Oyeon Kwon

11 papers receiving 823 citations

Oyeon Kwon's Hit Papers

Subject-Independent Brain–Computer Interfaces Based on Deep Convolutional Neural Networks 2019 · 253 citations
2530+2+4Years since publication100200300

Peers

Oyeon Kwon
Comparison fields: 5 of 67
  • Health Informatics 42
  • Cognitive Neuroscience 589
  • Human-Computer Interaction 124
  • Cellular and Molecular Neuroscience 272
  • Signal Processing 103
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Andrey Eliseyev France
Louis Mayaud France
Franz Fürbass Austria
Ruhi Mahajan United States
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Ali Bahrami Rad United States
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Viswam Nathan United States
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Countries citing papers authored by Oyeon Kwon

Since Specialization
Citations

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

Fields of papers citing papers by Oyeon Kwon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Oyeon Kwon, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Oyeon Kwon Line = papers co-authored together Oyeon Kwon links everyone, so they are left out of the graph.

All Works

11 of 11 papers shown
#Work
1
EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy
Hit paper breakdown →
2019381
2
Subject-Independent Brain–Computer Interfaces Based on Deep Convolutional Neural Networks
Hit paper breakdown →
2019253
3 202076
4 202050
5 202132
6 202117
7 202011
8 20226
9 20226
10 20192
11 20191

About Oyeon Kwon

Oyeon Kwon is a scholar working on Epidemiology, Artificial Intelligence, Emergency Medicine, Cardiology and Cardiovascular Medicine and Cellular and Molecular Neuroscience, having authored 11 papers that have together received 835 indexed citations. Recurring topics across this work include Sepsis Diagnosis and Treatment (6 papers), Machine Learning in Healthcare (5 papers), Emergency and Acute Care Studies (3 papers), Neuroscience and Neural Engineering (2 papers), EEG and Brain-Computer Interfaces (2 papers), ECG Monitoring and Analysis (1 paper), Digital Imaging for Blood Diseases (1 paper) and Cardiac Arrhythmias and Treatments (1 paper). The work is most often cited by research in Health Informatics (42 citations), Cognitive Neuroscience (589 citations), Human-Computer Interaction (124 citations), Cellular and Molecular Neuroscience (272 citations) and Signal Processing (103 citations). Oyeon Kwon has collaborated with scholars based in South Korea, Kazakhstan and Singapore. Frequent co-authors include Min-Ho Lee, Seong‐Whan Lee, Cuntai Guan, Young-Eun Lee, Siamac Fazli, John Williamson, Yeha Lee, Hwa Jin Cho, Hyunho Park and Joon‐myoung Kwon. Their work appears in journals such as Critical Care Medicine, Biomedical Journal, Journal of Korean Medical Science, Scandinavian Journal of Trauma Resuscitation and Emergency Medicine and Resuscitation.

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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