Şengül Doğan
Impact in
- Cognitive Neuroscience top 1%
- EEG and Brain-Computer Interfaces
- Functional Brain Connectivity Studies
- Signal Processing top 1%
- Music and Audio Processing
- Blind Source Separation Techniques
Papers in
-
- EEG and Brain-Computer Interfaces 69
- Functional Brain Connectivity Studies 12
- Co-authors
- Türker Tuncer (157 shared papers)U. Rajendra Acharya (106 shared papers)Türker Tuncer (63 shared papers)Abdülhamit Subaşı (19 shared papers)Prabal Datta Barua (93 shared papers)Mehmet Bayğın (66 shared papers)Erhan Akbal (31 shared papers)Fatih Özyurt (10 shared papers)
In The Last Decade
Şengül Doğan
221 papers receiving 4.8k citations
Peers
Comparison fields: 5 of 160
- Cognitive Neuroscience 1.7k
- Signal Processing 807
- Experimental and Cognitive Psychology 701
- Neurology 398
- Computer Vision and Pattern Recognition 946
Countries citing papers authored by Şengül Doğan
This map shows the geographic impact of Şengül Doğan'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 Şengül Doğan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Şengül Doğan more than expected).
Fields of papers citing papers by Şengül Doğan
This network shows the impact of papers produced by Şengül Doğan. 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 Şengül Doğan. The network helps show where Şengül Doğan may publish in the future.
Co-authors
The 25 scholars most cited alongside Şengül Doğan, 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 243 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 189 | |
| 2 | 2020 | 146 | |
| 3 | 2021 | 134 | |
| 4 | 2020 | 128 | |
| 5 | 2020 | 114 | |
| 6 | 2021 | 113 | |
| 7 | 2020 | 102 | |
| 8 | 2021 | 100 | |
| 9 | 2019 | 99 | |
| 10 | 2021 | 93 | |
| 11 | 2021 | 85 | |
| 12 | 2021 | 81 | |
| 13 | 2021 | 75 | |
| 14 | 2022 | 74 | |
| 15 | 2022 | 74 | |
| 16 | 2023 | 73 | |
| 17 | 2022 | 70 | |
| 18 | 2023 | 67 | |
| 19 | 2022 | 67 | |
| 20 | 2022 | 64 |
About Şengül Doğan
Şengül Doğan is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition, Signal Processing, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging, having authored 243 papers that have together received 4.9k indexed citations. Recurring topics across this work include EEG and Brain-Computer Interfaces (69 papers), Music and Audio Processing (27 papers), ECG Monitoring and Analysis (23 papers), COVID-19 diagnosis using AI (20 papers), Emotion and Mood Recognition (20 papers), Speech and Audio Processing (20 papers), Blind Source Separation Techniques (13 papers) and Functional Brain Connectivity Studies (12 papers). The work is most often cited by research in Cognitive Neuroscience (1.7k citations), Signal Processing (807 citations), Experimental and Cognitive Psychology (701 citations), Neurology (398 citations) and Computer Vision and Pattern Recognition (946 citations). Şengül Doğan has collaborated with scholars based in Türkiye, Australia and Singapore. Frequent co-authors include Türker Tuncer, U. Rajendra Acharya, Türker Tuncer, Abdülhamit Subaşı, Prabal Datta Barua, Mehmet Bayğın, Erhan Akbal, Fatih Özyurt, Ru‐San Tan and Emrah Aydemir. Their work appears in journals such as Biomedical Signal Processing and Control, Multimedia Tools and Applications, Applied Acoustics, Expert Systems with Applications and Engineering Applications of Artificial Intelligence.
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.