Sung Chan Jun
- Cognitive Neuroscience top 1%
- EEG and Brain-Computer Interfaces 65
- Neural dynamics and brain function 24
- Functional Brain Connectivity Studies 13
- Human-Computer Interaction top 1%
-
- Neuroscience and Neural Engineering 36
- Neurology top 5%
- Transcranial Magnetic Stimulation Studies 23
- Neurological disorders and treatments 11
- Signal Processing top 5%
- Blind Source Separation Techniques 19
-
- Advanced Memory and Neural Computing 16
- Co-authors
- Minkyu AhnSangtae AhnHohyun ChoMoonyoung KwonHyeon SeoJae Gwan KimThien Huu NguyenMi-Jin Lee
- Partner nations
- South KoreaUnited StatesUnited Kingdom
In The Last Decade
Sung Chan Jun
111 papers receiving 2.8k citations
Peers
Comparison fields: 5 of 135
- Cognitive Neuroscience 1.9k
- Human-Computer Interaction 272
- Cellular and Molecular Neuroscience 765
- Neurology 207
- Signal Processing 243
Countries citing papers authored by Sung Chan Jun
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
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
The 25 scholars most cited alongside Sung Chan Jun, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 0 | |
| 3 | 2023 | 0 | |
| 4 | 2023 | 6 | |
| 5 | 2022 | 13 | |
| 6 | 2021 | 5 | |
| 7 | 2020 | 19 | |
| 8 | 2020 | 24 | |
| 9 | 2019 | 20 | |
| 10 | 2019 | 23 | |
| 11 | 2016 | 183 | |
| 12 | 2016 | 20 | |
| 13 | 2015 | 231 | |
| 14 | 2015 | 37 | |
| 15 | 2014 | 5 | |
| 16 | 2014 | 18 | |
| 17 | 2014 | 11 | |
| 18 | 2013 | 10 | |
| 19 | Feasibility of hybrid BCI using ERD- and SSSEP- BCI | 2012 | 8 |
| 20 | 2006 | 44 |
About Sung Chan Jun
Sung Chan Jun is a scholar working on Cognitive Neuroscience, Neurology and Cellular and Molecular Neuroscience, having authored 123 papers that have together received 2.9k indexed citations. Recurring topics across this work include EEG and Brain-Computer Interfaces (65 papers), Neuroscience and Neural Engineering (36 papers), Neural dynamics and brain function (24 papers), Transcranial Magnetic Stimulation Studies (23 papers), Blind Source Separation Techniques (19 papers), Advanced Memory and Neural Computing (16 papers), Functional Brain Connectivity Studies (13 papers) and Neurological disorders and treatments (11 papers). The work is most often cited by research in Cognitive Neuroscience (1.9k citations), Human-Computer Interaction (272 citations) and Cellular and Molecular Neuroscience (765 citations). Sung Chan Jun has collaborated with scholars based in South Korea, United States and United Kingdom. Frequent 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. Their work appears in journals such as Frontiers in Human Neuroscience, Brain stimulation, Sensors, IEEE Access and Journal of Neural Engineering.
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.