Boxun Fu
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
-
- EEG and Brain-Computer Interfaces 10
- Neural dynamics and brain function 2
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- Blind Source Separation Techniques 6
- Co-authors
- Guangming Shi (10 shared papers)Fu Li (10 shared papers)Yi Niu (5 shared papers)Wenming Zheng (2 shared papers)Hao Wu (3 shared papers)Yuchen Li (1 shared paper)Minghao Dong (1 shared paper)Yang Li (7 shared papers)
- Journals
- Journal of Neural Engineering (3 papers)IEEE Transactions on Instrumentation and Measurement (2 papers)IEEE Sensors Journal (1 paper)IEEE Transactions on Affective Computing (1 paper)Frontiers in Neuroscience (1 paper)
- Partner nations
- China
In The Last Decade
Boxun Fu
11 papers receiving 421 citations
Boxun Fu's Hit Papers
Peers
Comparison fields: 5 of 36
- Cognitive Neuroscience 370
- Human-Computer Interaction 92
- Experimental and Cognitive Psychology 188
- Signal Processing 60
- Cellular and Molecular Neuroscience 79
Countries citing papers authored by Boxun Fu
This map shows the geographic impact of Boxun Fu'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 Boxun Fu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Boxun Fu more than expected).
Fields of papers citing papers by Boxun Fu
This network shows the impact of papers produced by Boxun Fu. 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 Boxun Fu. The network helps show where Boxun Fu may publish in the future.
Co-authors
The 16 scholars most cited alongside Boxun Fu, 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 | 2019 | 137 | |
| 2 | GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition Hit paper breakdown → | 2022 | 122 |
| 3 | 2021 | 90 | |
| 4 | 2021 | 20 | |
| 5 | 2020 | 17 | |
| 6 | 2023 | 12 | |
| 7 | 2022 | 8 | |
| 8 | 2024 | 8 | |
| 9 | 2022 | 6 | |
| 10 | 2019 | 5 | |
| 11 | 2024 | 2 |
About Boxun Fu
Boxun Fu is a scholar working on Cognitive Neuroscience, Signal Processing, Experimental and Cognitive Psychology, Artificial Intelligence and Electrical and Electronic Engineering, having authored 11 papers that have together received 427 indexed citations. Recurring topics across this work include EEG and Brain-Computer Interfaces (10 papers), Blind Source Separation Techniques (6 papers), Emotion and Mood Recognition (4 papers), Neural Networks and Applications (2 papers), Advanced Memory and Neural Computing (2 papers), Neural dynamics and brain function (2 papers), Gaze Tracking and Assistive Technology (1 paper) and Neuroscience and Neural Engineering (1 paper). The work is most often cited by research in Cognitive Neuroscience (370 citations), Human-Computer Interaction (92 citations), Experimental and Cognitive Psychology (188 citations), Signal Processing (60 citations) and Cellular and Molecular Neuroscience (79 citations). Boxun Fu has collaborated with scholars based in China. Frequent co-authors include Guangming Shi, Fu Li, Yi Niu, Wenming Zheng, Hao Wu, Yuchen Li, Minghao Dong, Yang Li, Yang Li and Li Fu. Their work appears in journals such as Journal of Neural Engineering, IEEE Transactions on Instrumentation and Measurement, IEEE Sensors Journal, IEEE Transactions on Affective Computing and Frontiers in Neuroscience.
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.