Yifan Xu
- Cognitive Neuroscience top 5%
- EEG and Brain-Computer Interfaces 7
- Human-Computer Interaction top 5%
- Gaze Tracking and Assistive Technology 3
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- Emotion and Mood Recognition 3
- Artificial Intelligence top 5%
- Topic Modeling 4
- Text and Document Classification Technologies 3
- Machine Learning and Algorithms 2
- Signal Processing top 10%
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- Neuroscience and Neural Engineering 3
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- Face and Expression Recognition 3
Yifan Xu
38 papers receiving 735 citations
Hit Papers
Peers
Comparison fields: 5 of 101
- Cognitive Neuroscience 342
- Human-Computer Interaction 65
- Experimental and Cognitive Psychology 109
- Artificial Intelligence 207
- Signal Processing 61
Countries citing papers authored by Yifan Xu
This map shows the geographic impact of Yifan Xu'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 Yifan Xu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yifan Xu more than expected).
Fields of papers citing papers by Yifan Xu
This network shows the impact of papers produced by Yifan Xu. 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 Yifan Xu. The network helps show where Yifan Xu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Yifan Xu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 8 | |
| 2 | 2024 | 4 | |
| 3 | 2024 | 4 | |
| 4 | 2024 | 17 | |
| 5 | 2024 | 41 | |
| 6 | 2024 | 3 | |
| 7 | 2023 | 6 | |
| 8 | 2023 | 6 | |
| 9 | 2023 | 1 | |
| 10 | 2023 | 0 | |
| 11 | 2023 | 8 | |
| 12 | 2023 | 0 | |
| 13 | 2023 | 1 | |
| 14 | 2023 | 0 | |
| 15 | 2023 | 45 | |
| 16 | 2022 | 6 | |
| 17 | Attentional Constellation Nets for Few-Shot Learning. | 2020 | 22 |
| 18 | 2020 | 8 | |
| 19 | 2020 | 5 | |
| 20 | 2019 | 2 |
About Yifan Xu
Yifan Xu is a scholar working on Human-Computer Interaction, Cognitive Neuroscience, Experimental and Cognitive Psychology, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 42 papers that have together received 748 indexed citations. Recurring topics across this work include EEG and Brain-Computer Interfaces (7 papers), Topic Modeling (4 papers), Neuroscience and Neural Engineering (3 papers), Emotion and Mood Recognition (3 papers), Face and Expression Recognition (3 papers), Gaze Tracking and Assistive Technology (3 papers), Text and Document Classification Technologies (3 papers) and Machine Learning and Algorithms (2 papers). The work is most often cited by research in Cognitive Neuroscience (342 citations), Human-Computer Interaction (65 citations), Experimental and Cognitive Psychology (109 citations), Artificial Intelligence (207 citations) and Signal Processing (61 citations). Yifan Xu has collaborated with scholars based in China, United States and Germany. Frequent co-authors include Dongrui Wu, Bao‐Liang Lu, Yuqi Cui, Zhuowen Tu, Ruimin Peng, Xin Luna Dong, Guoqing Huang, Jie Pan, Tian Li and Qingshan Yang. Their work appears in journals such as Nature Neuroscience, IEEE Transactions on Neural Systems and Rehabilitation Engineering, IEEE Transactions on Affective Computing, Proceedings of the VLDB Endowment and IEEE Transactions on Cognitive and Developmental Systems.
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.