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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:

Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD)
Wajid Mumtaz, Likun Xia, Syed Saad Azhar Ali, et al.
Biomedical Signal Processing and Control (2016) Vol. 31, pp. 108-115
Closed Access | Times Cited: 187

Showing 1-25 of 187 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
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 comparative analysis of signal processing and classification methods for different applications based on EEG signals
Ashima Khosla, Padmavati Khandnor, Trilok Chand
Journal of Applied Biomedicine (2020) Vol. 40, Iss. 2, pp. 649-690
Closed Access | Times Cited: 208
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
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
A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD)
Wajid Mumtaz, Syed Saad Azhar Ali, Mohd Azhar Mohd Yasin, et al.
Medical & Biological Engineering & Computing (2017) Vol. 56, Iss. 2, pp. 233-246
Closed Access | Times Cited: 163
A deep learning framework for automatic diagnosis of unipolar depression
Wajid Mumtaz, Abdul Qayyum
International Journal of Medical Informatics (2019) Vol. 132, pp. 103983-103983
Closed Access | Times Cited: 129
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
Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach
Çağlar Uyulan, Türker Tekin Ergüzel, Hüseyin Ünübol, et al.
Clinical EEG and Neuroscience (2020) Vol. 52, Iss. 1, pp. 38-51
Closed Access | Times Cited: 112
Classification of Depression Patients and Normal Subjects Based on Electroencephalogram (EEG) Signal Using Alpha Power and Theta Asymmetry
Shalini Mahato, Sanchita Paul
Journal of Medical Systems (2019) Vol. 44, Iss. 1
Closed Access | Times Cited: 109
EEG-based mild depression recognition using convolutional neural network
Xiaowei Li, Rong La, Ying Wang, et al.
Medical & Biological Engineering & Computing (2019) Vol. 57, Iss. 6, pp. 1341-1352
Closed Access | Times Cited: 107
Detection of major depressive disorder using linear and non-linear features from EEG signals
Shalini Mahato, Sanchita Paul
Microsystem Technologies (2018) Vol. 25, Iss. 3, pp. 1065-1076
Closed Access | Times Cited: 103
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: 99
Performance of machine learning methods applied to structural MRI and ADAS cognitive scores in diagnosing Alzheimer’s disease
Salim Lahmiri, Amir Shmuel
Biomedical Signal Processing and Control (2018) Vol. 52, pp. 414-419
Closed Access | Times Cited: 91
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
Machine Learning Approaches for MDD Detection and Emotion Decoding Using EEG Signals
Lijuan Duan, Huifeng Duan, Yuanhua Qiao, et al.
Frontiers in Human Neuroscience (2020) Vol. 14
Open 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
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
DepCap: A Smart Healthcare Framework for EEG Based Depression Detection Using Time-Frequency Response and Deep Neural Network
Geetanjali Sharma, Amit M. Joshi, Richa Gupta, et al.
IEEE Access (2023) Vol. 11, pp. 52327-52338
Open Access | Times Cited: 26
Automated major depressive disorder diagnosis using a dual-input deep learning model and image generation from EEG signals
Ahmad Afzali, Ali Khaleghi, Boshra Hatef, et al.
Waves in Random and Complex Media (2023), pp. 1-16
Closed Access | Times Cited: 22
Electroencephalography signals-based sparse networks integration using a fuzzy ensemble technique for depression detection
Surbhi Soni, Ayan Seal, Sraban Kumar Mohanty, et al.
Biomedical Signal Processing and Control (2023) Vol. 85, pp. 104873-104873
Closed Access | Times Cited: 22
DiffMDD: A Diffusion-Based Deep Learning Framework for MDD Diagnosis Using EEG
Yilin Wang, Sha Zhao, Haiteng Jiang, et al.
IEEE Transactions on Neural Systems and Rehabilitation Engineering (2024) Vol. 32, pp. 728-738
Open Access | Times Cited: 10
Depression Detection Using Relative EEG Power Induced by Emotionally Positive Images and a Conformal Kernel Support Vector Machine
Chien‐Te Wu, Daniel G. Dillon, Hao-Chun Hsu, et al.
Applied Sciences (2018) Vol. 8, Iss. 8, pp. 1244-1244
Open Access | Times Cited: 66
Functional connectivity of major depression disorder using ongoing EEG during music perception
Wenya Liu, Chi Zhang, Xiaoyu Wang, et al.
Clinical Neurophysiology (2020) Vol. 131, Iss. 10, pp. 2413-2422
Open Access | Times Cited: 63
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