Chuangquan Chen

691 total citations
31 papers, 446 citations indexed

About

Chuangquan Chen is a scholar working on Artificial Intelligence, Cognitive Neuroscience and Computer Vision and Pattern Recognition. According to data from OpenAlex, Chuangquan Chen has authored 31 papers receiving a total of 446 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 10 papers in Cognitive Neuroscience and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Chuangquan Chen's work include Machine Learning and ELM (10 papers), EEG and Brain-Computer Interfaces (10 papers) and Domain Adaptation and Few-Shot Learning (6 papers). Chuangquan Chen is often cited by papers focused on Machine Learning and ELM (10 papers), EEG and Brain-Computer Interfaces (10 papers) and Domain Adaptation and Few-Shot Learning (6 papers). Chuangquan Chen collaborates with scholars based in China, Macao and Greece. Chuangquan Chen's co-authors include Hongtao Wang, Anastasios Bezerianos, Chi‐Man Vong, Feng Wan, Zhiguo Zhang, Yu Sun, Junhua Li, Pak Kin Wong, Nitish V. Thakor and Jie Du and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Information Sciences.

In The Last Decade

Chuangquan Chen

28 papers receiving 438 citations

Peers

Chuangquan Chen

ECP

EEE

Jingcong Li China

Sobhan Sheykhivand Iran

Ming Meng China

Pengpai Wang China

Barjinder Kaur India

Shang-Lin Wu Taiwan

Taha Beyrouthy Kuwait

Jordan J. Bird United Kingdom

Jingyu Liu China

Jingcong Li China

Chuangquan Chen

310 ×1.3

109 ×0.8

187 ×1.4

ECP

68 ×1.1

EEE

59 ×1.0

Citations per year, relative to Chuangquan Chen Chuangquan Chen (= 1×) peers Jingcong Li

Countries citing papers authored by Chuangquan Chen

Since Specialization

Citations

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

Fields of papers citing papers by Chuangquan Chen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chuangquan Chen

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

All Works

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20 of 20 papers shown

Vong, Chi‐Man, et al.. (2025). HSA-Former: Hierarchical Spatial Aggregation Transformer for EEG-Based Emotion Recognition. IEEE Transactions on Computational Social Systems. 12(6). 4254–4265. 2 indexed citations

Li, Shuran, Chuangquan Chen, Junhua Li, et al.. (2025). Towards a nuanced classification of mental fatigue: A comprehensive review of detection techniques and prospective research. Biomedical Signal Processing and Control. 111. 108496–108496. 3 indexed citations

Vong, Chi‐Man, et al.. (2025). A new glass segmentation network with indoor and outdoor panoramic images. Applied Soft Computing. 183. 113626–113626.

Xu, Tao, et al.. (2024). Subject-specific CNN model with parameter-based transfer learning for SSVEP detection. Biomedical Signal Processing and Control. 103. 107404–107404. 2 indexed citations

Wang, Hongtao, et al.. (2024). Multi-Scale Masked Autoencoders for Cross-Session Emotion Recognition. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 32. 1637–1646. 14 indexed citations

Vong, Chi‐Man, et al.. (2024). Weakly Supervised Semantic Segmentation via Dual-Stream Contrastive Learning of Cross-Image Contextual Information. IEEE Transactions on Industrial Informatics. 20(10). 11635–11643.

Zhang, Wen, et al.. (2024). Billion-scale pre-trained knowledge graph model for conversational chatbot. Neurocomputing. 606. 128353–128353. 2 indexed citations

Chen, Chuangquan, et al.. (2024). Comprehensive Multisource Learning Network for Cross-Subject Multimodal Emotion Recognition. IEEE Transactions on Emerging Topics in Computational Intelligence. 9(1). 365–380. 12 indexed citations

Chen, Chuangquan, et al.. (2023). ST-CapsNet: Linking Spatial and Temporal Attention With Capsule Network for P300 Detection Improvement. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 31. 991–1000. 36 indexed citations

10.

Chen, Chuangquan, et al.. (2023). DDIO-Mapping: A Fast and Robust Visual-Inertial Odometry for Low-Texture Environment Challenge. IEEE Transactions on Industrial Informatics. 20(3). 4418–4428. 6 indexed citations

11.

Chen, Chuangquan, et al.. (2023). Self-Attentive Channel-Connectivity Capsule Network for EEG-Based Driving Fatigue Detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 31. 3152–3162. 41 indexed citations

12.

Du, Jie, Peng Liu, Chi‐Man Vong, et al.. (2023). Class-Incremental Learning Method With Fast Update and High Retainability Based on Broad Learning System. IEEE Transactions on Neural Networks and Learning Systems. 35(8). 11332–11345. 16 indexed citations

13.

Chen, Chuangquan, et al.. (2022). Fusing Frequency-Domain Features and Brain Connectivity Features for Cross-Subject Emotion Recognition. IEEE Transactions on Instrumentation and Measurement. 71. 1–15. 55 indexed citations

14.

Chen, Chuangquan, et al.. (2022). An Accurate Defect Detection Model for Photosensitive Elements Based on Improved YOLOV4 Deep Neural Network. 337–345. 2 indexed citations

15.

Wang, Hongtao, Fan Yan, Tao Xu, et al.. (2021). Brain-Controlled Wheelchair Review: From Wet Electrode to Dry Electrode, From Single Modal to Hybrid Modal, From Synchronous to Asynchronous. IEEE Access. 9. 55920–55938. 21 indexed citations

16.

Luo, Jiahua, Chi‐Man Vong, Zhenbao Liu, & Chuangquan Chen. (2021). An Inverse-Free and Scalable Sparse Bayesian Extreme Learning Machine for Classification Problems. IEEE Access. 9. 87543–87551. 1 indexed citations

17.

Chen, Chuangquan, et al.. (2021). Easy Domain Adaptation for cross-subject multi-view emotion recognition. Knowledge-Based Systems. 239. 107982–107982. 22 indexed citations

18.

Wang, Hongtao, et al.. (2020). Linking Attention-Based Multiscale CNN With Dynamical GCN for Driving Fatigue Detection. IEEE Transactions on Instrumentation and Measurement. 70. 1–11. 69 indexed citations

19.

Wang, Hongtao, Tao Xu, Cong Tang, et al.. (2020). Diverse Feature Blend Based on Filter-Bank Common Spatial Pattern and Brain Functional Connectivity for Multiple Motor Imagery Detection. IEEE Access. 8. 155590–155601. 25 indexed citations

20.

Du, Jie, Chi‐Man Vong, Chuangquan Chen, Peng Liu, & Zhenbao Liu. (2019). Supervised Extreme Learning Machine-Based Auto-Encoder for Discriminative Feature Learning. IEEE Access. 8. 11700–11709. 10 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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