Shugeng Chen

933 total citations
35 papers, 593 citations indexed

About

Shugeng Chen is a scholar working on Cognitive Neuroscience, Rehabilitation and Cellular and Molecular Neuroscience. According to data from OpenAlex, Shugeng Chen has authored 35 papers receiving a total of 593 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Cognitive Neuroscience, 16 papers in Rehabilitation and 11 papers in Cellular and Molecular Neuroscience. Recurrent topics in Shugeng Chen's work include EEG and Brain-Computer Interfaces (22 papers), Stroke Rehabilitation and Recovery (16 papers) and Neuroscience and Neural Engineering (11 papers). Shugeng Chen is often cited by papers focused on EEG and Brain-Computer Interfaces (22 papers), Stroke Rehabilitation and Recovery (16 papers) and Neuroscience and Neural Engineering (11 papers). Shugeng Chen collaborates with scholars based in China, United States and United Kingdom. Shugeng Chen's co-authors include Jie Jia, Xiaokang Shu, Hewei Wang, Li Ding, Xiangyang Zhu, Lin Yao, Xinjun Sheng, Ning Jiang, Peter B. Shull and Xinyu Song and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Neurophysiology and IEEE Transactions on Biomedical Engineering.

In The Last Decade

Shugeng Chen

33 papers receiving 583 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shugeng Chen China 16 385 203 184 182 110 35 593
Hewei Wang China 14 191 0.5× 166 0.8× 100 0.5× 59 0.3× 123 1.1× 43 449
Andrew James Thomas Stevenson Denmark 11 319 0.8× 72 0.4× 236 1.3× 155 0.9× 111 1.0× 24 506
О. А. Мокиенко Russia 13 480 1.2× 155 0.8× 213 1.2× 204 1.1× 66 0.6× 40 617
Andrea Biasiucci Switzerland 7 671 1.7× 112 0.6× 224 1.2× 297 1.6× 80 0.7× 13 822
Manuel Agostini Germany 3 653 1.7× 173 0.9× 291 1.6× 331 1.8× 81 0.7× 3 723
Andrej M. Savić Serbia 14 273 0.7× 91 0.4× 121 0.7× 111 0.6× 32 0.3× 52 511
N.S. Stoykov United States 12 222 0.6× 100 0.5× 488 2.7× 205 1.1× 20 0.2× 20 692
Nebojša Malešević Sweden 15 343 0.9× 151 0.7× 628 3.4× 309 1.7× 61 0.6× 49 732
Gangadhar Garipelli Switzerland 8 430 1.1× 168 0.8× 161 0.9× 195 1.1× 51 0.5× 16 606
Eliana García‐Cossio Germany 9 967 2.5× 289 1.4× 447 2.4× 438 2.4× 178 1.6× 11 1.1k

Countries citing papers authored by Shugeng Chen

Since Specialization
Citations

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

Fields of papers citing papers by Shugeng Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shugeng Chen

This figure shows the co-authorship network connecting the top 25 collaborators of Shugeng Chen. A scholar is included among the top collaborators of Shugeng Chen 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 Shugeng Chen. Shugeng Chen 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.
Bai, Long, Shugeng Chen, Peng Wang, et al.. (2025). DeepSeek or ChatGPT: Can brain‐computer interfaces/brain‐inspired computing achieve leapfrog development with large AI models?. VU Research Portal. 3(1). 1 indexed citations
2.
Yin, Baobing, Ying Zhu, Hongdao Meng, et al.. (2025). Efficacy of a Digital Postoperative Rehabilitation Intervention in Patients With Primary Liver Cancer: Randomized Controlled Trial. JMIR mhealth and uhealth. 13. e59228–e59228. 1 indexed citations
3.
Ding, Ling, et al.. (2025). Resting-state EEG associated with clinical measures to predict upper limb motor recovery of subacute stroke. Frontiers in Neurology. 16. 1577393–1577393.
4.
Zhou, Jing, Fubing Zha, Mingchao Zhou, et al.. (2024). Comparative analysis of sleep parameters and structures derived from wearable flexible electrode sleep patches and polysomnography in young adults. Journal of Neurophysiology. 131(4). 738–749. 2 indexed citations
5.
Shu, Xiaokang, et al.. (2023). Correlation between the ERD in grasp/open tasks of BCIs and hand function of stroke patients: a cross-sectional study. BioMedical Engineering OnLine. 22(1). 36–36. 3 indexed citations
6.
Chen, Shugeng, et al.. (2023). Functional-oriented, portable brain–computer interface training for hand motor recovery after stroke: a randomized controlled study. Frontiers in Neuroscience. 17. 1146146–1146146. 12 indexed citations
7.
Chen, Shugeng, et al.. (2023). Prediction of the hand function part of the Fugl‐Meyer scale after stroke using an automatic quantitative assessment system. SHILAP Revista de lepidopterología. 1(3). 1 indexed citations
8.
9.
Song, Xinyu, et al.. (2022). Proposal of a Wearable Multimodal Sensing-Based Serious Games Approach for Hand Movement Training After Stroke. Frontiers in Physiology. 13. 811950–811950. 25 indexed citations
10.
11.
Cao, Lei, et al.. (2021). An Inter- and Intra-Subject Transfer Calibration Scheme for Improving Feedback Performance of Sensorimotor Rhythm-Based BCI Rehabilitation. Frontiers in Neuroscience. 14. 629572–629572. 13 indexed citations
12.
Li, Chong, Xinyu Song, Shugeng Chen, et al.. (2021). Long-term Effectiveness and Adoption of a Cellphone Augmented Reality System on Patients with Stroke: Randomized Controlled Trial. JMIR Serious Games. 9(4). e30184–e30184. 18 indexed citations
13.
Chen, Shugeng, Lei Cao, Xiaokang Shu, et al.. (2020). Longitudinal Electroencephalography Analysis in Subacute Stroke Patients During Intervention of Brain–Computer Interface With Exoskeleton Feedback. Frontiers in Neuroscience. 14. 809–809. 41 indexed citations
14.
Ding, Li, Jiayuan He, Lin Yao, et al.. (2020). Mirror Visual Feedback Combining Vibrotactile Stimulation Promotes Embodiment Perception: An Electroencephalogram (EEG) Pilot Study. Frontiers in Bioengineering and Biotechnology. 8. 553270–553270. 17 indexed citations
15.
Chen, Shugeng, Yang Li, Xiaokang Shu, et al.. (2020). Electroencephalography Mu Rhythm Changes and Decreased Spasticity After Repetitive Peripheral Magnetic Stimulation in Patients Following Stroke. Frontiers in Neurology. 11. 546599–546599. 29 indexed citations
16.
17.
18.
Song, Xinyu, Shugeng Chen, Jie Jia, & Peter B. Shull. (2019). Cellphone-Based Automated Fugl-Meyer Assessment to Evaluate Upper Extremity Motor Function After Stroke. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 27(10). 2186–2195. 26 indexed citations
19.
Shu, Xiaokang, Shugeng Chen, Guohong Chai, et al.. (2018). Neural Modulation By Repetitive Transcranial Magnetic Stimulation (rTMS) for BCI Enhancement in Stroke Patients. PubMed. 2018. 2272–2275. 11 indexed citations
20.
Shu, Xiaokang, Shugeng Chen, Jianjun Meng, et al.. (2018). Tactile Stimulation Improves Sensorimotor Rhythm-Based BCI Performance in Stroke Patients. IEEE Transactions on Biomedical Engineering. 66(7). 1987–1995. 41 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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