Jun Kitazono
Impact in
- Cognitive Neuroscience top 10%
- Neural dynamics and brain function
- Functional Brain Connectivity Studies
- Face Recognition and Perception
- Visual perception and processing mechanisms
- EEG and Brain-Computer Interfaces
Papers in
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- Neural dynamics and brain function 8
- Functional Brain Connectivity Studies 7
- EEG and Brain-Computer Interfaces 3
- Face Recognition and Perception 2
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- Sentiment Analysis and Opinion Mining 3
- Co-authors
- Seiichi Ozawa (9 shared papers)Masato Okada (4 shared papers)Masafumi Oizumi (8 shared papers)Mark D. Lescroart (1 shared paper)Manabu Tanifuji (1 shared paper)Kenji Nagata (2 shared papers)Takayuki Sato (1 shared paper)Toshiaki Omori (4 shared papers)
In The Last Decade
Jun Kitazono
20 papers receiving 229 citations
Peers
Comparison fields: 5 of 66
- Cognitive Neuroscience 94
- Artificial Intelligence 64
- Signal Processing 21
- Computational Mathematics 1
- Computer Networks and Communications 34
Countries citing papers authored by Jun Kitazono
This map shows the geographic impact of Jun Kitazono'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 Jun Kitazono with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Kitazono more than expected).
Fields of papers citing papers by Jun Kitazono
This network shows the impact of papers produced by Jun Kitazono. 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 Jun Kitazono. The network helps show where Jun Kitazono may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun Kitazono, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2013 | 44 | |
| 2 | 2014 | 36 | |
| 3 | 2017 | 31 | |
| 4 | 2014 | 20 | |
| 5 | 2017 | 20 | |
| 6 | 2015 | 19 | |
| 7 | 2014 | 14 | |
| 8 | 2020 | 13 | |
| 9 | 2022 | 9 | |
| 10 | 2015 | 8 | |
| 11 | 2022 | 4 | |
| 12 | 2016 | 4 | |
| 13 | 2021 | 3 | |
| 14 | 2012 | 3 | |
| 15 | 2025 | 2 | |
| 16 | 2016 | 2 | |
| 17 | 2016 | 2 | |
| 18 | 2009 | 2 | |
| 19 | 2025 | 1 | |
| 20 | 2017 | 1 |
About Jun Kitazono
Jun Kitazono is a scholar working on Cognitive Neuroscience, Artificial Intelligence, Computer Networks and Communications, Information Systems and Computer Vision and Pattern Recognition, having authored 22 papers that have together received 238 indexed citations. Recurring topics across this work include Neural dynamics and brain function (8 papers), Functional Brain Connectivity Studies (7 papers), Network Security and Intrusion Detection (3 papers), Spam and Phishing Detection (3 papers), Sentiment Analysis and Opinion Mining (3 papers), EEG and Brain-Computer Interfaces (3 papers), Face and Expression Recognition (2 papers) and Face Recognition and Perception (2 papers). The work is most often cited by research in Cognitive Neuroscience (94 citations), Artificial Intelligence (64 citations), Signal Processing (21 citations), Computational Mathematics (1 citation) and Computer Networks and Communications (34 citations). Jun Kitazono has collaborated with scholars based in Japan, Germany and Taiwan. Frequent co-authors include Seiichi Ozawa, Masato Okada, Masafumi Oizumi, Mark D. Lescroart, Manabu Tanifuji, Kenji Nagata, Takayuki Sato, Toshiaki Omori, Tao Ban and Ryota Kanai. Their work appears in journals such as Journal of Neuroscience, iScience, Cell Reports, Journal of the Physical Society of Japan and Network Neuroscience.
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.