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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:

DEPRESSION DIAGNOSIS SUPPORT SYSTEM BASED ON EEG SIGNAL ENTROPIES
Oliver Faust, Peng Chuan Alvin Ang, Subha D. Puthankattil, et al.
Journal of Mechanics in Medicine and Biology (2014) Vol. 14, Iss. 03, pp. 1450035-1450035
Closed Access | Times Cited: 115

Showing 1-25 of 115 citing articles:

Automated EEG-based screening of depression using deep convolutional neural network
U. Rajendra Acharya, Shu Lih Oh, Yuki Hagiwara, et al.
Computer Methods and Programs in Biomedicine (2018) Vol. 161, pp. 103-113
Closed Access | Times Cited: 536
Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis
Oliver Faust, U. Rajendra Acharya, Hojjat Adeli, et al.
Seizure (2015) Vol. 26, pp. 56-64
Open Access | Times Cited: 510
A Novel Depression Diagnosis Index Using Nonlinear Features in EEG Signals
U. Rajendra Acharya, Vidya K. Sudarshan, Hojjat Adeli, et al.
European Neurology (2015) Vol. 74, Iss. 1-2, pp. 79-83
Closed Access | Times Cited: 242
Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals
Betül Ay, Özal Yıldırım, Muhammed Talo, et al.
Journal of Medical Systems (2019) Vol. 43, Iss. 7
Closed Access | Times Cited: 238
A Pervasive Approach to EEG‐Based Depression Detection
Hanshu Cai, Jiashuo Han, Yun-Fei Chen, et al.
Complexity (2018) Vol. 2018, Iss. 1
Open Access | Times Cited: 233
A wavelet-based technique to predict treatment outcome for Major Depressive Disorder
Wajid Mumtaz, Likun Xia, Mohd Azhar Mohd Yasin, et al.
PLoS ONE (2017) Vol. 12, Iss. 2, pp. e0171409-e0171409
Open Access | Times Cited: 171
DeprNet: A Deep Convolution Neural Network Framework for Detecting Depression Using EEG
Ayan Seal, Rishabh Bajpai, Jagriti Agnihotri, et al.
IEEE Transactions on Instrumentation and Measurement (2021) Vol. 70, pp. 1-13
Closed Access | Times Cited: 161
EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks:A review
Sana Yasin, Syed Asad Hussain, Sinem Aslan, et al.
Computer Methods and Programs in Biomedicine (2021) Vol. 202, pp. 106007-106007
Open Access | Times Cited: 154
DepHNN: A novel hybrid neural network for electroencephalogram (EEG)-based screening of depression
Geetanjali Sharma, Abhishek Parashar, Amit M. Joshi
Biomedical Signal Processing and Control (2021) Vol. 66, pp. 102393-102393
Closed Access | Times Cited: 141
Computer-Aided Diagnosis of Depression Using EEG Signals
U. Rajendra Acharya, Vidya K. Sudarshan, Hojjat Adeli, et al.
European Neurology (2015) Vol. 73, Iss. 5-6, pp. 329-336
Open Access | Times Cited: 176
Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach
Abdolkarim Saeedi, Maryam Saeedi, Arash Maghsoudi, et al.
Cognitive Neurodynamics (2020) Vol. 15, Iss. 2, pp. 239-252
Open Access | Times Cited: 126
An automated diagnosis of depression using three-channel bandwidth-duration localized wavelet filter bank with EEG signals
Manish Sharma, P.V. Achuth, Dipankar Deb, et al.
Cognitive Systems Research (2018) Vol. 52, pp. 508-520
Closed Access | Times Cited: 123
Nonlinear analysis of EEGs of patients with major depression during different emotional states
Saime Akdemir Akar, Sadık Kara, Sümeyra Agambayev, et al.
Computers in Biology and Medicine (2015) Vol. 67, pp. 49-60
Closed Access | Times Cited: 112
Depression recognition based on the reconstruction of phase space of EEG signals and geometrical features
Hesam Akbari, Muhammad Tariq Sadiq, Ateeq Ur Rehman, et al.
Applied Acoustics (2021) Vol. 179, pp. 108078-108078
Closed Access | Times Cited: 77
EEG-based deep learning model for the automatic detection of clinical depression
Pristy Paul Thoduparambil, Anna Dominic, Surekha Mariam Varghese
Physical and Engineering Sciences in Medicine (2020) Vol. 43, Iss. 4, pp. 1349-1360
Closed Access | Times Cited: 72
Decision support system for major depression detection using spectrogram and convolution neural network with EEG signals
Hui Wen Loh, Chui Ping Ooi, Emrah Aydemir, et al.
Expert Systems (2021) Vol. 39, Iss. 3
Closed Access | Times Cited: 72
Major depressive disorder assessment via enhanced k-nearest neighbor method and EEG signals
Maryam Saeedi, Abdolkarim Saeedi, Arash Maghsoudi
Physical and Engineering Sciences in Medicine (2020) Vol. 43, Iss. 3, pp. 1007-1018
Closed Access | Times Cited: 70
Classification of normal and depressed EEG signals based on centered correntropy of rhythms in empirical wavelet transform domain
Hesam Akbari, Muhammad Tariq Sadiq, Ateeq Ur Rehman
Health Information Science and Systems (2021) Vol. 9, Iss. 1
Open Access | Times Cited: 69
Depression Detection Based on Geometrical Features Extracted from SODP Shape of EEG Signals and Binary PSO
Hesam Akbari, Muhammad Tariq Sadiq, Malih Payan, et al.
Traitement du signal (2021) Vol. 38, Iss. 1, pp. 13-26
Open Access | Times Cited: 69
Exploration of EEG-Based Depression Biomarkers Identification Techniques and Their Applications: A Systematic Review
Antora Dev, N. Roy, Md. Kafiul Islam, et al.
IEEE Access (2022) Vol. 10, pp. 16756-16781
Open Access | Times Cited: 56
Machine learning based approaches for clinical and non-clinical depression recognition and depression relapse prediction using audiovisual and EEG modalities: A comprehensive review
Sana Yasin, Alice Othmani, Imran Raza, et al.
Computers in Biology and Medicine (2023) Vol. 159, pp. 106741-106741
Closed Access | Times Cited: 30
Analysis of the Complexity Measures in the EEG of Schizophrenia Patients
Saime Akdemir Akar, Sadık Kara, Fatma Lati̇foğlu, et al.
International Journal of Neural Systems (2015) Vol. 26, Iss. 02, pp. 1650008-1650008
Closed Access | Times Cited: 90
Nonlinear analysis of EEG complexity in episode and remission phase of recurrent depression
Milena Čukić, Miodrag Stokić, Slavoljub Radenković, et al.
International Journal of Methods in Psychiatric Research (2019) Vol. 29, Iss. 2
Open Access | Times Cited: 61
Prediction of Depression Severity Scores Based on Functional Connectivity and Complexity of the EEG Signal
Yousef Mohammadi, Mohammad Hassan Moradi
Clinical EEG and Neuroscience (2020) Vol. 52, Iss. 1, pp. 52-60
Closed Access | Times Cited: 56
Continuous Scoring of Depression From EEG Signals via a Hybrid of Convolutional Neural Networks
Sara Hashempour, Reza Boostani, Mokhtar Mohammadi, et al.
IEEE Transactions on Neural Systems and Rehabilitation Engineering (2022) Vol. 30, pp. 176-183
Open Access | Times Cited: 35
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