of Intelligent Systems, Biomedical Signal Processing and Control, Enterprise Information Systems and Applied Soft Computing.
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.