The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
In the citing paper
“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]
Citations
Cites
Diagnostic and Statistical Manual of Mental Disorders
American Psychiatric Association (2013) · doi:10.1176/appi.books.9780890425596 · Cited by 116,369
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565
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 28,665
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371
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
Applied Linear Statistical Models
Ziegel, E. R., Neter, J., Wasserman, W., & Kutner, M. (1992) · Technometrics · doi:10.2307/1269588 · Cited by 9,906
Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004) · The Annals of Statistics · doi:10.1214/009053604000000067 · Cited by 9,357
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
Diagnostic and Statistical Manual of Mental Disorders, 5th Edition
(2013) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9780890425596.893619 · Cited by 4,412
Research works printed but not linked: 4
- World Health Organization, Depression and other common mental disorders Global Health estimates. Geneva: WHO Document Production Services, 2017.
- Teplan, M., Fundamental of EEG measurement. In: Measurement Science Review, Vol 2, pp 1–11, (2002).
- Alelyani, S., Tang, J., Liu, H., Feature selection for clustering: A review.In: Reddy CK, Agarwal CC, data clustering: Algorithms and applications pp 1-12, (2013).
- Mahato, S., Roy, A., Verma, A., Paul, S., Analysis of feature selection and reduction techniques for detection of depression using EEG signals. International conference on computational intelligence and data analytics (ICCIDA-2018), Bhubaneswar, (2018).
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 521
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
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 197
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
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 110
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
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 104
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
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
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 98
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
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
Khosla, A., Khandnor, P., & Chand, T. (2021) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2021.12.005 · Cited by 89
Rafiei, A., Zahedifar, R., Sitaula, C., & Marzbanrad, F. (2022) · IEEE Access · doi:10.1109/access.2022.3190502 · Cited by 91
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 84
Khan, D. M., Yahya, N., Kamel, N., & Faye, I. (2021) · IEEE Access · doi:10.1109/access.2021.3049427 · Cited by 75
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
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
Elnaggar, K., El-Gayar, M. M., & Elmogy, M. M. (2025) · Diagnostics · doi:10.3390/diagnostics15020210 · Cited by 64
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
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 58
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
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
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 48
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 53
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
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
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
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
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
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
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 42
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
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
Sharma, C. M., & Chariar, V. M. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e32548 · Cited by 38
Mohammed, H., & Diykh, M. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104923 · Cited by 36
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
Deng, X., Fan, X., Lv, X., & Sun, K. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.914823 · Cited by 37
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
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
Ahmed, A., Misrani, A., Tabassum, S., Yang, L., & Long, C. (2021) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2021.05.028 · Cited by 32
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 31
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
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
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
Hassan, M., & Kaabouch, N. (2024) · Applied Sciences · doi:10.3390/app142210532 · Cited by 27
Singh, J., & Hamid, M. A. (2022) · Cognitive Computation · doi:10.1007/s12559-022-10042-2 · Cited by 28
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
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 29
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
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
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 28
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
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 24
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 23
Sakib, N., Islam, M. K., & Faruk, T. (2023) · Computational Intelligence and Neuroscience · doi:10.1155/2023/1701429 · Cited by 21
Yousufi, M., Damaševičius, R., & Maskeliūnas, R. (2024) · Brain Sciences · doi:10.3390/brainsci14101018 · Cited by 23
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
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 19
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
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
Gan, W., Zhao, R., Ma, Y., & Ning, X. (2025) · Bioengineering · doi:10.3390/bioengineering12020095 · Cited by 16
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
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
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
Mulc, D., Vukojević, J., Kalafatic, E., Cifrek, M., Vidović, D., & Jović, A. (2025) · Sensors · doi:10.3390/s25020409 · Cited by 14
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
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
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
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
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
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 11
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
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
Penava, P., & Buettner, R. (2024) · IEEE Access · doi:10.1109/access.2024.3502540 · Cited by 10
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
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
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
Mahler, R., & Reichenbach, A. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1595221 · Cited by 8
Rafiei, A., & Wang, Y. (2022) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci51031.2022.10022271 · Cited by 7
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
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
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
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
Sachdev, J. S., & Bhatnagar, R. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-91103-4_5 · Cited by 6
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
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
Alaqel, R., Hussain, M., & Al-Ahmadi, S. M. (2026) · Diagnostics · doi:10.3390/diagnostics16020363 · Cited by 5
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
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
Kaur, G., Aggarwal, H., & Goel, N. (2025) · Neuroscience · doi:10.1016/j.neuroscience.2025.07.010 · Cited by 5
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
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
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
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
EEG Analysis with Wavelet Transform under Music Perception Stimulation
Xue, J. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/9725762 · Cited by 4
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
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
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
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
Metadata
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Health Sciences › Medicine › Pharmacology (medical) › 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