Hengjin Ke

657 total citations
14 papers, 387 citations indexed

About

Hengjin Ke is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Computational Mathematics. According to data from OpenAlex, Hengjin Ke has authored 14 papers receiving a total of 387 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Cognitive Neuroscience, 5 papers in Cellular and Molecular Neuroscience and 4 papers in Computational Mathematics. Recurrent topics in Hengjin Ke's work include EEG and Brain-Computer Interfaces (9 papers), Functional Brain Connectivity Studies (6 papers) and Tensor decomposition and applications (4 papers). Hengjin Ke is often cited by papers focused on EEG and Brain-Computer Interfaces (9 papers), Functional Brain Connectivity Studies (6 papers) and Tensor decomposition and applications (4 papers). Hengjin Ke collaborates with scholars based in China, United Kingdom and Australia. Hengjin Ke's co-authors include Xiaoli Li, Dan Chen, Yunbo Tang, Tejal Shah, Rajiv Ranjan, Dan Chen, Xianzeng Liu, Xinhua Zhang, Fengqin Wang and Lei Zhang and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Pattern Recognition.

In The Last Decade

Hengjin Ke

14 papers receiving 382 citations

Peers

Hengjin Ke
Duanpo Wu China
Junbiao Liu China
Yunbo Tang China
Phattarapong Sawangjai Thailand
Ömer Türk Türkiye
Ziad Sankari United States
Arti Anuragi India
Peyvand Ghaderyan Iran
Yinghua Wang China
Duanpo Wu China
Hengjin Ke
Citations per year, relative to Hengjin Ke Hengjin Ke (= 1×) peers Duanpo Wu

Countries citing papers authored by Hengjin Ke

Since Specialization
Citations

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

Fields of papers citing papers by Hengjin Ke

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hengjin Ke

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

All Works

14 of 14 papers shown
1.
Wang, Fengqin, Hengjin Ke, & Chang Cai. (2025). Deep Wavelet Self-Attention Non-negative Tensor Factorization for non-linear analysis and classification of fMRI data. Applied Soft Computing. 182. 113522–113522. 4 indexed citations
2.
Wang, Fengqin, Hengjin Ke, Hsi‐Pin Ma, & Yunbo Tang. (2025). Deep Wavelet Temporal-Frequency Attention for nonlinear fMRI factorization in ASD. Pattern Recognition. 165. 111543–111543. 7 indexed citations
3.
Wang, Fengqin, Hengjin Ke, & Yunbo Tang. (2024). Fusion of generative adversarial networks and non-negative tensor decomposition for depression fMRI data analysis. Information Processing & Management. 62(2). 103961–103961. 9 indexed citations
4.
5.
Ke, Hengjin, Dan Chen, Quanming Yao, et al.. (2023). Deep Factor Learning for Accurate Brain Neuroimaging Data Analysis on Discrimination for Structural MRI and Functional MRI. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 21(4). 582–595. 19 indexed citations
6.
Chen, Boran, Bo Yin, & Hengjin Ke. (2023). Interpretation of deep non-linear factorization for autism. Frontiers in Psychiatry. 14. 1199113–1199113. 1 indexed citations
7.
Ke, Hengjin, et al.. (2022). ADHD identification and its interpretation of functional connectivity using deep self-attention factorization. Knowledge-Based Systems. 250. 109082–109082. 32 indexed citations
8.
Ke, Hengjin, et al.. (2021). Interpretation of Frequency Channel-Based CNN on Depression Identification. Frontiers in Computational Neuroscience. 15. 773147–773147. 10 indexed citations
9.
10.
Zhang, Shasha, Dan Chen, Rajiv Ranjan, et al.. (2020). A lightweight solution to epileptic seizure prediction based on EEG synchronization measurement. The Journal of Supercomputing. 77(4). 3914–3932. 62 indexed citations
11.
Ke, Hengjin, Dan Chen, Benyun Shi, et al.. (2019). Improving Brain E-Health Services via High-Performance EEG Classification With Grouping Bayesian Optimization. IEEE Transactions on Services Computing. 13(4). 696–708. 58 indexed citations
12.
Ke, Hengjin, Dan Chen, Xiaoli Li, et al.. (2018). Towards Brain Big Data Classification: Epileptic EEG Identification With a Lightweight VGGNet on Global MIC. IEEE Access. 6. 14722–14733. 72 indexed citations
13.
Wang, Fengqin & Hengjin Ke. (2018). Global Epileptic Seizure Identification With Affinity Propagation Clustering Partition Mutual Information Using Cross-Layer Fully Connected Neural Network. Frontiers in Human Neuroscience. 12. 396–396. 3 indexed citations
14.
Ke, Hengjin, Dan Chen, Tejal Shah, et al.. (2018). Cloud‐aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN. Software Practice and Experience. 50(5). 596–610. 74 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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2026