Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

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

Showing 1-25 of 176 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
EEG-Based Emotion Recognition Using Regularized Graph Neural Networks
Peixiang Zhong, Di Wang, Chunyan Miao
IEEE Transactions on Affective Computing (2020) Vol. 13, Iss. 3, pp. 1290-1301
Open Access | Times Cited: 495
Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review
Abeer Al-Nafjan, Manar Hosny, Yousef Al-Ohali, et al.
Applied Sciences (2017) Vol. 7, Iss. 12, pp. 1239-1239
Open Access | Times Cited: 249
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: 243
EEG-based emotion recognition using an end-to-end regional-asymmetric convolutional neural network
Heng Cui, Aiping Liu, Xu Zhang, et al.
Knowledge-Based Systems (2020) Vol. 205, pp. 106243-106243
Closed Access | Times Cited: 210
Automated detection of schizophrenia using nonlinear signal processing methods
Jahmunah Vicnesh, Shu Lih Oh, V. Rajinikanth, et al.
Artificial Intelligence in Medicine (2019) Vol. 100, pp. 101698-101698
Closed Access | Times Cited: 199
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
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
Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain states
Alexander E. Hramov, Vladimir Maksimenko, Alexander N. Pisarchik
Physics Reports (2021) Vol. 918, pp. 1-133
Closed Access | Times Cited: 152
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
MS-MDA: Multisource Marginal Distribution Adaptation for Cross-Subject and Cross-Session EEG Emotion Recognition
Hao Chen, Ming Jin, Zhunan Li, et al.
Frontiers in Neuroscience (2021) Vol. 15
Open Access | Times Cited: 105
GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition
Yang Li, J.J. Chen, Fu Li, et al.
IEEE Transactions on Affective Computing (2022) Vol. 14, Iss. 3, pp. 2512-2525
Open Access | Times Cited: 81
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
Visibility Graph from Adaptive Optimal Kernel Time-Frequency Representation for Classification of Epileptiform EEG
Zhongke Gao, Qing Cai, Yuxuan Yang, et al.
International Journal of Neural Systems (2016) Vol. 27, Iss. 04, pp. 1750005-1750005
Closed Access | Times Cited: 162
An automatic detection of focal EEG signals using new class of time–frequency localized orthogonal wavelet filter banks
Manish Sharma, Abhinav Dhere, Ram Bilas Pachori, et al.
Knowledge-Based Systems (2016) Vol. 118, pp. 217-227
Closed Access | Times Cited: 158
Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns
Shih‐Cheng Liao, Chien‐Te Wu, Hao-Chuan Huang, et al.
Sensors (2017) Vol. 17, Iss. 6, pp. 1385-1385
Open Access | Times Cited: 143
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
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
Depression recognition using machine learning methods with different feature generation strategies
Xiaowei Li, Xin Zhang, Jing Zhu, et al.
Artificial Intelligence in Medicine (2019) Vol. 99, pp. 101696-101696
Closed Access | Times Cited: 122
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
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
Data mining EEG signals in depression for their diagnostic value
Mehdi Mohammadi, Fadwa Gamal Mohammed Al-Azab, Bijan Raahemi, et al.
BMC Medical Informatics and Decision Making (2015) Vol. 15, Iss. 1
Open Access | Times Cited: 97
A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer’s disease using EEG signals
Juan P. Amézquita-Sánchez, Nadia Mammone, Francesco Carlo Morabito, et al.
Journal of Neuroscience Methods (2019) Vol. 322, pp. 88-95
Closed Access | Times Cited: 97
Automated detection of abnormal EEG signals using localized wavelet filter banks
Manish Sharma, Sohamkumar Patel, U. Rajendra Acharya
Pattern Recognition Letters (2020) Vol. 133, pp. 188-194
Closed Access | Times Cited: 94
An Improved Empirical Mode Decomposition of Electroencephalogram Signals for Depression Detection
Jian Shen, Xiaowei Zhang, Gang Wang, et al.
IEEE Transactions on Affective Computing (2019) Vol. 13, Iss. 1, pp. 262-271
Open Access | Times Cited: 91
Page 1 - Next Page

Scroll to top