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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Identification of mental fatigue levels in a language understanding task based on multi-domain EEG features and an ensemble convolutional neural network
Chunhua Ye, Zhong Yin, Mengyuan Zhao, et al.
Biomedical Signal Processing and Control (2021) Vol. 72, pp. 103360-103360
Closed Access | Times Cited: 17

Showing 17 citing articles:

Application of Artificial Intelligence Techniques for Brain–Computer Interface in Mental Fatigue Detection: A Systematic Review (2011–2022)
Hamwira Yaacob, Farhad Hossain, Sharunizam Shari, et al.
IEEE Access (2023) Vol. 11, pp. 74736-74758
Open Access | Times Cited: 31
TFormer: A time–frequency Transformer with batch normalization for driver fatigue recognition
Ruilin Li, Minghui Hu, Ruobin Gao, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102575-102575
Closed Access | Times Cited: 11
CubicPat: Investigations on the Mental Performance and Stress Detection Using EEG Signals
Uğur İnce, Yunus Talu, Aleyna Duz, et al.
Diagnostics (2025) Vol. 15, Iss. 3, pp. 363-363
Open Access | Times Cited: 1
Air traffic controllers' mental fatigue recognition: A multi-sensor information fusion-based deep learning approach
Xiaoqing Yu, Chun‐Hsien Chen, Haohan Yang
Advanced Engineering Informatics (2023) Vol. 57, pp. 102123-102123
Closed Access | Times Cited: 17
Depression assessment using integrated multi-featured EEG bands deep neural network models: Leveraging ensemble learning techniques
Kuo‐Hsuan Chung, Yue‐Shan Chang, Wei-Ting Yen, et al.
Computational and Structural Biotechnology Journal (2024) Vol. 23, pp. 1450-1468
Open Access | Times Cited: 3
Fatigue Detection of Air Traffic Controllers Based on Radiotelephony Communications and Self-Adaption Quantum Genetic Algorithm Optimization Ensemble Learning
Nan Wu, Jingjuan Sun
Applied Sciences (2022) Vol. 12, Iss. 20, pp. 10252-10252
Open Access | Times Cited: 14
A spectral-ensemble deep random vector functional link network for passive brain–computer interface
Ruilin Li, Ruobin Gao, Ponnuthurai Nagaratnam Suganthan, et al.
Expert Systems with Applications (2023) Vol. 227, pp. 120279-120279
Open Access | Times Cited: 8
Complexity of the instantaneous frequency variation in auditory steady-state response: A high sensitivity, high anti-interference index of mental fatigue
Yan Li, Shengyi Zhou, Chi Tang, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102564-102564
Closed Access | Times Cited: 2
EEG spatial inter-channel connectivity analysis: A GCN-based dual stream approach to distinguish mental fatigue status
Kun Chen, Shulong Chai, Tianli Xie, et al.
Artificial Intelligence in Medicine (2024) Vol. 157, pp. 102996-102996
Closed Access | Times Cited: 2
Physiological records-based situation awareness evaluation under aviation context: A comparative analysis
Jun Chen, Anqi Chen, Bingkun Jiang, et al.
Heliyon (2024) Vol. 10, Iss. 5, pp. e26409-e26409
Open Access | Times Cited: 1
A New Method for Detecting the Fatigue Using Automated Deep Learning Techniques for Medical Imaging Applications
Naveen Sundar Gnanadesigan, G. A. Lincoln, Narmadha Dhanasegar, et al.
Wireless Personal Communications (2024) Vol. 135, Iss. 2, pp. 1009-1034
Closed Access | Times Cited: 1
A Multimodal Feature Fusion Framework for Sleep-Deprived Fatigue Detection to Prevent Accidents
Jitender Singh Virk, Mandeep Singh, Mandeep Singh, et al.
Sensors (2023) Vol. 23, Iss. 8, pp. 4129-4129
Open Access | Times Cited: 3
Cross-subject and cross-experimental classification of mental fatigue based on two-stream self-attention network
Shuo Yang, Aoyang Shan, Lei Wang, et al.
Biomedical Signal Processing and Control (2023) Vol. 88, pp. 105638-105638
Closed Access | Times Cited: 3
Looseness Identification of Track Fasteners Based on Ultra-Weak FBG Sensing Technology and Convolutional Autoencoder Network
Sheng Li, Liang Jin, Jinpeng Jiang, et al.
Sensors (2022) Vol. 22, Iss. 15, pp. 5653-5653
Open Access | Times Cited: 5
Non-visual Effects Driven Fatigue Level Recognition Method for Enclosed Space Workers
Xian Zhang, Yuan Feng, Jingluan Wang, et al.
Lecture notes in computer science (2024), pp. 172-185
Closed Access
HRV Related to Mental Fatigue Obtained Based on MZI-BCG Cushion
Liufeng Zhu, Yifei Feng, Yi Liu, et al.
(2024) Vol. 93, pp. 1-2
Closed Access
Assessment of Construction Workers’ Spontaneous Mental Fatigue Based on Non-Invasive and Multimodal In-Ear EEG Sensors
Xin Fang, Heng Li, Jie Ma, et al.
Buildings (2024) Vol. 14, Iss. 9, pp. 2793-2793
Open Access
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