Gang Pan

11.8k total citations · 1 hit paper
359 papers, 6.7k citations indexed

About

Gang Pan is a scholar working on Cognitive Neuroscience, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Gang Pan has authored 359 papers receiving a total of 6.7k indexed citations (citations by other indexed papers that have themselves been cited), including 102 papers in Cognitive Neuroscience, 100 papers in Electrical and Electronic Engineering and 80 papers in Computer Vision and Pattern Recognition. Recurrent topics in Gang Pan's work include Advanced Memory and Neural Computing (66 papers), Neural dynamics and brain function (61 papers) and EEG and Brain-Computer Interfaces (47 papers). Gang Pan is often cited by papers focused on Advanced Memory and Neural Computing (66 papers), Neural dynamics and brain function (61 papers) and EEG and Brain-Computer Interfaces (47 papers). Gang Pan collaborates with scholars based in China, United States and Singapore. Gang Pan's co-authors include Zhaohui Wu, Shijian Li, Daqing Zhang, Lin Sun, Huajin Tang, Guande Qi, Shihong Lao, Yueming Wang, Qi Xu and Wangsheng Zhang and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Gang Pan

328 papers receiving 6.5k citations

Hit Papers

Eyeblink-based Anti-Spoofing in Face Recognition from a G... 2007 2026 2013 2019 2007 100 200 300 400

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Gang Pan China 42 1.5k 1.4k 1.3k 1.2k 1.1k 359 6.7k
Lina Yao Australia 46 811 0.6× 2.0k 1.4× 1.2k 0.9× 427 0.3× 688 0.7× 309 7.8k
Weihai Chen China 38 871 0.6× 1.4k 1.0× 507 0.4× 538 0.4× 206 0.2× 434 7.1k
Zhaohui Wu China 42 466 0.3× 1.7k 1.2× 328 0.3× 648 0.5× 896 0.9× 325 7.0k
Chai Quek Singapore 42 587 0.4× 908 0.6× 1.1k 0.9× 145 0.1× 603 0.6× 246 6.3k
Christopher J. Watkins United Kingdom 10 2.0k 1.4× 1.1k 0.8× 538 0.4× 239 0.2× 223 0.2× 14 9.8k
Abdulmotaleb El Saddik Canada 47 1.2k 0.8× 3.2k 2.2× 1.5k 1.2× 188 0.2× 710 0.7× 609 12.2k
Xin Xu China 51 1.5k 1.0× 2.5k 1.7× 232 0.2× 694 0.6× 515 0.5× 465 10.5k
Shiliang Sun China 43 423 0.3× 2.7k 1.9× 582 0.5× 255 0.2× 747 0.7× 197 7.2k
Zhenghua Chen Singapore 44 2.1k 1.5× 1.6k 1.1× 551 0.4× 193 0.2× 898 0.9× 225 9.3k
Jayavardhana Gubbi Australia 21 2.5k 1.7× 1.6k 1.1× 333 0.3× 291 0.2× 636 0.6× 89 9.9k

Countries citing papers authored by Gang Pan

Since Specialization
Citations

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

Fields of papers citing papers by Gang Pan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gang Pan

This figure shows the co-authorship network connecting the top 25 collaborators of Gang Pan. A scholar is included among the top collaborators of Gang Pan 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 Gang Pan. Gang Pan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Zhang, Yaoyun, et al.. (2025). The capacity of skin potential in generalized anxiety disorder discrimination using weighted feature fusion. Biomedical Signal Processing and Control. 106. 107749–107749. 1 indexed citations
2.
Wu, Fan, Xiangfeng Lin, Yuying Chen, et al.. (2025). Breaking barriers: noninvasive AI model for BRAFV600E mutation identification. International Journal of Computer Assisted Radiology and Surgery. 20(5). 935–947. 1 indexed citations
3.
Shi, Boxin, et al.. (2025). Revisiting Supervised Learning-Based Photometric Stereo Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(8). 6320–6337.
4.
Yu, Xiao, Peng Chen, Huaze Zhu, et al.. (2025). Bio-plausible reconfigurable spiking neuron for neuromorphic computing. Science Advances. 11(6). eadr6733–eadr6733. 14 indexed citations
5.
Han, H., et al.. (2025). LAC-PS: A Light Direction Selection Policy Under the Accuracy Constraint for Photometric Stereo. IEEE Transactions on Circuits and Systems for Video Technology. 35(12). 12622–12635.
6.
Yang, Bo, et al.. (2024). Enhancing SNN-based spatio-temporal learning: A benchmark dataset and Cross-Modality Attention model. Neural Networks. 180. 106677–106677. 6 indexed citations
7.
Chen, Yuying, et al.. (2024). Ferroptosis in thyroid cancer: Potential mechanisms, effective therapeutic targets and predictive biomarker. Biomedicine & Pharmacotherapy. 177. 116971–116971. 8 indexed citations
8.
Li, Guoqi, Lei Deng, Huajin Tang, et al.. (2024). Brain-Inspired Computing: A Systematic Survey and Future Trends. Proceedings of the IEEE. 112(6). 544–584. 25 indexed citations
9.
Wang, Weixiao, et al.. (2024). A Battery-Free Neural-Recording Chip Achieving 5.5 cm Fully-Implanted Depth by Galvanically-Switching Passive Body Channel Communication. IEEE Journal of Solid-State Circuits. 59(8). 2591–2603. 4 indexed citations
10.
Zhang, Li, et al.. (2023). RM-FSP: Regret minimization optimizes neural fictitious self-play. Neurocomputing. 549. 126471–126471. 1 indexed citations
11.
Zhang, Chengjun, et al.. (2023). Trainable Spiking-YOLO for low-latency and high-performance object detection. Neural Networks. 172. 106092–106092. 18 indexed citations
12.
Fang, Tao, Qiang Zheng, Qi Yu, & Gang Pan. (2023). Extracting Semantic-Dynamic Features for Long-Term Stable Brain Computer Interface. Proceedings of the AAAI Conference on Artificial Intelligence. 37(5). 5965–5973. 1 indexed citations
13.
Jin, Xiaobo, Ming Zhang, Rui Yan, Gang Pan, & De Ma. (2023). R-SNN: Region-Based Spiking Neural Network for Object Detection. IEEE Transactions on Cognitive and Developmental Systems. 16(3). 810–817. 8 indexed citations
14.
Zhao, Sha, Shiwei Zhao, Runze Wu, et al.. (2023). PU-Detector: A PU Learning-based Framework for Real Money Trading Detection in MMORPG. ACM Transactions on Knowledge Discovery from Data. 18(4). 1–26. 3 indexed citations
15.
Luo, Yuxuan, et al.. (2023). A Self-Adaptive Dual-ILRO Clock-Recovery Technique for Two-Tone Battery-Free Crystal-Free Neural-Recording SoC. IEEE Transactions on Biomedical Circuits and Systems. 18(1). 39–50. 1 indexed citations
16.
Luo, Yuxuan, et al.. (2022). A Wireless Headstage System Based on Neural-Recording Chip Featuring 315 nW Kickback-Reduction SAR ADC. IEEE Transactions on Biomedical Circuits and Systems. 17(1). 105–115. 10 indexed citations
17.
Sun, Xuyun, Yu Qi, Yueming Wang, & Gang Pan. (2022). Convolutional Multiple Instance Learning for Sleep Spindle Detection With Label Refinement. IEEE Transactions on Cognitive and Developmental Systems. 15(1). 272–284. 2 indexed citations
19.
Pan, Gang, et al.. (2018). Security risk assessment of information system based on FAHP and attack tree. SHILAP Revista de lepidopterología. 1 indexed citations
20.
Wu, Hong, et al.. (2016). Clinical effect of fissure for ligamentum teres hepatic approach in hepatectomy. Zhōnghuá xiāohuà wàikē zázhì/Zhonghua xiaohua waike zazhi. 15(1). 53–57.

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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