Classification of depression patients and normal subjects based on electroencephalogram (EEG) signal using alpha power and theta asymmetry
Abstract
Depression or Major Depressive Disorder (MDD) is a mental illness which negatively affects how a person thinks, acts or feels. MDD has become a major disease affecting millions of people presently. The diagnosis of depression is questionnaire based and is not based on any objective criteria. In this paper, feature extracted from EEG signal are used for the diagnosis of depression. Alpha, alpha1, alpha2, beta, delta and theta power and theta asymmetry was used as feature. Alpha1, alpha2 along with theta asymmetry was also used as a feature. Multi-Cluster Feature Selection (MCFS) was used for feature selection when feature combination was used. The classifiers used were Support Vector Machine (SVM), Logistic Regression (LR), Naïve-Bayesian (NB) and Decision Tree (DT). Alpha2 showed higher classification accuracy than alpha1 and alpha power in all applied classifier. From t-test it was found that there was a significant difference in the theta power of left and right hemisphere of normal subjects, but there was no significant difference in depression patients. Average theta asymmetry in normal subjects is higher than MDD patients but the difference in theta asymmetry in normal subjects and MDD patients is not significant. The combination of alpha2 and theta asymmetry showed the highest classification accuracy of 88.33% in SVM.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Asymmetries in the alpha2 and theta bands, particularly when analyzed with a Support Vector Machine (SVM), achieved an accuracy rate of 88.33% for depression diagnosis.
“Notably, findings suggest that asymmetries in the alpha2 and theta bands, particularly when analyzed with a Support Vector Machine (SVM), lead to higher diagnostic precision, with an accuracy rate of 88.33%.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [129]
Cites
Diagnostic and Statistical Manual of Mental Disorders
American Psychiatric Association (2013) · doi:10.1176/appi.books.9780890425596 · Cited by 116,369 · see citing works
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565 · see citing works
Data mining: concepts and techniques
Sharma, R., Shah, K., Shirahatti, Y., Patel, S., Sharma, R., Shah, K., Patel, S., Sharma, R., & Han, J. (2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,655 · see citing works
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371 · see citing works
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162 · see citing works
Applied Linear Statistical Models
Ziegel, E. R., Neter, J., Wasserman, W., & Kutner, M. (1992) · Technometrics · doi:10.2307/1269588 · Cited by 9,906 · see citing works
Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004) · The Annals of Statistics · doi:10.1214/009053604000000067 · Cited by 9,357 · see citing works
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
Belkin, M., & Niyogi, P. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.003.0080 · Cited by 4,534 · see citing works
Diagnostic and Statistical Manual of Mental Disorders, 5th Edition
(2013) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9780890425596.893619 · Cited by 4,390 · see citing works
Unsupervised feature selection for multi-cluster data
Cai, D., Zhang, C., & He, X. (2010) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1835804.1835848 · Cited by 1,181 · see citing works
Hosseinifard, B., Moradi, M. H., & Rostami, R. (2012) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2012.10.008 · Cited by 560 · see citing works
Issues in the application of the average reference: Review, critiques, and recommendations
Dien, J. (1998) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03209414 · Cited by 430 · see citing works
A Pervasive Approach to EEG‐Based Depression Detection
Cai, H., Han, J., Chen, Y.-F., Sha, X., Wang, Z., Hu, B., Yang, J., Feng, L., Ding, Z., Chen, Y., et al. (2018) · Complexity · doi:10.1155/2018/5238028 · Cited by 302 · see citing works
A wavelet-based technique to predict treatment outcome for Major Depressive Disorder
Mumtaz, W., Xia, L., Yasin, M. A. M., Ali, S. S. A., & Malik, A. S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0171409 · Cited by 273 · see citing works
Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach
Roffo, G., Melzi, S., Castellani, U., & Vinciarelli, A. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.156 · Cited by 267 · see citing works
Mumtaz, W., Xia, L., Ali, S. S. A., Yasin, M. A. M., Hussain, M., & Malik, A. S. (2016) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2016.07.006 · Cited by 234 · see citing works
Grin-Yatsenko, V. A., Baas, I., Ponomarev, V. A., & Kropotov, J. D. (2009) · Clinical Neurophysiology · doi:10.1016/j.clinph.2009.11.015 · Cited by 212 · see citing works
Stewart, J. L., Coan, J. A., Towers, D. N., & Allen, J. J. B. (2014) · Psychophysiology · doi:10.1111/psyp.12191 · Cited by 167 · see citing works
Frontal alpha EEG asymmetry before and after behavioral activation treatment for depression
Gollan, J. K., Hoxha, D., Chihade, D., Pflieger, M. E., Rosebrock, L. E., & Cacioppo, J. T. (2014) · Biological Psychology · doi:10.1016/j.biopsycho.2014.03.003 · Cited by 149 · see citing works
Detection of major depressive disorder using linear and non-linear features from EEG signals
Mahato, S., & Paul, S. (2018) · Microsystem Technologies · doi:10.1007/s00542-018-4075-z · Cited by 141 · see citing works
Self-citationHan, J., Kamber, M., & Pei, J. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-381479-1.00008-3 · Cited by 101 · see citing works
Mahato, S., & Paul, S. (2018) · Lecture notes in electrical engineering · doi:10.1007/978-981-13-0776-8_30 · Cited by 72 · see citing works
Self-citationDecrease alpha waves in depression: An electroencephalogram(EEG) study
Kan, D. P. X., & Lee, P. F. (2015) · International Conference on BioSignal Analysis, Processing and Systems (ICBAPS) · doi:10.1109/icbaps.2015.7292237 · Cited by 63 · see citing works
Niemiec, A. J., & Lithgow, B. (2005) · doi:10.1109/iembs.2005.1616251 · Cited by 56 · see citing works
Roffo, G., & Melzi, S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-61461-8_2 · Cited by 48 · see citing works
Ranking to Learn: Feature Ranking and Selection via Eigenvector CentralityPreprint
Roffo, G., & Melzi, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.05409 · Cited by 38 · see citing works
Classifying depression patients and normal subjects using machine learning techniques
Hosseinifard, B., Moradi, M. H., & Rostami, R. (2011) · Iranian Conference on Electrical Engineering · Cited by 21 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques
Gedam, S., & Paul, S. (2021) · IEEE Access · doi:10.1109/access.2021.3085502 · Cited by 497 · see citing works
Self-citationA Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
Xu, X., Li, J., Zhu, Z., Zhao, L., Wang, H., Song, C., Chen, Y., Zhao, Q., Yang, J., & Pei, Y. (2024) · Bioengineering · doi:10.3390/bioengineering11030219 · Cited by 242 · see citing works
The applied principles of EEG analysis methods in neuroscience and clinical neurology
Zhang, H., Zhou, Q.-Q., Chen, H., Hu, X., Li, W., Bai, Y., Han, J., Wang, Y., Liang, Z., Chen, D., et al. (2023) · Military Medical Research · doi:10.1186/s40779-023-00502-7 · Cited by 185 · see citing works
Movahed, R. A., Jahromi, G. P., Shahyad, S., & Meftahi, G. H. (2021) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2021.109209 · Cited by 136 · see citing works
Dev, A., Roy, N., Islam, M. K., Biswas, C., Ahmed, H. U., Amin, M. A., Sarker, F., Vaidyanathan, R., & Mamun, K. A. (2022) · IEEE Access · doi:10.1109/access.2022.3146711 · Cited by 108 · see citing works
Loh, H. W., Ooi, C. P., Aydemir, E., Tuncer, T., Doğan, Ş., & Acharya, U. R. (2021) · Expert Systems · doi:10.1111/exsy.12773 · Cited by 104 · see citing works
Detection of Depression and Scaling of Severity Using Six Channel EEG Data
Mahato, S., Goyal, N., Ram, D., & Paul, S. (2020) · Journal of Medical Systems · doi:10.1007/s10916-020-01573-y · Cited by 102 · see citing works
Self-citationAn Improved Classification Model for Depression Detection Using EEG and Eye Tracking Data
Zhu, J., Wang, Z., Gong, T., Zeng, S., Li, X., Hu, B., Li, J., Sun, S., & Zhang, L. (2020) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2020.2990690 · Cited by 98 · see citing works
Shen, J. G., Zhang, Y., Liang, H., Zhao, Z., Zhu, K., Qian, K., Dong, Q., Zhang, X., & Hu, B. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3265805 · Cited by 98 · see citing works
Taşçı, G., Loh, H. W., Barua, P. D., Bayğın, M., Taşçı, B., Doğan, Ş., Tuncer, T., Palmer, E. E., Tan, R., & Acharya, U. R. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110190 · Cited by 97 · see citing works
Wu, C., Huang, H.-C., Huang, S., Chen, I., Liao, S., Chen, C., Lin, C., Lee, S., Chen, M., Tsai, C.-F., et al. (2021) · Biosensors · doi:10.3390/bios11120499 · Cited by 93 · see citing works
EEG based depression recognition using improved graph convolutional neural network
Zhu, J., Jiang, C., Chen, J., Lin, X., Yu, R., Li, X., & Hu, B. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105815 · Cited by 91 · see citing works
Khosla, A., Khandnor, P., & Chand, T. (2021) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2021.12.005 · Cited by 89 · see citing works
Rafiei, A., Zahedifar, R., Sitaula, C., & Marzbanrad, F. (2022) · IEEE Access · doi:10.1109/access.2022.3190502 · Cited by 88 · see citing works
An insight into diagnosis of depression using machine learning techniques: a systematic review
Bhadra, S., & Kumar, C. J. (2022) · Current Medical Research and Opinion · doi:10.1080/03007995.2022.2038487 · Cited by 81 · see citing works
Khan, D. M., Yahya, N., Kamel, N., & Faye, I. (2021) · IEEE Access · doi:10.1109/access.2021.3049427 · Cited by 75 · see citing works
Deep-Asymmetry: Asymmetry Matrix Image for Deep Learning Method in Pre-Screening Depression
Kang, M., Kwon, H., Park, J.-H., Kang, S., & Lee, Y. (2020) · Sensors · doi:10.3390/s20226526 · Cited by 71 · see citing works
Development of Wavelet Coherence EEG as a Biomarker for Diagnosis of Major Depressive Disorder
Khan, D. M., Masroor, K., Jailani, M. F. M., Yahya, N., Yusoff, M. Z., & Khan, S. M. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3143176 · Cited by 71 · see citing works
Elnaggar, K., El-Gayar, M. M., & Elmogy, M. M. (2025) · Diagnostics · doi:10.3390/diagnostics15020210 · Cited by 61 · see citing works
Sahu, R., Dash, S. R., Cacha, L. A., Poznański, R. R., & Parida, S. (2020) · Journal of Integrative Neuroscience · doi:10.31083/j.jin.2020.01.24 · Cited by 59 · see citing works
Liu, S., Liu, X., Yan, D., Chen, S., Liu, Y., Hao, X., Ou, W., Huang, Z., Su, F., He, F., et al. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3166824 · Cited by 56 · see citing works
EEG-Based Anxious States Classification Using Affective BCI-Based Closed Neurofeedback System
Chen, C., Yu, X., Belkacem, A. N., Lu, L., Li, P., Zhang, Z., Wang, X., Tan, W., Gao, Q., Shin, D., et al. (2021) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-020-00596-7 · Cited by 48 · see citing works
Computer-Aided Recognition Based on Decision-Level Multimodal Fusion for Depression
Zhang, B., Cai, H., Song, Y., Tao, L., & Li, Y. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3165640 · Cited by 47 · see citing works
Afzali, A., Khaleghi, A., Hatef, B., Movahed, R. A., & Jahromi, G. P. (2023) · Waves in Random and Complex Media · doi:10.1080/17455030.2023.2187237 · Cited by 47 · see citing works
Zhu, F., Zhang, J., Dang, R., Hu, B., & Wang, Q. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106996 · Cited by 47 · see citing works
EDT: An EEG-based attention model for feature learning and depression recognition
Ying, M., Shao, X., Zhu, J., Zhao, Q., Li, X., & Hu, B. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106182 · Cited by 46 · see citing works
Wang, B., Li, M., Haihambo, N. P., Qiu, Z., Sun, M., Guo, M., Zhao, X., & Han, C. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.03.145 · Cited by 46 · see citing works
Machine learning approaches for diagnosing depression using EEG: A review
Liu, Y., Pu, C., Xia, S., Deng, D., Wang, X., & Li, M. (2022) · Translational Neuroscience · doi:10.1515/tnsci-2022-0234 · Cited by 45 · see citing works
Quantum Machine-Based Decision Support System for the Detection of Schizophrenia from EEG Records
Aksoy, G., Cattan, G., Chakraborty, S., & Karabatak, M. (2024) · Journal of Medical Systems · doi:10.1007/s10916-024-02048-0 · Cited by 44 · see citing works
A novel EEG-based major depressive disorder detection framework with two-stage feature selection
Li, Y., Shen, Y., Fan, X., Huang, X., Yu, H., Zhao, G., & Ma, W. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01956-w · Cited by 43 · see citing works
Cui, W., Sun, M., Dong, Q., Guo, Y., Liao, X., & Li, Y. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3291950 · Cited by 42 · see citing works
Cross-Subject Classification of Depression by Using Multiparadigm EEG Feature Fusion
Yang, J., Zhang, Z., Fu, Z., Li, B., Xiong, P., & Liu, X. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107360 · Cited by 41 · see citing works
Yi, L., Xie, G., Li, Z., Li, X., Zhang, Y., Wu, K., Shao, G., Lv, B., Jing, H., Zhang, C., et al. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1205931 · Cited by 39 · see citing works
Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning
Xu, Y., Zhong, H., Ying, S., Liu, W., Chen, G.-B., Luo, X., & Li, G. (2023) · Sensors · doi:10.3390/s23208639 · Cited by 37 · see citing works
Sharma, C. M., & Chariar, V. M. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e32548 · Cited by 37 · see citing works
Mohammed, H., & Diykh, M. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104923 · Cited by 36 · see citing works
Spatial–Temporal EEG Fusion Based on Neural Network for Major Depressive Disorder Detection
Zhang, B., Wei, D., Yan, G., Li, X., Su, Y., & Cai, H. (2023) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-023-00567-x · Cited by 35 · see citing works
Deng, X., Fan, X., Lv, X., & Sun, K. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.914823 · Cited by 35 · see citing works
EEG Based Depression Recognition by Combining Functional Brain Network and Traditional Biomarkers
Sun, S., Chen, H., Shao, X., Liu, L., Li, X., & Hu, B. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313270 · Cited by 34 · see citing works
A Robust Deep-Learning Model to Detect Major Depressive Disorder Utilizing EEG Signals
Anik, I. A., Kamal, A. H. M., Kabir, M. A., Uddin, S., & Moni, M. A. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3394792 · Cited by 33 · see citing works
Ahmed, A., Misrani, A. A., Tabassum, S., Yang, L., & Long, C. (2021) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2021.05.028 · Cited by 31 · see citing works
Xu, M., Guo, P., Ma, Q., Zhou, X., Wei, Y., Wang, L., Chen, Y., & Guo, Y. (2022) · Journal of Chinese Integrative Medicine · doi:10.1016/j.joim.2022.05.002 · Cited by 30 · see citing works
Computer Aided Detection of Major Depressive Disorder (MDD) Using Electroencephalogram Signals
Khadidos, A. O., Alyoubi, K. H., Mahato, S., Khadidos, A. O., & Mohanty, S. N. (2023) · IEEE Access · doi:10.1109/access.2023.3262930 · Cited by 30 · see citing works
Self-citationMethod for Classifying Schizophrenia Patients Based on Machine Learning
Soria, C. G., Arroyo, Y., Torres, A., Redondo, M. Á., Basar, C., & Mateo, J. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12134375 · Cited by 30 · see citing works
Zhu, J., Yang, C., Xie, X., Wei, S., Li, Y., Li, X., & Hu, B. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3171782 · Cited by 29 · see citing works
Hassan, M., & Kaabouch, N. (2024) · Applied Sciences · doi:10.3390/app142210532 · Cited by 27 · see citing works
Singh, J., & Hamid, M. A. (2022) · Cognitive Computation · doi:10.1007/s12559-022-10042-2 · Cited by 27 · see citing works
Explainable Depression Recognition from EEG Signals via Graph Convolutional Network
Shen, J. G., Chen, J., Ma, Y., Cao, Z., Zhang, Y., & Hu, B. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10386011 · Cited by 27 · see citing works
A Hybrid Deep Learning Model for Predicting Depression Symptoms From Large-Scale Textual Dataset
Almutairi, S. S., Abohashrh, M., Razzaq, H. H., Zulqarnain, M., Namoun, A., & Khan, F. N. (2024) · IEEE Access · doi:10.1109/access.2024.3496741 · Cited by 26 · see citing works
Integration of Deep Learning for Improved Diagnosis of Depression using EEG and Facial Features
Hamid, D. S. B. A., Goyal, S., & Bedi, P. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.05.659 · Cited by 26 · see citing works
Wang, Z., Feng, J., Jiang, R., Shi, Y., Li, X., Xue, R., Du, X., Ji, M., Zhong, F., Meng, Y., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3197645 · Cited by 26 · see citing works
Analysis of region of interest (RoI) of brain for detection of depression using EEG signal
Mahato, S., & Paul, S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15827-7 · Cited by 26 · see citing works
Self-citationLin, H., Jian, C., Cao, Y., Ma, X., Wang, H., Miao, F., Fan, X., Yang, J., Zhao, G., & Zhou, H. D. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.105039 · Cited by 23 · see citing works
Machine Learning and Electroencephalogram Signal based Diagnosis of Depression
Khadidos, A. O., Alyoubi, K. H., Mahato, S., Khadidos, A. O., & Mohanty, S. N. (2023) · Neuroscience Letters · doi:10.1016/j.neulet.2023.137313 · Cited by 23 · see citing works
Self-citationWang, Y., Liu, J. Y., Chen, S., Zheng, C., Zou, X., & Zhou, Y. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.02.035 · Cited by 22 · see citing works
Sakib, N., Islam, M. K., & Faruk, T. (2023) · Computational Intelligence and Neuroscience · doi:10.1155/2023/1701429 · Cited by 21 · see citing works
Yousufi, M., Damaševičius, R., & Maskeliūnas, R. (2024) · Brain Sciences · doi:10.3390/brainsci14101018 · Cited by 19 · see citing works
Achieving EEG-based depression recognition using Decentralized-Centralized structure
Shao, X., Ying, M., Zhu, J., Li, X., & Hu, B. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106402 · Cited by 19 · see citing works
Li, H., Qin, W., Li, N., Feng, S., Wang, J., Zhang, Y., Wang, T., Wang, C., Cai, X., Sun, W., et al. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1124344 · Cited by 18 · see citing works
Xie, X.-M., Sha, S., Cai, H. X., Liu, X., Jiang, I., Zhang, L., & Wang, G. (2024) · Psychology Research and Behavior Management · doi:10.2147/prbm.s459954 · Cited by 18 · see citing works
DepML: An Efficient Machine Learning-Based MDD Detection System in IoMT Framework
Sharma, G., Joshi, A. M., & Pilli, E. S. (2022) · SN Computer Science · doi:10.1007/s42979-022-01250-6 · Cited by 18 · see citing works
Gan, W., Zhao, R., Ma, Y., & Ning, X. (2025) · Bioengineering · doi:10.3390/bioengineering12020095 · Cited by 16 · see citing works
EEG-based mild depression recognition using multi-kernel convolutional and spatial-temporal Feature
Fan, Y., Yu, R., Li, J., Zhu, J., & Li, X. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313499 · Cited by 16 · see citing works
Machine Learning Algorithms based Detection and Analysis of Stress - A Review
Ravi Raja, A., Jyothirmy, S., Geethika, G., Sai, S. B., Saiteja, B., & Reddy, V. H. P. (2023) · International Conference on Electronics and Renewable Systems (ICEARS) · doi:10.1109/icears56392.2023.10084933 · Cited by 15 · see citing works
Decoding Depression from Different Brain Regions Using Hybrid Machine Learning Methods
Sang, Q., Chen, C., & Shao, Z. (2025) · Bioengineering · doi:10.3390/bioengineering12050449 · Cited by 14 · see citing works
Mulc, D., Vukojević, J., Kalafatic, E., Cifrek, M., Vidović, D., & Jović, A. (2025) · Sensors · doi:10.3390/s25020409 · Cited by 13 · see citing works
The Role of Equilibrium between Free Radicals and Antioxidants in Depression and Bipolar Disorder
Kotzaeroglou, A., & Tsamesidis, I. (2022) · Medicines · doi:10.3390/medicines9110057 · Cited by 13 · see citing works
Movahed, R. A., Jahromi, G. P., Shahyad, S., & Meftahi, G. H. (2022) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-022-01135-1 · Cited by 13 · see citing works
Research on the Method of Depression Detection by Single-Channel Electroencephalography Sensor
Lei, X., Ji, W., Guo, J., Wu, X., Wang, H., Zhu, L., & Chen, L. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.850159 · Cited by 12 · see citing works
Yang, J., Li, J., Zhao, S., Zhang, Y., Li, B., & Liu, X. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106964 · Cited by 12 · see citing works
Neural quantification of timbre and emotions from Indian Classical Music: A multifractal exploration
Basu, M., Sanyal, S., Banerjee, A., Nag, S., Banerjee, K., Sengupta, R., & Ghosh, D. C. (2023) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2023.128937 · Cited by 11 · see citing works
A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network
Wang, R., Wang, H., Shi, L., Han, C., He, Q., Che, Y., & Li, L. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1160534 · Cited by 10 · see citing works
Al-shammari, M. K. M., Gao, T., Mohammed, R. K., & Zhou, S. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10159-2 · Cited by 10 · see citing works
The superiority verification of morphological features in the EEG-based assessment of depression
Wu, X., & Yang, J. (2022) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2022.109690 · Cited by 10 · see citing works
Penava, P., & Buettner, R. (2024) · IEEE Access · doi:10.1109/access.2024.3502540 · Cited by 10 · see citing works
Low channel electroencephalogram based deep learning method to pre-screening depression
Kang, M., Park, J., Kang, S., & Lee, Y. (2020) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc49870.2020.9289308 · Cited by 10 · see citing works
Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals
Taşçı, G., Barua, P. D., Tanko, D., Keleş, T., Tas, S., Sercek, I., Kaya, Ş., Yıldırım, K., Talu, Y., Taşçı, B., et al. (2025) · Diagnostics · doi:10.3390/diagnostics15020154 · Cited by 9 · see citing works
Revealing patterns in major depressive disorder with machine learning and networks
Sallum, L. F., Alves, C. L., Toutain, T. G. L. D. O., Porto, J. A. M., Thielemann, C., & Rodrigues, F. A. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.116163 · Cited by 9 · see citing works
Mahler, R., & Reichenbach, A. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1595221 · Cited by 8 · see citing works
Rafiei, A., & Wang, Y. (2022) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci51031.2022.10022271 · Cited by 7 · see citing works
Zhang, F., Yang, C., You, L., Wang, X., Yuan, Y., Jeannès, R. L. B., Shu, H., & Xiang, W. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3502843 · Cited by 7 · see citing works
Detection of Major Depressive Disorder using Signal Processing and Machine Learning Approaches
Saleque, S., Spriha, G.-A.-Z., Kamal, R. I., Khan, R. T., Chakrabarty, A., & Parvez, M. Z. (2020) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea48937.2020.9248419 · Cited by 7 · see citing works
González, L. L., Vaca, G. F., Figueroa, M., Estrella, A. E., González, E. G., Cadena-Morejón, C., Almeida-Galárraga, D. A., Tirado-Espín, A., Cruz-Varela, J., & Villalba-Meneses, F. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-32213-6_23 · Cited by 6 · see citing works
Li, Z., Tong, L., Zeng, Y., Gao, Y., Gong, D., Yang, K., Hu, Y., & Yan, B. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad9cc0 · Cited by 6 · see citing works
Sachdev, J. S., & Bhatnagar, R. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-91103-4_5 · Cited by 6 · see citing works
Mechanism of Depression through Brain Function Imaging of Depression Patients and Normal People
Tang, C., Zhang, Y., Zhai, Z., Zhu, X., Wang, C., & Yang, G. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/1125049 · Cited by 6 · see citing works
RetractedAn Automated MDD Detection System based on Machine Learning Methods in Smart Connected Healthcare
Sharma, G., Joshi, A. M., & Pilli, E. S. (2021) · IEEE International Symposium on Smart Electronic Systems (iSES) · doi:10.1109/ises52644.2021.00019 · Cited by 6 · see citing works
Alaqel, R., Hussain, M., & Al-Ahmadi, S. M. (2026) · Diagnostics · doi:10.3390/diagnostics16020363 · Cited by 5 · see citing works
Chatterjee, A., Bala, P. K., Gedam, S., Paul, S., & Goyal, N. (2023) · Aslib Journal of Information Management · doi:10.1108/ajim-10-2022-0468 · Cited by 5 · see citing works
Self-citationA generalized depression recognition framework based on cross-center and cross-task EEG signals
Liu, X., Qu, S., Luo, G., Yan, C., Wang, D., Chu, N., Tian, F., Zhu, J., Li, X., Sun, S., et al. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107492 · Cited by 5 · see citing works
Kaur, G., Aggarwal, H., & Goel, N. (2025) · Neuroscience · doi:10.1016/j.neuroscience.2025.07.010 · Cited by 5 · see citing works
Clinical tools to detect Postpartum Depression based on Machine learning and EEG: A Review
Acharya, A., Ramesh, R., Fathima, T. H. H. A. A., Lakhani, T., & Selva Kumar, S. (2023) · International Conference on Computational Systems and Communication (ICCSC) · doi:10.1109/iccsc56913.2023.10142970 · Cited by 5 · see citing works
A Novel EEG-Based Depression Detection Framework
Shen, Y., Xu, M., & Fan, X. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-06767-9_53 · Cited by 4 · see citing works
A Local-Global Graph Convolutional Network for Depression Recognition using EEG Signals
Chen, Y., Hu, X., & Xia, L. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140720 · Cited by 4 · see citing works
Prediction of Beck Depression Inventory Score in EEG: Application of Deep-Asymmetry Method
Kang, M., Kang, S., & Lee, Y. (2021) · Applied Sciences · doi:10.3390/app11199218 · Cited by 4 · see citing works
EEG Analysis with Wavelet Transform under Music Perception Stimulation
Xue, J. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/9725762 · Cited by 4 · see citing works
RetractedYang, C., Sun, Z., Zhang, F., Shu, H., Li, J., & Xiang, W. (2023) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc40787.2023.10340299 · Cited by 4 · see citing works
Alexithymia modulates emotion concept activation during facial expression processing
Yu, L., Wang, W., Li, Z., Ren, Y. L., Liu, J., Jiao, L., & Xu, Q. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae071 · Cited by 3 · see citing works
Mohanto, R., Shamim-Al-Mamun, M., Sufi, F. B., Ali, S. S., Alahmadi, T. J., Moni, M. A., & Islam, M. B. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115331 · Cited by 3 · see citing works
Depression Detection Based on Eeg Signals Using Machine Learning Algorithms
Alneqrat, F. M., Maamar, A. H., Mohammed, A. N., & Aghnaya, H. A. (2024) · IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA) · doi:10.1109/mi-sta61267.2024.10599680 · Cited by 3 · see citing works
- Authors
- Mahato, S., & Paul, S.
- Affiliations
- Birla Institute of Technology, Mesra (IN)
- Corresponding author
- Mahato, Shalini
- Venue
- Journal of Medical Systems
- Published
- 2019-12-13
- Volume
- 44
- Issue
- 1
- Pages
- 28-28
- ISSN
- 0148-5598 (ISSN-L)
- 1573-689X
- OpenAlex
- W2996393987
- Cited by
- 148
- References
- 28
- Citations by year
- 2026: 19 · 2025: 25 · 2024: 24 · 2023: 30 · 2022: 25 · 2021: 17 · 2020: 8
- FWCI
- 7.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Medical Systems (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
- Keywords
- major depressive disorder, depression diagnosis, EEG features, alpha power, theta power, feature selection, support vector machine, brain asymmetry
- MeSH terms
- Adult, Bayes Theorem, Depressive Disorder, Major, Major Depressive Disorder, Electroencephalography, Female, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Logistic Models, Support Vector Machine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
