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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:

A major depressive disorder classification framework based on EEG signals using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis
Reza Akbari Movahed, Gila Pirzad Jahromi, Shima Shahyad, et al.
Journal of Neuroscience Methods (2021) Vol. 358, pp. 109209-109209
Closed Access | Times Cited: 100

Showing 1-25 of 100 citing articles:

A Review on Mental Stress Assessment Methods Using EEG Signals
Rateb Katmah, Fares Al-Shargie, Usman Tariq, et al.
Sensors (2021) Vol. 21, Iss. 15, pp. 5043-5043
Open Access | Times Cited: 158
Alterations in EEG functional connectivity in individuals with depression: A systematic review
Aleksandra Miljevic, Neil W. Bailey, Oscar W. Murphy, et al.
Journal of Affective Disorders (2023) Vol. 328, pp. 287-302
Closed Access | Times Cited: 44
Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset
Chien‐Te Wu, Hao-Chuan Huang, Shiuan Huang, et al.
Biosensors (2021) Vol. 11, Iss. 12, pp. 499-499
Open Access | Times Cited: 58
EEG-based functional connectivity analysis of brain abnormalities: A systematic review study
Nastaran Khaleghi, Shaghayegh Hashemi, Mohammad Peivandi, et al.
Informatics in Medicine Unlocked (2024) Vol. 47, pp. 101476-101476
Open Access | Times Cited: 8
Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis
Ashima Khosla, Padmavati Khandnor, Trilok Chand
Journal of Applied Biomedicine (2021) Vol. 42, Iss. 1, pp. 108-142
Closed Access | Times Cited: 51
Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works
Parisa Moridian, Afshin Shoeibi, Marjane Khodatars, et al.
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (2022) Vol. 12, Iss. 6
Closed Access | Times Cited: 30
Landscape and future directions of machine learning applications in closed-loop brain stimulation
Anirudha S. Chandrabhatla, I. Jonathan Pomeraniec, Taylor M. Horgan, et al.
npj Digital Medicine (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 21
EEG-based major depressive disorder recognition by selecting discriminative features via stochastic search
Hongli Chang, Yuan Zong, Wenming Zheng, et al.
Journal of Neural Engineering (2023) Vol. 20, Iss. 2, pp. 026021-026021
Closed Access | Times Cited: 19
A gated temporal-separable attention network for EEG-based depression recognition
Lijun Yang, Zhaoran Wang, Xiangru Zhu, et al.
Computers in Biology and Medicine (2023) Vol. 157, pp. 106782-106782
Closed Access | Times Cited: 18
An End-to-End Deep Learning Model for EEG-Based Major Depressive Disorder Classification
Min Xia, Yangsong Zhang, Yihan Wu, et al.
IEEE Access (2023) Vol. 11, pp. 41337-41347
Open Access | Times Cited: 18
Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis
Zhiyi Chen, Xuerong Liu, Qingwu Yang, et al.
JAMA Network Open (2023) Vol. 6, Iss. 3, pp. e231671-e231671
Open Access | Times Cited: 17
Cross subject emotion identification from multichannel EEG sub-bands using Tsallis entropy feature and KNN classifier
Pragati Patel, Sivarenjani Balasubramanian, Ramesh Naidu Annavarapu
Brain Informatics (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 7
A machine learning approach for differentiating bipolar disorder type II and borderline personality disorder using electroencephalography and cognitive abnormalities
Mohammad-Javad Nazari, Mohammadreza Shalbafan, Negin Eissazade, et al.
PLoS ONE (2024) Vol. 19, Iss. 6, pp. e0303699-e0303699
Open Access | Times Cited: 6
Alterations in Patients With First-Episode Depression in the Eyes-Open and Eyes-Closed Conditions: A Resting-State EEG Study
Shuang Liu, Xiaoya Liu, Danfeng Yan, et al.
IEEE Transactions on Neural Systems and Rehabilitation Engineering (2022) Vol. 30, pp. 1019-1029
Open Access | Times Cited: 24
Machine learning approaches for diagnosing depression using EEG: A review
Yuan Liu, Changqin Pu, Shan Xia, et al.
Translational Neuroscience (2022) Vol. 13, Iss. 1, pp. 224-235
Open Access | Times Cited: 24
Improving EEG major depression disorder classification using FBSE coupled with domain adaptation method based machine learning algorithms
Hadeer Mohammed, Mohammed Diykh
Biomedical Signal Processing and Control (2023) Vol. 85, pp. 104923-104923
Open Access | Times Cited: 15
A Framework for Systematically Evaluating the Representations Learned by A Deep Learning Classifier from Raw Multi-Channel Electroencephalogram Data
Charles A. Ellis, Abhinav Sattiraju, Robyn L. Miller, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 13
M-Mdd: A Multi-Task Deep Learning Framework for Major Depressive Disorder Diagnosis Using Eeg
Yilin Wang, Sha Zhao, Haiteng Jiang, et al.
(2025)
Closed Access
Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning
Damir Mulc, Jakša Vukojević, Eda Jovičić, et al.
Sensors (2025) Vol. 25, Iss. 2, pp. 409-409
Open Access
Depression Detection and Diagnosis Based on Electroencephalogram (EEG) Analysis: A Systematic Review
Kholoud Elnaggar, M. M. El-Gayar, Mohammed Elmogy
Diagnostics (2025) Vol. 15, Iss. 2, pp. 210-210
Open Access
Research on Depression Recognition Model and Its Temporal Characteristics Based on Multiscale Entropy of EEG Signals
Xin Xu, Jiangnan Xu, Ruoyu Du, et al.
Entropy (2025) Vol. 27, Iss. 2, pp. 142-142
Open Access
Revealing patterns in major depressive disorder with machine learning and networks
Loriz Francisco Sallum, Caroline L. Alves, Thaise G. L. de O. Toutain, et al.
Chaos Solitons & Fractals (2025) Vol. 194, pp. 116163-116163
Open Access
M-MDD: A multi-task deep learning framework for major depressive disorder diagnosis using EEG
Yilin Wang, Sha Zhao, Haiteng Jiang, et al.
Neurocomputing (2025), pp. 130008-130008
Closed Access
Detection of Major Depressive Disorders in Children, Teens, and Young Adults
Rushil Patra, Ritej Dhamala, Sadhana Tiwari, et al.
Lecture notes in networks and systems (2025), pp. 25-37
Closed Access
Assessing human responses to construction noise using EEG and EDA signal features with Consideration of individual sensitivity
Sungjoo Hwang, Sungchan Lee, Meesung Lee, et al.
Applied Acoustics (2025) Vol. 236, pp. 110717-110717
Closed Access
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