Seong-Eun Moon
- Cognitive Neuroscience top 10%
- EEG and Brain-Computer Interfaces 8
- Neural dynamics and brain function 4
- Visual perception and processing mechanisms 3
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- Emotion and Mood Recognition 3
- Human-Computer Interaction top 10%
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- Image and Video Quality Assessment 3
- Visual Attention and Saliency Detection 2
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- Color perception and design 3
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- Neural Networks and Applications 2
- Co-authors
- Jong‐Seok LeeCho‐Jui HsiehJane-Ling WangSunwook KimJunhyuk KimJooyeon LeeManri CheonEdward Choi
- Journals
- IEEE Transactions on Affective Computing (2 papers)IEEE Journal of Biomedical and Health Informatics (1 paper)Neural Networks (1 paper)
- Partner nations
- South KoreaUnited States
In The Last Decade
Seong-Eun Moon
18 papers receiving 249 citations
Peers
Comparison fields: 5 of 52
- Cognitive Neuroscience 195
- Experimental and Cognitive Psychology 121
- Human-Computer Interaction 46
- Health Informatics 3
- Signal Processing 17
Countries citing papers authored by Seong-Eun Moon
This map shows the geographic impact of Seong-Eun Moon'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 Seong-Eun Moon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Seong-Eun Moon more than expected).
Fields of papers citing papers by Seong-Eun Moon
This network shows the impact of papers produced by Seong-Eun Moon. 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 Seong-Eun Moon. The network helps show where Seong-Eun Moon may publish in the future.
Co-authorship network
The 12 scholars most cited alongside Seong-Eun Moon, 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 | 2023 | 9 | |
| 2 | 2022 | 1 | |
| 3 | 2022 | 2 | |
| 4 | 2021 | 12 | |
| 5 | 2020 | 71 | |
| 6 | 2020 | 2 | |
| 7 | 2019 | 17 | |
| 8 | Learning cross-regional dependence of EEG with convolutional neural networks for emotion classification | 2018 | 1 |
| 9 | 2018 | 88 | |
| 10 | Graph Signal Representation of EEG for Graph Convolutional Neural Network | 2018 | 2 |
| 11 | 2018 | 2 | |
| 12 | 2017 | 2 | |
| 13 | 2016 | 31 | |
| 14 | 2016 | 8 | |
| 15 | 2015 | 1 | |
| 16 | 2015 | 3 | |
| 17 | 2015 | 5 | |
| 18 | 2014 | 1 |
About Seong-Eun Moon
Seong-Eun Moon is a scholar working on Cognitive Neuroscience, Human-Computer Interaction and Experimental and Cognitive Psychology, having authored 18 papers that have together received 258 indexed citations. Recurring topics across this work include EEG and Brain-Computer Interfaces (8 papers), Neural dynamics and brain function (4 papers), Emotion and Mood Recognition (3 papers), Visual perception and processing mechanisms (3 papers), Image and Video Quality Assessment (3 papers), Color perception and design (3 papers), Neural Networks and Applications (2 papers) and Visual Attention and Saliency Detection (2 papers). The work is most often cited by research in Cognitive Neuroscience (195 citations), Experimental and Cognitive Psychology (121 citations) and Human-Computer Interaction (46 citations). Seong-Eun Moon has collaborated with scholars based in South Korea and United States. Frequent co-authors include Jong‐Seok Lee, Cho‐Jui Hsieh, Jane-Ling Wang, Sunwook Kim, Junhyuk Kim, Jooyeon Lee, Manri Cheon, Edward Choi, Young‐Hak Kim and Louis Atallah. Their work appears in journals such as IEEE Transactions on Affective Computing, IEEE Journal of Biomedical and Health Informatics, Neural Networks, IEEE Transactions on Multimedia and PubMed.
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.