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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:

Electroencephalography-Based Depression Detection Using Multiple Machine Learning Techniques
Amel Ksibi, Mohammed Zakariah, Leila Jamel, et al.
Diagnostics (2023) Vol. 13, Iss. 10, pp. 1779-1779
Open Access | Times Cited: 31

Showing 1-25 of 31 citing articles:

A comprehensive review of predictive analytics models for mental illness using machine learning algorithms
Md. Monirul Islam, Shahriar Hassan, Sharmin Akter, et al.
Healthcare Analytics (2024) Vol. 6, pp. 100350-100350
Open Access | Times Cited: 6
A machine learning based depression screening framework using temporal domain features of the electroencephalography signals
Sheharyar Khan, Sanay Muhammad Umar Saeed, Jaroslav Frnda, et al.
PLoS ONE (2024) Vol. 19, Iss. 3, pp. e0299127-e0299127
Open Access | Times Cited: 5
Enhancing Depression Detection Using EEG Signals Through Adaptive Feature Weighting in Extra Trees Classifier
Challa Madhavi Latha, M. Kezia Joseph
Smart innovation, systems and technologies (2025), pp. 583-592
Closed Access
A Comparative Study of Machine Learning and Deep Learning Models for Automatic Parkinson’s Disease Detection from Electroencephalogram Signals
Swagota Bera, Zong Woo Geem, Young Im Cho, et al.
Diagnostics (2025) Vol. 15, Iss. 6, pp. 773-773
Open Access
Experimental Analysis of Employee Depression Identification System Using Modified Learning Procedure with AI Assistance
A. Megala˥, Ramchandra D. Patil, V.P. Gladis Pushparathi, et al.
(2024)
Closed Access | Times Cited: 4
Electrophysiological biomarkers based on CISANET characterize illness severity and suicidal ideation among patients with major depressive disorder
Yuchen Liang, Xuelin Gu, Yifan Shi, et al.
Medical & Biological Engineering & Computing (2025)
Closed Access
Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis
Haijun Lin, Jing Fang, Junpeng Zhang, et al.
Sensors (2024) Vol. 24, Iss. 21, pp. 6815-6815
Open Access | Times Cited: 2
Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder
Xuchen Qi, Wanxiu Xu, Gang Li
Brain Sciences (2023) Vol. 13, Iss. 9, pp. 1282-1282
Open Access | Times Cited: 5
EmotionNet: Dissecting Stress and Anxiety Through EEG-based Deep Learning Approaches
Yassine Daadaa
International Journal of Advanced Computer Science and Applications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 1
Deep Learning Analysis Approach for Major Depressive Disorder in Children and Adolescents
Amir Jahanian Najafabadi, Khaled Bagh
(2024)
Open Access | Times Cited: 1
Brain and Heart Rate Variability Patterns Recognition for Depression Classification of Mental Health Disorder
Qaisar Abbas, M. Emre Celebi, Talal Albalawi, et al.
International Journal of Advanced Computer Science and Applications (2024) Vol. 15, Iss. 7
Open Access | Times Cited: 1
Clinics to Algorithms Using Science and Technology
Amit Pimpalkar, Nisarg Gandhewar, Nilesh Shelke, et al.
Advances in medical technologies and clinical practice book series (2024), pp. 158-187
Closed Access
A Meta-Learning Framework with Model Stacking for Depression Detection Through Sentiment Classification
K. Remya, P. Ranjana
(2024), pp. 1-8
Closed Access
Depression recognition using voice-based pre-training model
Xiangsheng Huang, Fang Wang, Yuan Gao, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access
Detection of Mental Stress using EEG signals - Alpha, Beta, Theta, and Gamma Bands
Savita Bakare, Shruti Kuge, Siri Sugandhi, et al.
(2024), pp. 1-9
Closed Access
Enhancing Diagnosis Precision in Alcohol Addiction Detection Through CNN Analysis with SMOTE-ENN Data Augmentation
Mohamed Ashif H, E. Grace Mary Kanaga
(2024), pp. 520-526
Closed Access
Optimal segmentation of non-linear and non-stationary time series based on Poincare section and its application in solving EEG-signal
Zolfagharzadeh-Kermani Mahdi, Saeid Rashidi, Asaseh Maryam
Applied Soft Computing (2024) Vol. 166, pp. 112178-112178
Closed Access
PsyneuroNet architecture for multi-class prediction of neurological disorders
Kavita Rawat, Trapti Sharma
Biomedical Signal Processing and Control (2024) Vol. 100, pp. 107080-107080
Closed Access
PsyNet: A robust Deep Learning-based framework for Detecting Psychiatric Disorders using EEG signals
Prithwijit Mukherjee, D Biswas, Sagnik De, et al.
(2024), pp. 1-6
Closed Access
Revolutionizing Depression Diagnosis: The Synergy of EEG-based Cognitive Biomarkers and Machine Learning
Kiran Boby, Sridevi Veerasingam
Behavioural Brain Research (2024) Vol. 478, pp. 115325-115325
Closed Access
Stacked ensemble machine learning approach for electroencephalography based major depressive disorder classification using temporal statistics
N. Ahmed, Tejas Kadengodlu Bhat, Omkar S Powar
Systems Science & Control Engineering (2024) Vol. 12, Iss. 1
Open Access
Physiological signal analysis using explainable artificial intelligence: A systematic review
Jian Shen, Jinwen Wu, Huajian Liang, et al.
Neurocomputing (2024), pp. 128920-128920
Closed Access
Recent Advances in Big Medical Image Data Analysis Through Deep Learning and Cloud Computing
Mohammed Y. Shakor, Mustafa Ibrahim Khaleel
Electronics (2024) Vol. 13, Iss. 24, pp. 4860-4860
Open Access
Artificial Intelligence Technologies in Mental Health
Elvira Nurfadhilah, Ambar Yoganingrum, Andi Djalal Latief, et al.
Advances in computational intelligence and robotics book series (2024), pp. 219-262
Closed Access
The PDS6Net, a sequence model based on state-space theory, is utilized to classifying Chronic Primary Pain and depression
Lihua You, Banghua Yang, Zuowei Wang, et al.
(2024), pp. 129-134
Closed Access
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