Kirill Aristovich
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- Neuroscience and Neural Engineering 29
- Neurology top 5%
- Vagus Nerve Stimulation Research 8
- Cognitive Neuroscience top 10%
- EEG and Brain-Computer Interfaces 18
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- Electrical and Bioimpedance Tomography 39
- Biomedical Engineering top 10%
- Muscle activation and electromyography studies 14
- Microfluidic and Bio-sensing Technologies 10
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- Hemodynamic Monitoring and Therapy 7
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- Flow Measurement and Analysis 6
- Co-authors
- David HolderJames AveryGustavo Sato dos SantosMayo FaulknerSvetlana MastitskayaJustin PerkinsNicole ThompsonMatthew C. Walker
- Journals
- Nature Communications (1 paper)SHILAP Revista de lepidopterología (3 papers)NeuroImage (6 papers)
- Partner nations
- United KingdomRussiaUnited States
In The Last Decade
Kirill Aristovich
60 papers receiving 882 citations
Peers
Comparison fields: 5 of 80
- Cellular and Molecular Neuroscience 362
- Neurology 136
- Cognitive Neuroscience 231
- Electrical and Electronic Engineering 510
- Biomedical Engineering 386
Countries citing papers authored by Kirill Aristovich
This map shows the geographic impact of Kirill Aristovich'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 Kirill Aristovich with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kirill Aristovich more than expected).
Fields of papers citing papers by Kirill Aristovich
This network shows the impact of papers produced by Kirill Aristovich. 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 Kirill Aristovich. The network helps show where Kirill Aristovich may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Kirill Aristovich, 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 | 2024 | 6 | |
| 2 | 2024 | 3 | |
| 3 | 2023 | 15 | |
| 4 | 2023 | 3 | |
| 5 | 2023 | 3 | |
| 6 | 2021 | 3 | |
| 7 | 2021 | 43 | |
| 8 | 2020 | 36 | |
| 9 | 2020 | 5 | |
| 10 | 2020 | 12 | |
| 11 | 2020 | 7 | |
| 12 | 2019 | 5 | |
| 13 | 2018 | 21 | |
| 14 | 2018 | 25 | |
| 15 | 2018 | 16 | |
| 16 | 2018 | 24 | |
| 17 | 2016 | 2 | |
| 18 | 2016 | 14 | |
| 19 | Machine learning approach to clinical stroke type differentiation using Electrical Impedance Tomography (EIT) | 2015 | 1 |
| 20 | 2015 | 118 |
About Kirill Aristovich
Kirill Aristovich is a scholar working on Cellular and Molecular Neuroscience, Cognitive Neuroscience and Neurology, having authored 62 papers that have together received 899 indexed citations. Recurring topics across this work include Electrical and Bioimpedance Tomography (39 papers), Neuroscience and Neural Engineering (29 papers), EEG and Brain-Computer Interfaces (18 papers), Muscle activation and electromyography studies (14 papers), Microfluidic and Bio-sensing Technologies (10 papers), Vagus Nerve Stimulation Research (8 papers), Hemodynamic Monitoring and Therapy (7 papers) and Flow Measurement and Analysis (6 papers). The work is most often cited by research in Cellular and Molecular Neuroscience (362 citations), Neurology (136 citations) and Cognitive Neuroscience (231 citations). Kirill Aristovich has collaborated with scholars based in United Kingdom, Russia and United States. Frequent co-authors include David Holder, James Avery, Gustavo Sato dos Santos, Mayo Faulkner, Svetlana Mastitskaya, Justin Perkins, Nicole Thompson, Matthew C. Walker, Daniel Chew and Matteo Donegà. Their work appears in journals such as Nature Communications, SHILAP Revista de lepidopterología and NeuroImage.
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.