Shuting Sun

1.8k total citations
42 papers, 1.1k citations indexed

About

Shuting Sun is a scholar working on Cognitive Neuroscience, Experimental and Cognitive Psychology and Molecular Biology. According to data from OpenAlex, Shuting Sun has authored 42 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Cognitive Neuroscience, 10 papers in Experimental and Cognitive Psychology and 7 papers in Molecular Biology. Recurrent topics in Shuting Sun's work include Functional Brain Connectivity Studies (23 papers), EEG and Brain-Computer Interfaces (22 papers) and Neural dynamics and brain function (12 papers). Shuting Sun is often cited by papers focused on Functional Brain Connectivity Studies (23 papers), EEG and Brain-Computer Interfaces (22 papers) and Neural dynamics and brain function (12 papers). Shuting Sun collaborates with scholars based in China, United Kingdom and Germany. Shuting Sun's co-authors include Xiaowei Li, Bin Hu, Jing Zhu, Hanshu Cai, Martyn Ratcliffe, Zihan Wang, Bin Hu, Jianxiu Li, Chen Xia and Ying Wang and has published in prestigious journals such as Green Chemistry, IEEE Access and Tetrahedron.

In The Last Decade

Shuting Sun

39 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shuting Sun China 16 737 340 206 88 83 42 1.1k
Zhongxiang Dai Singapore 11 368 0.5× 132 0.4× 108 0.5× 44 0.5× 37 0.4× 22 590
Kenneth J. Pope Australia 22 1.2k 1.6× 117 0.3× 177 0.9× 75 0.9× 17 0.2× 67 1.8k
Masahiro Takamura Japan 20 582 0.8× 315 0.9× 69 0.3× 28 0.3× 196 2.4× 54 1.2k
Erika Abe Japan 7 166 0.2× 159 0.5× 179 0.9× 17 0.2× 12 0.1× 12 418
Eric R. Samuels United States 12 456 0.6× 137 0.4× 108 0.5× 4 0.0× 65 0.8× 22 1.2k
J.M. Segall United States 11 637 0.9× 250 0.7× 24 0.1× 29 0.3× 33 0.4× 20 1.1k
Zhaoxin Wang China 20 346 0.5× 110 0.3× 34 0.2× 32 0.4× 48 0.6× 70 1.2k
Lisa E. Williams United States 21 576 0.8× 274 0.8× 24 0.1× 14 0.2× 61 0.7× 34 1.3k
Maie Bachmann Estonia 19 615 0.8× 202 0.6× 245 1.2× 45 0.5× 8 0.1× 53 1.1k
Jorge Bernal Mexico 17 690 0.9× 97 0.3× 55 0.3× 23 0.3× 21 0.3× 28 837

Countries citing papers authored by Shuting Sun

Since Specialization
Citations

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

Fields of papers citing papers by Shuting Sun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shuting Sun

This figure shows the co-authorship network connecting the top 25 collaborators of Shuting Sun. A scholar is included among the top collaborators of Shuting Sun 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 Shuting Sun. Shuting Sun 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.
Wang, Su, et al.. (2025). MDH-NAS: Accelerating EEG Signal Classification With Mixed-Level Differentiable and Hardware-Aware Neural Architecture Search. IEEE Internet of Things Journal. 12(13). 23866–23878. 4 indexed citations
2.
He, Huanhuan, et al.. (2025). VAE-CapsNet: A common emotion information extractor for cross-subject emotion recognition. Knowledge-Based Systems. 311. 113018–113018. 4 indexed citations
3.
Liu, Xuesong, et al.. (2025). A generalized depression recognition framework based on cross-center and cross-task EEG signals. Biomedical Signal Processing and Control. 104. 107492–107492. 1 indexed citations
4.
Luo, Gang, Huayu Chen, Xuesong Liu, et al.. (2024). Depression recognition using high-order generalized multilayer brain functional network fused with EEG multi-domain information. Information Fusion. 114. 102723–102723. 3 indexed citations
5.
Luo, Gang, Shuting Sun, Xuesong Liu, et al.. (2024). IMGWOFS: A Feature Selector With Trade-Off Between Conflict Objectives for EEG-Based Emotion Recognition. IEEE Transactions on Affective Computing. 16(2). 598–610. 3 indexed citations
6.
Luo, Gang, Xuesong Liu, Xiping Hu, et al.. (2024). Stable construction and analysis of MDD modular networks based on multi-center EEG data. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 136. 111149–111149. 1 indexed citations
7.
Sun, Shuting, Shanshan Qu, Gang Luo, et al.. (2024). Resting-state dynamic functional connectivity in major depressive disorder: A systematic review. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 135. 111076–111076. 19 indexed citations
8.
Sun, Shuting, Mei Liao, Yan Zhang, et al.. (2023). Brain function changes reveal rapid antidepressant effects of nitrous oxide for treatment-resistant depression:Evidence from task-state EEG. Psychiatry Research. 322. 115072–115072. 9 indexed citations
9.
Luo, Gang, Shuting Sun, Kun Qian, et al.. (2023). How does Music Affect Your Brain? A Pilot Study on EEG and Music Features for Automatic Analysis. mediaTUM (Technical University of Munich). 1–4. 1 indexed citations
10.
Sun, Shuting, et al.. (2023). Clustering-Fusion Feature Selection Method in Identifying Major Depressive Disorder Based on Resting State EEG Signals. IEEE Journal of Biomedical and Health Informatics. 27(7). 3152–3163. 13 indexed citations
11.
12.
Sun, Shuting, et al.. (2022). EEG Based Depression Recognition by Employing Static and Dynamic Network Metrics. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 1740–1744. 1 indexed citations
13.
Sun, Shuting, et al.. (2021). Analysis of Functional Brain Network in MDD Based on Improved Empirical Mode Decomposition With Resting State EEG Data. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 29. 1546–1556. 36 indexed citations
14.
Zhu, Jing, Zihan Wang, Tao Gong, et al.. (2020). An Improved Classification Model for Depression Detection Using EEG and Eye Tracking Data. IEEE Transactions on NanoBioscience. 19(3). 527–537. 59 indexed citations
15.
Li, Xiaowei, et al.. (2019). EEG-based mild depression recognition using convolutional neural network. Medical & Biological Engineering & Computing. 57(6). 1341–1352. 92 indexed citations
16.
Sun, Shuting, Xiaowei Li, Jing Zhu, et al.. (2019). Graph Theory Analysis of Functional Connectivity in Major Depression Disorder With High-Density Resting State EEG Data. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 27(3). 429–439. 125 indexed citations
17.
Li, Xiaowei, Bin Hu, Shuting Sun, & Hanshu Cai. (2016). EEG-based mild depressive detection using feature selection methods and classifiers. Computer Methods and Programs in Biomedicine. 136. 151–161. 136 indexed citations
18.
19.
Sun, Shuting, Dan Wu, Qin Wei, et al.. (2009). Study and Application of the Interaction Between Asymmetrical Porphyrin and Ascorbic Acid. Journal of Fluorescence. 19(5). 809–815. 4 indexed citations
20.
Yan, Mei, et al.. (2008). [Study on binding mechanism of meso-tetra-(3,5-dibromo-4-hydroxyphenyl) porphyrin with protein by fluorescence method].. PubMed. 28(6). 1322–6. 2 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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