Xiaohong Sui

943 total citations
53 papers, 667 citations indexed

About

Xiaohong Sui is a scholar working on Cellular and Molecular Neuroscience, Cognitive Neuroscience and Biomedical Engineering. According to data from OpenAlex, Xiaohong Sui has authored 53 papers receiving a total of 667 indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Cellular and Molecular Neuroscience, 27 papers in Cognitive Neuroscience and 22 papers in Biomedical Engineering. Recurrent topics in Xiaohong Sui's work include Neuroscience and Neural Engineering (33 papers), EEG and Brain-Computer Interfaces (21 papers) and Muscle activation and electromyography studies (18 papers). Xiaohong Sui is often cited by papers focused on Neuroscience and Neural Engineering (33 papers), EEG and Brain-Computer Interfaces (21 papers) and Muscle activation and electromyography studies (18 papers). Xiaohong Sui collaborates with scholars based in China, Australia and United States. Xiaohong Sui's co-authors include Guohong Chai, Ning Lan, Xinyu Chai, Liming Li, Si Li, Dinesh Kumar, Sridhar P. Arjunan, Beth Jelfs, Dingguo Zhang and Dominique M. Durand and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Journal of Neurochemistry.

In The Last Decade

Xiaohong Sui

44 papers receiving 656 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiaohong Sui China 15 375 334 317 91 71 53 667
Fabien B. Wagner United States 12 449 1.2× 266 0.8× 267 0.8× 69 0.8× 103 1.5× 17 760
Jean‐Louis Divoux France 10 204 0.5× 201 0.6× 137 0.4× 28 0.3× 99 1.4× 18 448
Liming Li China 18 593 1.6× 161 0.5× 351 1.1× 292 3.2× 41 0.6× 61 994
Jose G. Grajales‐Reyes United States 12 322 0.9× 206 0.6× 100 0.3× 81 0.9× 68 1.0× 19 778
Klaus Peter Koch Germany 18 662 1.8× 511 1.5× 441 1.4× 130 1.4× 56 0.8× 59 1.2k
Tomislav Milekovic Switzerland 12 271 0.7× 151 0.5× 331 1.0× 98 1.1× 47 0.7× 16 616
Angelica J. Herrera United States 4 252 0.7× 134 0.4× 298 0.9× 107 1.2× 23 0.3× 6 464
Jordi Badía Spain 10 698 1.9× 532 1.6× 355 1.1× 63 0.7× 102 1.4× 19 814
Antonella Benvenuto Italy 9 309 0.8× 424 1.3× 222 0.7× 99 1.1× 43 0.6× 20 582
Jonas B. Zimmermann United Kingdom 9 276 0.7× 181 0.5× 401 1.3× 74 0.8× 76 1.1× 11 628

Countries citing papers authored by Xiaohong Sui

Since Specialization
Citations

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

Fields of papers citing papers by Xiaohong Sui

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaohong Sui

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaohong Sui. A scholar is included among the top collaborators of Xiaohong Sui 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 Xiaohong Sui. Xiaohong Sui 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.
2.
Zhang, Xiaohua, Yun Zhang, Xin Wang, et al.. (2025). Composite phase change materials by confining polymers inside nanocarbon assemblies: A review. Next Energy. 7. 100281–100281. 3 indexed citations
3.
4.
5.
Shi, Mingxia, et al.. (2023). System-level biological effects of extremely low-frequency electromagnetic fields: an in vivo experimental review. Frontiers in Neuroscience. 17. 1247021–1247021. 17 indexed citations
6.
Wang, Yansong, et al.. (2022). Motion Intention Prediction and Joint Trajectories Generation Toward Lower Limb Prostheses Using EMG and IMU Signals. IEEE Sensors Journal. 22(11). 10719–10729. 35 indexed citations
7.
Chen, Jianping, Liming Li, Yao Chen, et al.. (2021). Computational Modeling of Spatially Selective Retinal Stimulation With Temporally Interfering Electric Fields. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 29. 418–428. 17 indexed citations
8.
Guo, Tianruo, Diansan Su, Dingguo Zhang, et al.. (2020). Mediating different-diameter Aβ nerve fibers using a biomimetic 3D TENS computational model. Journal of Neuroscience Methods. 346. 108891–108891. 5 indexed citations
9.
Yu, Xiao, Jinpeng Su, Xiaohua Zhang, et al.. (2019). Spatiotemporal characteristics of neural activity in tibial nerves with carbon nanotube yarn electrodes. Journal of Neuroscience Methods. 328. 108450–108450. 12 indexed citations
10.
Zhu, Fengyuan, Xin Zhang, Grant A. McCallum, et al.. (2019). Flexural characterization of carbon nanotube (CNT) yarn neural electrodes. Materials Research Express. 6(4). 45402–45402. 10 indexed citations
11.
Sui, Xiaohong, Ze Yang, Fan Wang, et al.. (2019). The relationship between plasma homocysteine levels and MTHFR gene variation, age, and sex in Northeast China. Nigerian Journal of Clinical Practice. 22(3). 380–380. 14 indexed citations
12.
Li, Mengnan, Dingguo Zhang, Yao Chen, et al.. (2018). Discrimination and Recognition of Phantom Finger Sensation Through Transcutaneous Electrical Nerve Stimulation. Frontiers in Neuroscience. 12. 283–283. 36 indexed citations
13.
Li, Heng, et al.. (2017). An optimized content-aware image retargeting method: toward expanding the perceived visual field of the high-density retinal prosthesis recipients. Journal of Neural Engineering. 15(2). 26025–26025. 9 indexed citations
14.
Li, Liming, et al.. (2017). A 3D Computational Model of Transcutaneous Electrical Nerve Stimulation for Estimating Aβ Tactile Nerve Fiber Excitability. Frontiers in Neuroscience. 11. 250–250. 20 indexed citations
15.
McCallum, Grant A., et al.. (2017). Chronic interfacing with the autonomic nervous system using carbon nanotube (CNT) yarn electrodes. Scientific Reports. 7(1). 11723–11723. 80 indexed citations
16.
Yan, Yan, Xiaohong Sui, Wenjia Liu, et al.. (2015). Spatial characteristics of evoked potentials elicited by a MEMS microelectrode array for suprachoroidal-transretinal stimulation in a rabbit. Graefe s Archive for Clinical and Experimental Ophthalmology. 253(9). 1515–1528. 5 indexed citations
17.
Cao, Xun, Xiaohong Sui, Qing Lyu, Liming Li, & Xinyu Chai. (2015). Effects of different three-dimensional electrodes on epiretinal electrical stimulation by modeling analysis. Journal of NeuroEngineering and Rehabilitation. 12(1). 73–73. 17 indexed citations
18.
Jiang, Xia, Xiaohong Sui, Yiliang Lu, et al.. (2013). In vitro and in vivo evaluation of a photosensitive polyimide thin-film microelectrode array suitable for epiretinal stimulation. Journal of NeuroEngineering and Rehabilitation. 10(1). 48–48. 33 indexed citations
19.
Liu, Wenjia, Zhengyu Song, Weijun Wang, et al.. (2012). In vitro Biocompatibility of a Platinum-Electrode Embedded Photosensitive Polyimide (Durimide) Retinal Prosthesis. Current Eye Research. 37(11). 1036–1044. 4 indexed citations
20.
Qiao, Guo‐Fen, Zhifeng Cheng, Rong Huo, et al.. (2008). GM1 ganglioside contributes to retain the neuronal conduction and neuronal excitability in visceral and baroreceptor afferents. Journal of Neurochemistry. 106(4). 1637–1645. 16 indexed citations

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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