Kai Keng Ang

11.9k total citations · 3 hit papers
193 papers, 8.5k citations indexed

About

Kai Keng Ang is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Signal Processing. According to data from OpenAlex, Kai Keng Ang has authored 193 papers receiving a total of 8.5k indexed citations (citations by other indexed papers that have themselves been cited), including 164 papers in Cognitive Neuroscience, 67 papers in Cellular and Molecular Neuroscience and 48 papers in Signal Processing. Recurrent topics in Kai Keng Ang's work include EEG and Brain-Computer Interfaces (161 papers), Neuroscience and Neural Engineering (67 papers) and Blind Source Separation Techniques (45 papers). Kai Keng Ang is often cited by papers focused on EEG and Brain-Computer Interfaces (161 papers), Neuroscience and Neural Engineering (67 papers) and Blind Source Separation Techniques (45 papers). Kai Keng Ang collaborates with scholars based in Singapore, United Kingdom and China. Kai Keng Ang's co-authors include Cuntai Guan, Zheng Yang Chin, Haihong Zhang, Chuanchu Wang, Chai Quek, Kok Soon Phua, Mahnaz Arvaneh, Karen Sui Geok Chua, Christopher Wee Keong Kuah and A. P. Vinod and has published in prestigious journals such as SHILAP Revista de lepidopterología, NeuroImage and Cancer.

In The Last Decade

Kai Keng Ang

190 papers receiving 8.3k citations

Hit Papers

Filter Bank Common Spatial Pattern (FBCSP) in Brain-Compu... 2008 2026 2014 2020 2008 2012 2014 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kai Keng Ang Singapore 42 7.4k 3.2k 1.7k 1.7k 1.5k 193 8.5k
Gernot Müller-Putz Austria 57 10.8k 1.5× 5.4k 1.7× 1.0k 0.6× 2.2k 1.3× 1.8k 1.2× 327 11.8k
Reinhold Scherer Austria 45 7.7k 1.0× 3.6k 1.1× 896 0.5× 1.5k 0.9× 1.4k 0.9× 164 8.3k
Fabien Lotte France 36 7.6k 1.0× 2.9k 0.9× 1.5k 0.9× 1.6k 0.9× 1.6k 1.1× 99 8.2k
Christa Neuper Austria 73 15.9k 2.2× 6.2k 2.0× 1.5k 0.9× 2.4k 1.4× 2.1k 1.3× 211 17.9k
Clemens Brunner Austria 32 5.2k 0.7× 2.2k 0.7× 940 0.5× 888 0.5× 984 0.6× 77 5.7k
Haihong Zhang Singapore 27 4.5k 0.6× 2.1k 0.7× 1.1k 0.6× 1.3k 0.8× 1.0k 0.7× 121 5.9k
Alois Schlögl Austria 38 8.5k 1.1× 3.5k 1.1× 2.2k 1.3× 1.3k 0.8× 1.5k 1.0× 84 9.4k
Gerwin Schalk United States 54 14.9k 2.0× 7.8k 2.5× 2.1k 1.2× 2.2k 1.3× 2.9k 1.9× 149 15.9k
Theresa M. Vaughan United States 30 12.9k 1.7× 7.3k 2.3× 2.1k 1.2× 3.2k 1.9× 2.5k 1.6× 42 13.4k
Marco Congedo France 37 6.9k 0.9× 2.1k 0.7× 1.3k 0.8× 1.1k 0.7× 1.1k 0.7× 98 7.9k

Countries citing papers authored by Kai Keng Ang

Since Specialization
Citations

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

Fields of papers citing papers by Kai Keng Ang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kai Keng Ang. 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 Kai Keng Ang. The network helps show where Kai Keng Ang may publish in the future.

Co-authorship network of co-authors of Kai Keng Ang

This figure shows the co-authorship network connecting the top 25 collaborators of Kai Keng Ang. A scholar is included among the top collaborators of Kai Keng Ang 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 Kai Keng Ang. Kai Keng Ang 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
2.
Gao, Weidong, et al.. (2025). FECT-OSA: A transformer-enhanced multimodal system for non-contact sleep apnea monitoring. Alexandria Engineering Journal. 128. 628–641. 1 indexed citations
3.
Phua, Kok Soon, et al.. (2024). Wearable EEG-Based Brain–Computer Interface for Stress Monitoring. SHILAP Revista de lepidopterología. 5(4). 407–428. 3 indexed citations
4.
Libedinsky, Camilo, et al.. (2024). Spike-Weighted Spiking Neural Network with Spiking Long Short-Term Memory: A Biomimetic Approach to Decoding Brain Signals. Algorithms. 17(4). 156–156. 1 indexed citations
5.
Chen, Yongming, et al.. (2023). A hierarchical dynamic Bayesian learning network for EMG-based early prediction of voluntary movement intention. Scientific Reports. 13(1). 4730–4730. 10 indexed citations
6.
Robinson, Neethu, et al.. (2023). Transferring a deep learning model from healthy subjects to stroke patients in a motor imagery brain–computer interface. Journal of Neural Engineering. 21(1). 16007–16007. 9 indexed citations
7.
Tint, Mya Thway, Marissa R. Lee, Peter D. Gluckman, et al.. (2023). Functional activity of the caudate mediates the relation between early childhood microstructural variations and elevated metabolic syndrome scores. NeuroImage. 278. 120273–120273. 1 indexed citations
9.
Nardi, Cosimo, et al.. (2023). Applications of Deep Learning to Neurodevelopment in Pediatric Imaging: Achievements and Challenges. Applied Sciences. 13(4). 2302–2302. 5 indexed citations
10.
11.
Cheng, Nicholas, Kok Soon Phua, Ka Yin Tang, et al.. (2020). Brain-Computer Interface-Based Soft Robotic Glove Rehabilitation for Stroke. IEEE Transactions on Biomedical Engineering. 67(12). 3339–3351. 110 indexed citations
12.
Fahimi, Fatemeh, Strahinja Došen, Kai Keng Ang, Natalie Mrachacz‐Kersting, & Cuntai Guan. (2020). Generative Adversarial Networks-Based Data Augmentation for Brain–Computer Interface. IEEE Transactions on Neural Networks and Learning Systems. 32(9). 4039–4051. 158 indexed citations
14.
Ang, Kai Keng, et al.. (2019). An iterative cross-subject negative-unlabeled learning algorithm for quantifying passive fatigue. Journal of Neural Engineering. 16(5). 56013–56013. 19 indexed citations
15.
Ang, Kai Keng, Chai Quek, Cuntai Guan, et al.. (2019). Assessment of the Efficacy of EEG-Based MI-BCI With Visual Feedback and EEG Correlates of Mental Fatigue for Upper-Limb Stroke Rehabilitation. IEEE Transactions on Biomedical Engineering. 67(3). 786–795. 141 indexed citations
16.
Mane, Ravikiran, Effie Chew, Kok Soon Phua, et al.. (2019). Prognostic and Monitory EEG-Biomarkers for BCI Upper-Limb Stroke Rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 27(8). 1654–1664. 68 indexed citations
17.
Phua, Kok Soon, et al.. (2019). Image-based Motor Imagery EEG Classification using Convolutional Neural Network. 1–4. 11 indexed citations
18.
Chouhan, Tushar, Neethu Robinson, A. P. Vinod, & Kai Keng Ang. (2017). Binary classification of hand movement directions from EEG using wavelet phase-locking. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 264–269. 8 indexed citations
19.
Ang, Kai Keng, et al.. (2011). Calibrating EEG-based motor imagery brain-computer interface from passive movement. PubMed. 2011. 4199–4202. 18 indexed citations
20.
Ang, Kai Keng, Cuntai Guan, Karen Sui Geok Chua, et al.. (2010). Clinical study of neurorehabilitation in stroke using EEG-based motor imagery brain-computer interface with robotic feedback. PubMed. 2010. 5549–5552. 169 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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