Nupur Chugh

556 citations
9 papers · 344 · 1 hit paper · h-index 5

Impact in

Papers in

Nupur Chugh

8 papers receiving 340 citations

Nupur Chugh's Hit Papers

Review of Machine Learning Techniques for EEG Based Brain Computer Interface 2022 · 150 citations
1500+1+2Years since publication50100150

Peers

Nupur Chugh
Comparison fields: 5 of 45
  • Cognitive Neuroscience 298
  • Human-Computer Interaction 41
  • Cellular and Molecular Neuroscience 112
  • Signal Processing 47
  • Computational Mathematics 2
Replace Sylvain Chevallier with:
Sylvain Chevallier France
Yar Muhammad Estonia
Mengxi Dai China
Vahid Shalchyan Iran
Attila Korik United Kingdom
Thorir Mar Ingolfsson Italy
Seif Eldawlatly Egypt
Donglin Li China
Xiangmin Lun China
Nupur Chugh relative to Sylvain Chevallier France Sylvain Chevallier's profile →
Citations per field
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Citations per year

Countries citing papers authored by Nupur Chugh

Since Specialization
Citations

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

Fields of papers citing papers by Nupur Chugh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 1 scholars most cited alongside Nupur Chugh, 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 Nupur Chugh Line = papers co-authored together Nupur Chugh links everyone, so they are left out of the graph.

All Works

9 of 9 papers shown
#Work
1 2019163
2
Review of Machine Learning Techniques for EEG Based Brain Computer Interface
Hit paper breakdown →
2022150
3 202013
4 20238
5 20215
6 20223
7 20251
8 20201
9 20240

About Nupur Chugh

Nupur Chugh is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience, Electrical and Electronic Engineering, Neurology and Cardiology and Cardiovascular Medicine, having authored 9 papers that have together received 344 indexed citations. Recurring topics across this work include EEG and Brain-Computer Interfaces (8 papers), Neuroscience and Neural Engineering (4 papers), Advanced Memory and Neural Computing (3 papers), Neural dynamics and brain function (2 papers), Attention Deficit Hyperactivity Disorder (1 paper), Neurological disorders and treatments (1 paper), Emotion and Mood Recognition (1 paper) and Functional Brain Connectivity Studies (1 paper). The work is most often cited by research in Cognitive Neuroscience (298 citations), Human-Computer Interaction (41 citations), Cellular and Molecular Neuroscience (112 citations), Signal Processing (47 citations) and Computational Mathematics (2 citations). Nupur Chugh has collaborated with scholars based in India. Frequent co-authors include Swati Aggarwal. Their work appears in journals such as Archives of Computational Methods in Engineering, Clinical EEG and Neuroscience, Minds and Machines, Computer Methods and Programs in Biomedicine and Array.

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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