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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:

The identification of children with autism spectrum disorder by SVM approach on EEG and eye-tracking data
Jiannan Kang, Xiaoya Han, Jiajia Song, et al.
Computers in Biology and Medicine (2020) Vol. 120, pp. 103722-103722
Closed Access | Times Cited: 170

Showing 1-25 of 170 citing articles:

Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review
Marjane Khodatars, Afshin Shoeibi, Delaram Sadeghi, et al.
Computers in Biology and Medicine (2021) Vol. 139, pp. 104949-104949
Closed Access | Times Cited: 219
Electroencephalography (EEG) Technology Applications and Available Devices
Mahsa Soufineyestani, Dale Dowling, Arshia Khan
Applied Sciences (2020) Vol. 10, Iss. 21, pp. 7453-7453
Open Access | Times Cited: 149
Application of data fusion for automated detection of children with developmental and mental disorders: A systematic review of the last decade
Smith K. Khare, Sonja March, Prabal Datta Barua, et al.
Information Fusion (2023) Vol. 99, pp. 101898-101898
Open Access | Times Cited: 78
The applied principles of EEG analysis methods in neuroscience and clinical neurology
Hao Zhang, Qing-Qi Zhou, He Chen, et al.
Military Medical Research (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 54
Diagnosis of autism spectrum disorder: a systematic review of clinical and artificial intelligence methods
Sahar Taneera, Reda Alhajj
Network Modeling Analysis in Health Informatics and Bioinformatics (2025) Vol. 14, Iss. 1
Closed Access | Times Cited: 2
Alzheimer’s disease diagnosis using rhythmic power changes and phase differences: a low-density EEG study
Juan Wang, Jiamei Zhao, Xiaoling Chen, et al.
Frontiers in Aging Neuroscience (2025) Vol. 16
Open Access | Times Cited: 2
A Multimodal Approach for Identifying Autism Spectrum Disorders in Children
Junxia Han, Guoqian Jiang, Gaoxiang Ouyang, et al.
IEEE Transactions on Neural Systems and Rehabilitation Engineering (2022) Vol. 30, pp. 2003-2011
Open Access | Times Cited: 53
An optimized Kernel Extreme Learning Machine for the classification of the autism spectrum disorder by using gaze tracking images
Angel Gaspar, Diego Oliva, Salvador Hinojosa, et al.
Applied Soft Computing (2022) Vol. 120, pp. 108654-108654
Closed Access | Times Cited: 44
Identification of autism spectrum disorder based on electroencephalography: A systematic review
Jing Li, Xiangjin Kong, Linlin Sun, et al.
Computers in Biology and Medicine (2024) Vol. 170, pp. 108075-108075
Closed Access | Times Cited: 17
Autism spectrum disorder diagnosis with EEG signals using time series maps of brain functional connectivity and a combined CNN–LSTM model
Yongjie Xu, Zengjie Yu, Yisheng Li, et al.
Computer Methods and Programs in Biomedicine (2024) Vol. 250, pp. 108196-108196
Open Access | Times Cited: 15
Driving Fatigue Detection Based on Hybrid Electroencephalography and Eye Tracking
Zequan Lian, Tao Xu, Zhen Yuan, et al.
IEEE Journal of Biomedical and Health Informatics (2024) Vol. 28, Iss. 11, pp. 6568-6580
Closed Access | Times Cited: 12
Decoding cognition in neurodevelopmental, psychiatric and neurological conditions with multivariate pattern analysis of EEG data
Gianluca Marsicano, Caterina Bertini, Luca Ronconi
Neuroscience & Biobehavioral Reviews (2024) Vol. 164, pp. 105795-105795
Open Access | Times Cited: 9
The emergence of artificial intelligence in autism spectrum disorder research: A review of neuro imaging and behavioral applications
i b, P. M. Durai Raj Vincent
Computer Science Review (2025) Vol. 56, pp. 100718-100718
Closed Access | Times Cited: 1
Machine learning in automated diagnosis of autism spectrum disorder: a comprehensive review
Khosro Rezaee
Computer Science Review (2025) Vol. 56, pp. 100730-100730
Closed Access | Times Cited: 1
Advanced sensors for smart healthcare: an introduction
Giovanni Diraco
Elsevier eBooks (2025), pp. 1-27
Closed Access | Times Cited: 1
Classification of Children With Autism and Typical Development Using Eye-Tracking Data From Face-to-Face Conversations: Machine Learning Model Development and Performance Evaluation
Zhong Zhao, Haiming Tang, Xiaobin Zhang, et al.
Journal of Medical Internet Research (2021) Vol. 23, Iss. 8, pp. e29328-e29328
Open Access | Times Cited: 56
Assessment of the Autism Spectrum Disorder Based on Machine Learning and Social Visual Attention: A Systematic Review
Maria Eleonora Minissi, Irene Alice Chicchi Giglioli, Fabrizia Mantovani, et al.
Journal of Autism and Developmental Disorders (2021) Vol. 52, Iss. 5, pp. 2187-2202
Open Access | Times Cited: 49
The Contribution of Machine Learning and Eye-Tracking Technology in Autism Spectrum Disorder Research: A Systematic Review
Konstantinos-Filippos Kollias, Christine K. Syriopoulou–Delli, Panagiotis Sarigiannidis, et al.
Electronics (2021) Vol. 10, Iss. 23, pp. 2982-2982
Open Access | Times Cited: 46
Machine learning based on eye-tracking data to identify Autism Spectrum Disorder: A systematic review and meta-analysis
Qiuhong Wei, Huiling Cao, Yuan Shi, et al.
Journal of Biomedical Informatics (2022) Vol. 137, pp. 104254-104254
Open Access | Times Cited: 38
AMPpred-EL: An effective antimicrobial peptide prediction model based on ensemble learning
Hongwu Lv, Ke Yan, Yichen Guo, et al.
Computers in Biology and Medicine (2022) Vol. 146, pp. 105577-105577
Closed Access | Times Cited: 30
Attention Measurement of an Autism Spectrum Disorder User Using EEG Signals: A Case Study
José Jaime Esqueda-Elizondo, Reyes Juárez‐Ramírez, Oscar Roberto López-Bonilla, et al.
Mathematical and Computational Applications (2022) Vol. 27, Iss. 2, pp. 21-21
Open Access | Times Cited: 29
A comparative assessment of most widely used machine learning classifiers for analysing and classifying autism spectrum disorder in toddlers and adolescents
Jyotismita Talukdar, Deba Kanta Gogoi, Thipendra P. Singh
Healthcare Analytics (2023) Vol. 3, pp. 100178-100178
Open Access | Times Cited: 22
Eye Tracking Biomarkers for Autism Spectrum Disorder Detection using Machine Learning and Deep Learning Techniques: Review
R. Asmetha Jeyarani, Radha Senthilkumar
Research in autism spectrum disorders (2023) Vol. 108, pp. 102228-102228
Open Access | Times Cited: 21
The classification of autism spectrum disorder by machine learning methods on multiple datasets for four age groups
Dhuha Dheyaa Khudhur, Saja Dheyaa Khudhur
Measurement Sensors (2023) Vol. 27, pp. 100774-100774
Open Access | Times Cited: 19
Resting-state EEG power differences in autism spectrum disorder: a systematic review and meta-analysis
Wei Siong Neo, Dan Foti, Brandon Keehn, et al.
Translational Psychiatry (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 19
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