Sigang Yu

459 total citations
12 papers, 79 citations indexed

About

Sigang Yu is a scholar working on Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Electrical and Electronic Engineering. According to data from OpenAlex, Sigang Yu has authored 12 papers receiving a total of 79 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Cognitive Neuroscience, 5 papers in Radiology, Nuclear Medicine and Imaging and 3 papers in Electrical and Electronic Engineering. Recurrent topics in Sigang Yu's work include EEG and Brain-Computer Interfaces (6 papers), Functional Brain Connectivity Studies (6 papers) and Neural dynamics and brain function (4 papers). Sigang Yu is often cited by papers focused on EEG and Brain-Computer Interfaces (6 papers), Functional Brain Connectivity Studies (6 papers) and Neural dynamics and brain function (4 papers). Sigang Yu collaborates with scholars based in China, United States and Australia. Sigang Yu's co-authors include Shu Zhang, Tianming Liu, Shijie Zhao, Ruoyang Wang, Jinglei Lv, Lin Zhao, Xi Jiang, Xintao Hu, Junwei Han and Huan Gao and has published in prestigious journals such as IEEE Transactions on Biomedical Engineering, IEEE Transactions on Neural Networks and Learning Systems and Neural Networks.

In The Last Decade

Sigang Yu

10 papers receiving 78 citations

Peers

Sigang Yu

CN

AI

RNMI

BE

CVPR

Ioanna Skampardoni United States

Zhijian Yang United States

Fabián Pedregosa United States

Steven Cao United States

Tiago Azevedo United Kingdom

Matthew Leming United States

Livija Jakaite United Kingdom

Василий Борисов Russia

Ghassem Tofighi Canada

Zhigang Li China

Ioanna Skampardoni United States

Sigang Yu

21 ×0.6

CN

21 ×1.1

AI

16 ×0.8

RNMI

8 ×1.0

BE

14 ×1.8

CVPR

Citations per year, relative to Sigang Yu Sigang Yu (= 1×) peers Ioanna Skampardoni

Countries citing papers authored by Sigang Yu

Since Specialization

Citations

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

Fields of papers citing papers by Sigang Yu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sigang Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Sigang Yu. A scholar is included among the top collaborators of Sigang Yu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sigang Yu. Sigang Yu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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12 of 12 papers shown

1.

Zhu, Di, Sigang Yu, Ruoyang Wang, et al.. (2024). Identifying influential nodes in brain networks via self-supervised graph-transformer. Computers in Biology and Medicine. 186. 109629–109629. 3 indexed citations

2.

Wang, Xuhui, et al.. (2024). ST-GF: Graph-based Fusion of Spatial and Temporal Features for EEG Motor Imagery Decoding. 3811–3816.

3.

Yu, Sigang, Lin Zhao, Dajiang Zhu, et al.. (2023). MEET: A Multi-Band EEG Transformer for Brain States Decoding. IEEE Transactions on Biomedical Engineering. 71(5). 1442–1453. 15 indexed citations

4.

Zhang, Shu, Ruoyang Wang, Sigang Yu, et al.. (2023). Differentiating brain states via multi-clip random fragment strategy-based interactive bidirectional recurrent neural network. Neural Networks. 165. 1035–1049. 11 indexed citations

5.

Zhang, Shu, et al.. (2023). MM-GLCM-CNN: A multi-scale and multi-level based GLCM-CNN for polyp classification. Computerized Medical Imaging and Graphics. 108. 102257–102257. 15 indexed citations

6.

Hu, Huawen, et al.. (2023). Effective Human Motor Imagery Recognition via Segment Pool Based on One-Dimensional Convolutional Neural Network with Bidirectional Recurrent Attention Unit Network. Applied Sciences. 13(16). 9233–9233.

7.

Zhang, Shu, et al.. (2022). An Explainable and Generalizable Recurrent Neural Network Approach for Differentiating Human Brain States on EEG Dataset. IEEE Transactions on Neural Networks and Learning Systems. 35(6). 7339–7350. 9 indexed citations

8.

Zhang, Shu, Junxin Wang, Sigang Yu, et al.. (2022). An explainable deep learning framework for characterizing and interpreting human brain states. Medical Image Analysis. 83. 102665–102665. 14 indexed citations

9.

Yu, Sigang, Ruoyang Wang, Shijie Zhao, et al.. (2022). A hybrid learning framework for fine-grained interpretation of brain spatiotemporal patterns during naturalistic functional magnetic resonance imaging. Frontiers in Human Neuroscience. 16. 944543–944543. 1 indexed citations

10.

Zhu, Zhanxia, et al.. (2022). Trajectory Planning of Free-Floating Space Robot Based on Dual-Mode Switching. Journal of Aerospace Engineering. 35(3). 2 indexed citations

11.

Zhang, Shu, Ruoyang Wang, Zhen Han, et al.. (2022). A DICCCOL-based K-nearest landmark detection method for identifying common and consistent 3-hinge gyral folding landmarks. Chaos Solitons & Fractals. 158. 112018–112018. 3 indexed citations

12.

Zhang, Shu, Lei Du, Yin Zhang⋆, et al.. (2021). Joint Analysis of Functional and Structural Connectomes Between Preterm and Term Infant Brains via Canonical Correlation Analysis With Locality Preserving Projection. Frontiers in Neuroscience. 15. 724391–724391. 6 indexed citations

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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