Shurui Li
Impact in
- Cognitive Neuroscience top 5%
- EEG and Brain-Computer Interfaces
- Neural dynamics and brain function
- Functional Brain Connectivity Studies
- Human-Computer Interaction top 5%
- Gaze Tracking and Assistive Technology
Papers in
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- EEG and Brain-Computer Interfaces 23
- Neural dynamics and brain function 4
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- Advanced Memory and Neural Computing 10
- Co-authors
- Jing Jin (21 shared papers)Andrzej Cichocki (19 shared papers)Ian Daly (9 shared papers)Chang Liu (7 shared papers)Yangyang Miao (5 shared papers)Xingyu Wang (5 shared papers)Xingyu Wang (4 shared papers)Hao Sun (3 shared papers)
In The Last Decade
Shurui Li
46 papers receiving 612 citations
Peers
Comparison fields: 5 of 79
- Cognitive Neuroscience 405
- Human-Computer Interaction 93
- Cellular and Molecular Neuroscience 170
- Signal Processing 81
- Computational Mathematics 2
Countries citing papers authored by Shurui Li
This map shows the geographic impact of Shurui Li'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 Shurui Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shurui Li more than expected).
Fields of papers citing papers by Shurui Li
This network shows the impact of papers produced by Shurui Li. 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 Shurui Li. The network helps show where Shurui Li may publish in the future.
Co-authors
The 25 scholars most cited alongside Shurui Li, 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 50 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 113 | |
| 2 | 2019 | 101 | |
| 3 | 2022 | 69 | |
| 4 | 2021 | 40 | |
| 5 | 2020 | 22 | |
| 6 | 2023 | 21 | |
| 7 | 1988 | 21 | |
| 8 | 2020 | 21 | |
| 9 | 2012 | 18 | |
| 10 | 2021 | 15 | |
| 11 | 2021 | 15 | |
| 12 | 2023 | 14 | |
| 13 | 2021 | 13 | |
| 14 | 2023 | 12 | |
| 15 | 1988 | 12 | |
| 16 | 2020 | 11 | |
| 17 | 2020 | 10 | |
| 18 | 2021 | 9 | |
| 19 | 2024 | 9 | |
| 20 | 2011 | 8 |
About Shurui Li
Shurui Li is a scholar working on Cognitive Neuroscience, Electrical and Electronic Engineering, Mechanical Engineering, Cellular and Molecular Neuroscience and Cardiology and Cardiovascular Medicine, having authored 50 papers that have together received 618 indexed citations. Recurring topics across this work include EEG and Brain-Computer Interfaces (23 papers), Advanced Memory and Neural Computing (10 papers), Neuroscience and Neural Engineering (7 papers), Blind Source Separation Techniques (5 papers), Gaze Tracking and Assistive Technology (5 papers), Additive Manufacturing Materials and Processes (4 papers), Neural dynamics and brain function (4 papers) and Microstructure and Mechanical Properties of Steels (3 papers). The work is most often cited by research in Cognitive Neuroscience (405 citations), Human-Computer Interaction (93 citations), Cellular and Molecular Neuroscience (170 citations), Signal Processing (81 citations) and Computational Mathematics (2 citations). Shurui Li has collaborated with scholars based in China, Poland and Russia. Frequent co-authors include Jing Jin, Andrzej Cichocki, Ian Daly, Chang Liu, Yangyang Miao, Xingyu Wang, Xingyu Wang, Hao Sun, Cili Zuo and David Galton. Their work appears in journals such as IEEE Transactions on Neural Systems and Rehabilitation Engineering, Journal of Neural Engineering, IEEE Transactions on Biomedical Engineering, Nucleic Acids Research and Neural Networks.
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.