Ayan Sengupta
Impact in
- Cognitive Neuroscience top 10%
- Neural dynamics and brain function
- Functional Brain Connectivity Studies
- Face Recognition and Perception
- Visual perception and processing mechanisms
-
- Advanced Data Compression Techniques
- Image and Signal Denoising Methods
Papers in
-
- Functional Brain Connectivity Studies 5
- EEG and Brain-Computer Interfaces 2
- Neural dynamics and brain function 2
-
- Image and Signal Denoising Methods 2
- Advanced Data Compression Techniques 2
- Co-authors
- Michael Hilton (2 shared papers)Michael Hanke (4 shared papers)Falko R. Kaule (2 shared papers)Jörg Stadler (2 shared papers)Roland Nigbur (1 shared paper)Nico Adelhöfer (1 shared paper)Vittorio Iacovella (1 shared paper)Florian Baumgartner (1 shared paper)
- Journals
- NeuroImage (3 papers)Scientific Data (2 papers)Multimedia Systems (1 paper)Data in Brief (1 paper)NMR in Biomedicine (1 paper)
- Partner nations
- GermanyUnited KingdomUnited States
In The Last Decade
Ayan Sengupta
8 papers receiving 209 citations
Peers
Comparison fields: 5 of 47
- Cognitive Neuroscience 122
- Computer Vision and Pattern Recognition 100
- Signal Processing 44
- Computational Mathematics 2
- Media Technology 17
Countries citing papers authored by Ayan Sengupta
This map shows the geographic impact of Ayan Sengupta'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 Ayan Sengupta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ayan Sengupta more than expected).
Fields of papers citing papers by Ayan Sengupta
This network shows the impact of papers produced by Ayan Sengupta. 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 Ayan Sengupta. The network helps show where Ayan Sengupta may publish in the future.
Co-authors
The 25 scholars most cited alongside Ayan Sengupta, 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 | 1994 | 91 | |
| 2 | 2016 | 65 | |
| 3 | 2016 | 41 | |
| 4 | 2020 | 13 | |
| 5 | 2017 | 11 | |
| 6 | 2019 | 3 | |
| 7 | 1994 | 3 | |
| 8 | 2017 | 2 | |
| 9 | 2024 | 0 |
About Ayan Sengupta
Ayan Sengupta is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Signal Processing and Computational Mechanics, having authored 9 papers that have together received 229 indexed citations. Recurring topics across this work include Functional Brain Connectivity Studies (5 papers), Advanced Neuroimaging Techniques and Applications (2 papers), EEG and Brain-Computer Interfaces (2 papers), Image and Signal Denoising Methods (2 papers), Advanced Data Compression Techniques (2 papers), Advanced MRI Techniques and Applications (2 papers), Neural dynamics and brain function (2 papers) and Transcranial Magnetic Stimulation Studies (1 paper). The work is most often cited by research in Cognitive Neuroscience (122 citations), Computer Vision and Pattern Recognition (100 citations), Signal Processing (44 citations), Computational Mathematics (2 citations) and Media Technology (17 citations). Ayan Sengupta has collaborated with scholars based in Germany, United Kingdom and United States. Frequent co-authors include Michael Hilton, Michael Hanke, Falko R. Kaule, Jörg Stadler, Roland Nigbur, Nico Adelhöfer, Vittorio Iacovella, Florian Baumgartner, J. Swaroop Guntupalli and Michael B. Hoffmann. Their work appears in journals such as NeuroImage, Scientific Data, Multimedia Systems, Data in Brief and NMR in Biomedicine.
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.