Yunfa Fu

1.2k total citations
91 papers, 779 citations indexed

About

Yunfa Fu is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Biomedical Engineering. According to data from OpenAlex, Yunfa Fu has authored 91 papers receiving a total of 779 indexed citations (citations by other indexed papers that have themselves been cited), including 82 papers in Cognitive Neuroscience, 34 papers in Cellular and Molecular Neuroscience and 20 papers in Biomedical Engineering. Recurrent topics in Yunfa Fu's work include EEG and Brain-Computer Interfaces (76 papers), Neuroscience and Neural Engineering (32 papers) and Functional Brain Connectivity Studies (15 papers). Yunfa Fu is often cited by papers focused on EEG and Brain-Computer Interfaces (76 papers), Neuroscience and Neural Engineering (32 papers) and Functional Brain Connectivity Studies (15 papers). Yunfa Fu collaborates with scholars based in China, Japan and United Kingdom. Yunfa Fu's co-authors include Anmin Gong, Changhao Jiang, Baolei Xu, Zhidong Wang, Lei Su, Gang Shi, Hongyi Li, Qian Qian, Fan Wang and Jianping Liu and has published in prestigious journals such as SHILAP Revista de lepidopterología, Neuroscience and IEEE Access.

In The Last Decade

Yunfa Fu

84 papers receiving 764 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yunfa Fu China 16 598 205 164 114 95 91 779
Denis Delisle-Rodríguez Brazil 16 411 0.7× 163 0.8× 274 1.7× 50 0.4× 95 1.0× 68 712
Han-Jeong Hwang South Korea 16 849 1.4× 329 1.6× 282 1.7× 169 1.5× 160 1.7× 36 1.1k
Sheng Ge China 14 507 0.8× 149 0.7× 88 0.5× 76 0.7× 53 0.6× 69 645
Weibo Yi China 15 576 1.0× 260 1.3× 134 0.8× 41 0.4× 104 1.1× 39 649
Kun Chen China 13 364 0.6× 110 0.5× 114 0.7× 50 0.4× 67 0.7× 60 557
Hongmiao Zhang China 8 375 0.6× 155 0.8× 199 1.2× 39 0.3× 87 0.9× 35 568
Yufeng Ke China 15 596 1.0× 158 0.8× 61 0.4× 46 0.4× 70 0.7× 61 747
Berdakh Abibullaev Kazakhstan 15 623 1.0× 165 0.8× 244 1.5× 115 1.0× 74 0.8× 56 787
Dominic Heger Germany 12 548 0.9× 87 0.4× 234 1.4× 279 2.4× 54 0.6× 27 772
Yunyuan Gao China 17 636 1.1× 95 0.5× 155 0.9× 73 0.6× 101 1.1× 59 898

Countries citing papers authored by Yunfa Fu

Since Specialization
Citations

This map shows the geographic impact of Yunfa 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 Yunfa Fu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yunfa Fu more than expected).

Fields of papers citing papers by Yunfa Fu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yunfa 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 Yunfa Fu. The network helps show where Yunfa Fu may publish in the future.

Co-authorship network of co-authors of Yunfa Fu

This figure shows the co-authorship network connecting the top 25 collaborators of Yunfa Fu. A scholar is included among the top collaborators of Yunfa Fu 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 Yunfa Fu. Yunfa Fu 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.
Fu, Yunfa, Y. Y. Xue, Xiaogang Chen, & Yong Hu. (2025). Brain-computer interface (BCI) in clinical neurorestorative practices. Journal of Neurorestoratology. 13(2). 100188–100188. 1 indexed citations
2.
Shi, Kelvin, et al.. (2025). AADNet: Exploring EEG Spatiotemporal Information for Fast and Accurate Orientation and Timbre Detection of Auditory Attention Based on a Cue-Masked Paradigm. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 33. 1349–1359. 2 indexed citations
3.
Ding, Peng, et al.. (2024). Emotion Classification Based on Transformer and CNN for EEG Spatial–Temporal Feature Learning. Brain Sciences. 14(3). 268–268. 17 indexed citations
4.
Shi, Kelvin, et al.. (2024). Intermediary-guided windowed attention Aggregation network for fine-grained characterization of Major Depressive Disorder fMRI. Biomedical Signal Processing and Control. 100. 107166–107166.
5.
Wang, Fan, et al.. (2024). Brain-computer interface paradigms and neural coding. Frontiers in Neuroscience. 17. 1345961–1345961. 16 indexed citations
6.
Zhang, Zhe, et al.. (2024). A review of ethical considerations for the medical applications of brain-computer interfaces. Cognitive Neurodynamics. 18(6). 3603–3614. 3 indexed citations
7.
Ding, Peng, et al.. (2023). The Effects of VR and TP Visual Cues on Motor Imagery Subjects and Performance. Electronics. 12(11). 2381–2381. 2 indexed citations
8.
Xu, Haotian, et al.. (2023). Online adaptive classification system for brain–computer interface based on error-related potentials and neurofeedback. Biomedical Signal Processing and Control. 82. 104554–104554. 4 indexed citations
9.
Gong, Anmin, et al.. (2023). Cross-Domain Identification of Multisite Major Depressive Disorder Using End-to-End Brain Dynamic Attention Network. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 32. 33–42. 3 indexed citations
10.
Shi, Qi, Anmin Gong, Peng Ding, Fan Wang, & Yunfa Fu. (2023). Neural Mechanisms of Visual–Spatial Judgment Behavior under Visual and Auditory Constraints: Evidence from an Electroencephalograph during Handgun Shooting. Brain Sciences. 13(12). 1702–1702. 1 indexed citations
11.
Wang, Fan, et al.. (2022). Improved Brain–Computer Interface Signal Recognition Algorithm Based on Few-Channel Motor Imagery. Frontiers in Human Neuroscience. 16. 880304–880304. 8 indexed citations
12.
Su, Lei, et al.. (2022). Multi-source joint domain adaptation for cross-subject and cross-session emotion recognition from electroencephalography. Frontiers in Human Neuroscience. 16. 921346–921346. 11 indexed citations
13.
Gu, Fengshou, Anmin Gong, Yi Qu, et al.. (2022). From Expert to Elite? — Research on Top Archer’s EEG Network Topology. Frontiers in Human Neuroscience. 16. 759330–759330. 10 indexed citations
14.
Wu, Fan, et al.. (2021). A New Subject-Specific Discriminative and Multi-Scale Filter Bank Tangent Space Mapping Method for Recognition of Multiclass Motor Imagery. Frontiers in Human Neuroscience. 15. 595723–595723. 13 indexed citations
15.
Li, Siyu, et al.. (2021). Identification of Emotion Using Electroencephalogram by Tunable Q-Factor Wavelet Transform and Binary Gray Wolf Optimization. Frontiers in Computational Neuroscience. 15. 732763–732763. 6 indexed citations
16.
Fu, Yunfa, et al.. (2018). Calculation and Analysis of Microstate Related to Variation in Executed and Imagined Movement of Force of Hand Clenching. Computational Intelligence and Neuroscience. 2018. 1–15. 10 indexed citations
17.
Xiong, Xin, et al.. (2018). Single-Trial Recognition of Imagined Forces and Speeds of Hand Clenching Based on Brain Topography and Brain Network. Brain Topography. 32(2). 240–254. 7 indexed citations
18.
Gong, Anmin, et al.. (2017). Correlation Between Resting-state Electroencephalographic Characteristics and Shooting Performance. Neuroscience. 366. 172–183. 10 indexed citations
19.
Xu, Baolei, et al.. (2014). Phase Information for Classification Between Clench Speed and Clench Force Motor Imagery. SHILAP Revista de lepidopterología. 4 indexed citations
20.

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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