Xiaotong Gu

606 citations
3 papers · 306 · 1 hit paper · h-index 2

Impact in

Papers in

Xiaotong Gu

2 papers receiving 300 citations

Xiaotong Gu's Hit Papers

EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies \n on Signal Sensing Technologies and Computational Intelligence Approaches and \n their Applications 2021 · 227 citations
2270+1+3Years since publication50100150200

Peers

Xiaotong Gu
Comparison fields: 5 of 62
  • Health Informatics 17
  • Cognitive Neuroscience 203
  • Human-Computer Interaction 33
  • Cellular and Molecular Neuroscience 66
  • Signal Processing 34
Replace Shubham Dodia with:
Shubham Dodia India
Seif Eldawlatly Egypt
Omar AlZoubi Jordan
Shuailei Zhang China
Veeky Baths India
Tamara Bonaci United States
Aklima Akter Lima Bangladesh
Sujoy Chandra Das Bangladesh
Aurora Saibene Italy
Reem Kateb Saudi Arabia
Xiaotong Gu relative to Shubham Dodia India Shubham Dodia's profile →
Citations per field
00.5×1.5×2.1×
Shubham Dodia · 1×
Citations per year

Countries citing papers authored by Xiaotong Gu

Since Specialization
Citations

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

Fields of papers citing papers by Xiaotong Gu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 19 scholars most cited alongside Xiaotong Gu, 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 Xiaotong Gu Line = papers co-authored together Xiaotong Gu links everyone, so they are left out of the graph.

All Works

3 of 3 papers shown
#Work
1
EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies \n on Signal Sensing Technologies and Computational Intelligence Approaches and \n their Applications
Hit paper breakdown →
2021227
2 202379
3 20250

About Xiaotong Gu

Xiaotong Gu is a scholar working on Computer Vision and Pattern Recognition, Instrumentation, Cognitive Neuroscience, Artificial Intelligence and Aerospace Engineering, having authored 3 papers that have together received 306 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (1 paper), EEG and Brain-Computer Interfaces (1 paper), Optical measurement and interference techniques (1 paper), Robotics and Sensor-Based Localization (1 paper), Explainable Artificial Intelligence (XAI) (1 paper), Adversarial Robustness in Machine Learning (1 paper), Advanced Optical Sensing Technologies (1 paper) and Advanced Memory and Neural Computing (1 paper). The work is most often cited by research in Health Informatics (17 citations), Cognitive Neuroscience (203 citations), Human-Computer Interaction (33 citations), Cellular and Molecular Neuroscience (66 citations) and Signal Processing (34 citations). Xiaotong Gu has collaborated with scholars based in Australia, China and United States. Frequent co-authors include Tzyy‐Ping Jung, Peng Xu, Zehong Cao, Chin‐Teng Lin, Alireza Jolfaei, Dongrui Wu, Renjie Li, Byeong Ho Kang, H. Wei and Guan Huang. Their work appears in journals such as Ocean Engineering, SHILAP Revista de lepidopterología and UTS ePRESS (University of Technology Sydney).

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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