Sung Chan Jun

4.2k total citations
123 papers, 2.9k citations indexed

About

Sung Chan Jun is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Electrical and Electronic Engineering. According to data from OpenAlex, Sung Chan Jun has authored 123 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 84 papers in Cognitive Neuroscience, 36 papers in Cellular and Molecular Neuroscience and 27 papers in Electrical and Electronic Engineering. Recurrent topics in Sung Chan Jun's work include EEG and Brain-Computer Interfaces (65 papers), Neuroscience and Neural Engineering (36 papers) and Neural dynamics and brain function (24 papers). Sung Chan Jun is often cited by papers focused on EEG and Brain-Computer Interfaces (65 papers), Neuroscience and Neural Engineering (36 papers) and Neural dynamics and brain function (24 papers). Sung Chan Jun collaborates with scholars based in South Korea, United States and United Kingdom. Sung Chan Jun's co-authors include Minkyu Ahn, Sangtae Ahn, Hohyun Cho, Moonyoung Kwon, Hyeon Seo, Jae Gwan Kim, Thien Huu Nguyen, Mi-Jin Lee, Donghyeon Kim and Ki Woong Kim and has published in prestigious journals such as PLoS ONE, Journal of Applied Physics and NeuroImage.

In The Last Decade

Sung Chan Jun

111 papers receiving 2.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sung Chan Jun South Korea 26 1.9k 765 556 415 272 123 2.9k
Yangsong Zhang China 32 2.3k 1.2× 650 0.8× 388 0.7× 313 0.8× 261 1.0× 100 3.1k
Maureen Clerc France 21 3.1k 1.6× 843 1.1× 522 0.9× 311 0.7× 327 1.2× 73 3.7k
Marc M. Van Hulle Belgium 35 2.1k 1.1× 671 0.9× 426 0.8× 156 0.4× 216 0.8× 258 4.0k
Chang‐Hwan Im South Korea 37 3.8k 2.0× 1.3k 1.6× 608 1.1× 707 1.7× 613 2.3× 266 5.5k
David Looney United Kingdom 23 1.3k 0.7× 370 0.5× 160 0.3× 385 0.9× 171 0.6× 51 2.1k
Minpeng Xu China 23 1.9k 1.0× 968 1.3× 568 1.0× 265 0.6× 378 1.4× 133 2.1k
Sung-Phil Kim South Korea 22 1.5k 0.8× 633 0.8× 247 0.4× 487 1.2× 175 0.6× 146 2.0k
Dingguo Zhang China 37 2.2k 1.1× 1.6k 2.0× 325 0.6× 2.2k 5.2× 444 1.6× 291 4.7k
Sheng‐Fu Liang Taiwan 27 1.9k 1.0× 547 0.7× 415 0.7× 602 1.5× 377 1.4× 106 3.2k
Damien Coyle United Kingdom 30 2.6k 1.4× 1.1k 1.4× 503 0.9× 312 0.8× 437 1.6× 149 3.2k

Countries citing papers authored by Sung Chan Jun

Since Specialization
Citations

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

Fields of papers citing papers by Sung Chan Jun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sung Chan Jun. 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 Sung Chan Jun. The network helps show where Sung Chan Jun may publish in the future.

Co-authorship network of co-authors of Sung Chan Jun

This figure shows the co-authorship network connecting the top 25 collaborators of Sung Chan Jun. A scholar is included among the top collaborators of Sung Chan Jun 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 Sung Chan Jun. Sung Chan Jun 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.
Kim, Sangyeon, et al.. (2025). Is what I think what you think? Multilayer network-based inter-brain synchrony approach. Social Cognitive and Affective Neuroscience. 20(1).
2.
Ahn, Minkyu, et al.. (2024). Comparison of recognition methods for an asynchronous (un-cued) BCI system: an investigation with 40-class SSVEP dataset. Biomedical Engineering Letters. 14(3). 617–630.
3.
Jun, Sung Chan, et al.. (2023). The impact of brain atrophy on invasive electrical stimulation – A computational study. Brain stimulation. 16(1). 221–221.
4.
Jun, Sung Chan, et al.. (2023). TSANet: multibranch attention deep neural network for classifying tactile selective attention in brain-computer interfaces. Biomedical Engineering Letters. 14(1). 45–55.
5.
Kim, Minhee, et al.. (2023). A comprehensive research setup for monitoring Alzheimer’s disease using EEG, fNIRS, and Gait analysis. Biomedical Engineering Letters. 14(1). 13–21. 6 indexed citations
6.
Park, Jinhee, Jeonghwan Gwak, Byeong C. Kim, et al.. (2022). Individualized diagnosis of preclinical Alzheimer’s Disease using deep neural networks. Expert Systems with Applications. 210. 118511–118511. 13 indexed citations
7.
Kwon, Moonyoung, et al.. (2022). EEG Dataset for RSVP and P300 Speller Brain-Computer Interfaces. Scientific Data. 9(1). 388–388. 35 indexed citations
8.
Seo, Hyeon, et al.. (2022). Key factors in the cortical response to transcranial electrical Stimulations—A multi-scale modeling study. Computers in Biology and Medicine. 144. 105328–105328. 11 indexed citations
9.
Kwon, Moonyoung, et al.. (2021). Selective Subject Pooling Strategy to Improve Model Generalization for a Motor Imagery BCI. Sensors. 21(16). 5436–5436. 5 indexed citations
10.
Kwon, Moonyoung, et al.. (2020). A Systematic Review of Closed-Loop Feedback Techniques in Sleep Studies—Related Issues and Future Directions. Sensors. 20(10). 2770–2770. 24 indexed citations
11.
Kwon, Moonyoung, et al.. (2020). Use of Both Eyes-Open and Eyes-Closed Resting States May Yield a More Robust Predictor of Motor Imagery BCI Performance. Electronics. 9(4). 690–690. 19 indexed citations
12.
Kwon, Moonyoung, et al.. (2019). Super-Resolution for Improving EEG Spatial Resolution using Deep Convolutional Neural Network—Feasibility Study. Sensors. 19(23). 5317–5317. 23 indexed citations
13.
Jun, Sung Chan, et al.. (2019). A Compressive Sensing-Based Automatic Sleep-Stage Classification System With Radial Basis Function Neural Network. IEEE Access. 7. 186499–186509. 20 indexed citations
14.
Ahn, Minkyu, Hohyun Cho, Sangtae Ahn, & Sung Chan Jun. (2018). User’s Self-Prediction of Performance in Motor Imagery Brain–Computer Interface. Frontiers in Human Neuroscience. 12. 59–59. 32 indexed citations
15.
Ahn, Minkyu & Sung Chan Jun. (2015). Performance variation in motor imagery brain–computer interface: A brief review. Journal of Neuroscience Methods. 243. 103–110. 231 indexed citations
16.
Ahn, Su‐Jin, Hun Wi, Tong In Oh, et al.. (2014). Continuous Nondestructive Monitoring Method Using the Reconstructed Three-Dimensional Conductivity Images via GREIT for Tissue Engineering. Journal of Applied Mathematics. 2014. 1–11. 5 indexed citations
17.
Kim, Donghyeon, Hyeon Seo, Hyoung-Ihl Kim, & Sung Chan Jun. (2014). Computational Study on Subdural Cortical Stimulation - The Influence of the Head Geometry, Anisotropic Conductivity, and Electrode Configuration. PLoS ONE. 9(9). e108028–e108028. 18 indexed citations
18.
Ahn, Sangtae & Sung Chan Jun. (2012). Feasibility of hybrid BCI using ERD- and SSSEP- BCI. International Conference on Control, Automation and Systems. 2053–2056. 8 indexed citations
19.
20.
Jun, Sung Chan, et al.. (2006). Limitations to the Development of Humanized Antibody Producing Chinese Hamster Ovary Cells Using Glutamine Synthetase-Mediated Gene Amplification. Biotechnology Progress. 22(3). 770–780. 44 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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