Study on feature selection methods for depression detection using three-electrode EEG data
Abstract
The early diagnosis of depression is important to the treatment of this condition, whereas a timely diagnosis can reduce the incidence of mortality caused in patients with depression. In the present study, we collected the EEG signals of Fp2, Fpz and Fp1, compared with 128 channels EEG, a simpler test (3 channels EEG) can make diagnosis more accessible and widespread, researchers can perform more tests on more patients given the same amount of time and money. The difference between the depressed and the non-depressed patients was explored by the linear and non-linear characteristics of these EEG signals. A total of 152 patients with depression and 113 healthy subjects participated in the study. In the current report, the linear features were as follows: peak, variance, inclination, kurtosis and Hjorth parameter. The nonlinear features included C0 complexity, correlation dimension, Shannon entropy, Kolmogorov entropy and power spectrum entropy. With regard to the aforementioned characteristics, the present report utilized four feature selection algorithms, namely WrapperSubsetEval, CorrelationAttributeEval, GainRatioAttributeEval, and PrincipalComponents and five classification algorithms that included Support Vector Machine, K-Nearest Neighbor, Decision Tree, Logistics Regression and Random Forest. The experimental results indicated that the WrapperSubsetEval of the wrapper class exhibited higher performance compared with the other three feature selection algorithms on each classifier, whereas the highest classification accuracy was 76.4. It is suggested that this analysis may be a complementary tool to aid psychiatrists in the diagnosis of depressed patients.
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?
The data preparation phase is critical for achieving optimal classification accuracy in EEG-based depression diagnosis.
“Through rigorous data preprocessing and feature extraction—identifying noise types and harnessing both linear and nonlinear features—the critical role of the data preparation phase is emphasized for achieving optimal classification accuracy.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [131]
Cites
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
Psychometric properties of the Beck Depression Inventory: Twenty-five years of evaluation
Beck, A. T., Steer, R. A., & Carbin, M. G. (1988) · Clinical Psychology Review · doi:10.1016/0272-7358(88)90050-5 · Cited by 11,696 · see citing works
Text categorization with Support Vector Machines: Learning with many relevant features
Joachims, T. (1998) · Lecture notes in computer science · doi:10.1007/bfb0026683 · Cited by 8,134 · see citing works
The ten-twenty electrode system of the international federation
Jasper, H. H. (1958) · Electroencephalography and Clinical Neurophysiology · Cited by 6,567 · see citing works
Nearest neighbor (NN) norms: NN pattern classification techniques
Dasarathy, B. V. (1991) · Medical Entomology and Zoology · Cited by 1,649 · see citing works
EEG analysis based on time domain properties
Hjorth, B. (1970) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(70)90143-4 · Cited by 1,504 · see citing works
The American Psychiatric Association at Washington
(1930) · Psychiatric Quarterly · doi:10.1007/bf01579058 · Cited by 771 · see citing works
Diagnostic and Statistical Manual of Mental Disorders (DSM)
Cooper, R. (2017) · KNOWLEDGE ORGANIZATION · doi:10.5771/0943-7444-2017-8-668 · Cited by 770 · 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
Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines
Nicolaou, N., & Georgiou, J. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.07.008 · Cited by 524 · see citing works
Applying the unscented Kalman filter for nonlinear state estimation
Kandepu, R., Foss, B. A., & Imsland, L. (2008) · Journal of Process Control · doi:10.1016/j.jprocont.2007.11.004 · Cited by 496 · see citing works
EEG power, frequency, asymmetry and coherence in male depression
Knott, V., Mahoney, C., Kennedy, S. H., & Evans, K. (2001) · Psychiatry Research Neuroimaging · doi:10.1016/s0925-4927(00)00080-9 · Cited by 409 · see citing works
Olbrich, S., & Arns, M. (2013) · International Review of Psychiatry · doi:10.3109/09540261.2013.816269 · Cited by 371 · see citing works
Lehmann, C., Koenig, T., Jelic, V., Prichep, L., John, R. E., Wahlund, L.-O., Dodge, Y., & Dierks, T. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2006.10.023 · Cited by 324 · see citing works
A simple KNN algorithm for text categorization
Soucy, P., & Mineau, G. W. (2002) · Proceedings · doi:10.1109/icdm.2001.989592 · Cited by 229 · 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
EEG-based mild depressive detection using feature selection methods and classifiers
Li, X., Hu, B., Sun, S., & Cai, H. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.08.010 · Cited by 189 · see citing works
Self-citationEEG Power Spectra at Early Stages of Depressive Disorders
Grin-Yatsenko, V. A., Baas, I., Ponomarev, V. A., & Kropotov, J. D. (2009) · Journal of Clinical Neurophysiology · doi:10.1097/wnp.0b013e3181c298fe · Cited by 147 · see citing works
Artifact and Recording Concepts in EEG
Tatum, W. O., Dworetzky, B. A., & Schomer, D. L. (2011) · Journal of Clinical Neurophysiology · doi:10.1097/wnp.0b013e31821c3c93 · Cited by 146 · see citing works
Gaonkar, B., & Davatzikos, C. A. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.03.066 · Cited by 121 · see citing works
Support vector EEG classification in the Fourier and time-frequency correlation domains
Garcia, G. N., Ebrahimi, T., & Vésin, J.-M. (2004) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/cne.2003.1196897 · Cited by 92 · see citing works
Complexity analysis of spontaneous EEG
Bhattacharya, J. (2000) · Acta Neurobiologiae Experimentalis · doi:10.55782/ane-2000-1369 · Cited by 92 · see citing works
Pervasive EEG diagnosis of depression using Deep Belief Network with three-electrodes EEG collector
Cai, H., Sha, X., Han, X., Wei, S., & Hu, B. (2016) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2016.7822696 · Cited by 89 · see citing works
Self-citationCuingnet, R., Rosso, C., Marie Chupin, A., Lehéricy, S., Dormont, D., Benali, H., Samson, Y., & Colliot, O. (2011) · Medical Image Analysis · doi:10.1016/j.media.2011.05.007 · Cited by 85 · see citing works
Neural Network Approaches to Grade Adult Depression
Chattopadhyay, S., Kaur, P., Rabhi, F., & Acharya, U. R. (2011) · Journal of Medical Systems · doi:10.1007/s10916-011-9759-1 · Cited by 32 · see citing works
More normal EEGs of depression patients during mental arithmetic than rest
Li, Y., Li, Y., Tong, S., Tang, Y., & Zhu, Y. (2007) · Joint Meeting of International Symposium on Noninvasive Functional Source Imaging of Brain and Heart and International Conference on Functional Biomedical Imaging · doi:10.1109/nfsi-icfbi.2007.4387716 · Cited by 27 · see citing works
ABST M-Type K-Nearest Neighbor (ABSTM-KNN) for Image Denoising
Gallegos-Funes, F. J. (2005) · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · doi:10.1093/ietfec/e88-a.3.798 · Cited by 15 · see citing works
Weak Target Detection Based on the Average Bandwidth of the Partial Hilbert Spectrum
Zhang, J. A., Guan, J., Dong, Y., & He, Y. (2012) · JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY · doi:10.3724/sp.j.1146.2011.00091 · Cited by 5 · see citing works
Li, N., Hu, B., Chen, J., Peng, H., Zhao, Q., & Zhao, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-42042-9_64 · Cited by 3 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Depression biomarkers using non-invasive EEG: A review
Neto, F. S. D. A., & Rosa, J. L. G. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.07.021 · Cited by 456 · see citing works
A 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
Self-citationHasib, K. M., Islam, M. R., Sakib, S., Akbar, M. A., Razzak, I., & Alam, M. S. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3263128 · Cited by 147 · 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
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
Self-citationWu, 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
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
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
Elnaggar, K., El-Gayar, M. M., & Elmogy, M. M. (2025) · Diagnostics · doi:10.3390/diagnostics15020210 · Cited by 61 · see citing works
Minimal EEG channel selection for depression detection with connectivity features during sleep
Zhang, Y., Wang, K., Wei, Y., Guo, X., Wen, J., & Luo, Y. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105690 · Cited by 53 · see citing works
Quantifying Feature Importance for Detecting Depression using Random Forest
AlSagri, H. S., & Ykhlef, M. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110577 · Cited by 52 · see citing works
Wang, B., Kang, Y., Huo, D., Chen, D., Song, W., & Zhang, F. (2022) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2022.111582 · 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
Self-citationJui, S., Deo, R., Barua, P., Devi, A., Soar, J., & Acharya, U. R. (2023) · IEEE Access · doi:10.1109/access.2023.3294473 · Cited by 45 · see citing works
Ahmed, T., Qassem, M., & Kyriacou, P. A. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103591 · Cited by 44 · 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
Tian, F., Zhang, H., Tan, Y., Zhu, L., Shen, L., Qian, K., Hu, B., Schuller, B. W., & Yamamoto, Y. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3487012 · Cited by 44 · see citing works
Self-citationFeature-level fusion based on spatial-temporal of pervasive EEG for depression recognition
Zhang, B., Wei, D., Yan, G., Lei, T., Cai, H., & Yang, Z. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107113 · Cited by 39 · see citing works
Self-citationA Pilot Study on Electroencephalogram-based Evaluation of Visually Induced Motion Sickness
Liu, R., Xu, M., Zhang, Y., Eli Peli, A., & Hwang, A. D. (2020) · Journal of Imaging Science and Technology · doi:10.2352/j.imagingsci.technol.2020.64.2.020501 · Cited by 35 · see citing works
Uyulan, Ç., de la Salle, S., Erguzel, T. T., Lynn, E., Blier, P., Knott, V., Adamson, M. M., Zelka, M., & Tarhan, N. (2021) · Clinical EEG and Neuroscience · doi:10.1177/15500594211018545 · Cited by 35 · see citing works
Qiao, Y., Zhou, J., Liang, J., Deng, X., Zhang, Z., Huang, H., Li, S., Dai, S.-F., Liu, C., Luan, Z., et al. (2020) · Phytomedicine · doi:10.1016/j.phymed.2020.153436 · Cited by 34 · see citing works
Zhang, B., Zhou, W., Cai, H., Su, Y., Wang, J., Zhang, Z., & Lei, T. (2020) · IEEE Access · doi:10.1109/access.2020.2994985 · Cited by 25 · see citing works
Self-citationDepression evaluation based on prefrontal EEG signals in resting state using fuzzy measure entropy
Chen, F., Zhao, L., Li, B., & Yang, L. (2020) · Physiological Measurement · doi:10.1088/1361-6579/abb144 · Cited by 25 · 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
Li, G., Zhong, H., Wang, J., Yang, Y., Li, H., Wang, S., Sun, Y., & Qi, X. (2023) · Brain Sciences · doi:10.3390/brainsci13030384 · Cited by 21 · see citing works
Physiological Electrosignal Asynchronous Acquisition Technology: Insight and Perspectives
Hu, B., Zhu, L., Dong, Q., Kun, Q., Cai, H., & Tian, F. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3350958 · Cited by 21 · see citing works
Self-citationNaulegari, J., & Kumaresh, N. (2022) · Traitement du signal · doi:10.18280/ts.390109 · Cited by 22 · see citing works
Wang, B., Kang, Y., Huo, D., Feng, G., Zhang, J., & Li, J. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.1029298 · Cited by 20 · see citing works
Xu, Z. J., Chen, C. L. P., & Zhang, T. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3527459 · Cited by 17 · see citing works
Mahato, S., Paul, S., Goyal, N., Mohanty, S. N., & Jain, S. (2022) · Recent Patents on Engineering · doi:10.2174/1872212117666220801105612 · Cited by 16 · see citing works
Mental fatigue assessment by an arbitrary channel EEG based on morphological features and LSTM-CNN
Wu, X., Yang, J., Shao, Y., & Chen, X. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107652 · Cited by 16 · see citing works
Machine Learning-Based Prediction of Depressive Disorders via Various Data Modalities: A Survey
Li, Q., Liu, X., Hu, X., Ahad, M. A. R., Ren, M., Yao, L., & Huang, Y. (2025) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2025.125393 · Cited by 17 · see citing works
Spatio-temporal features based deep learning model for depression detection using two electrodes
Choudhary, S., Bajpai, M. K., & Bharti, K. K. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad4dc0 · Cited by 11 · see citing works
Zhao, Q., Jiang, H., Wu, Z., Zhang, L., Cui, K., Zheng, K., Liu, J., Cai, R., Zhao, M., Tian, F., et al. (2025) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2025.3608181 · Cited by 11 · see citing works
Self-citationThe 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
Alzate, M., Torres, R., la Roca, J. D., Quintero-Zea, A., & Hernandez, M. (2024) · Applied Sciences · doi:10.3390/app14188312 · Cited by 10 · see citing works
Pan, Y., Jie, J., & Yin, M. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107879 · Cited by 9 · see citing works
Wosiak, A., & Dura, A. (2020) · Sensors · doi:10.3390/s20247083 · Cited by 8 · see citing works
Li, X., Hu, X., Zhang, S., Dai, Z., Zhou, H., Chen, Z., Yao, Z., & Lü, Q. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.10.092 · Cited by 7 · see citing works
Sethi, S. R., & Mahadik, D. A. (2025) · Journal of Chinese Economic and Business Studies · doi:10.1080/14765284.2025.2549237 · Cited by 7 · see citing works
Kopańska, M., Ochojska, D., Sarzyńska, I., Bartkowska, O., & Szczygielski, J. (2025) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2025.1624434 · Cited by 5 · see citing works
A novel fast detection algorithm for depression based on 3-channel EEG signals
Guo, X., Guo, Z., & Xie, T. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1651762 · Cited by 4 · see citing works
Artificial Intelligence Technologies in Mental Health
Nurfadhilah, E., Yoganingrum, A., Latief, A. D., Widyasuri, A., Jarin, A., Afra, D. I. N., Gunarso, G., Komariah, K. S., Uliniansyah, M. T., Untariyati, N. A., et al. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-7011-7.ch011 · Cited by 5 · see citing works
Artificial intelligence–based depression detection using EEG signals
Tokmak, F., & Subaşı, A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-22308-2.00007-x · Cited by 4 · see citing works
A multi-source window-dependent transfer learning approach for COVID-19 vaccination rate prediction
Altarawneh, L., Agarwal, A., Yang, Y., & Jin, Y. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109037 · Cited by 4 · see citing works
A 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
Self-citationWang, B., Hu, X., Mei, T. X., Jian, S. D., & Wang, W. (2020) · Journal of Vibration and Control · doi:10.1177/1077546320954958 · Cited by 4 · see citing works
Al-Qazzaz, N. K., Ali, S. H. M., & Ahmad, S. A. (2023) · Springer eBooks · doi:10.1007/978-3-031-23239-8_14 · Cited by 3 · see citing works
A multi-source behavioral and physiological recording system for cognitive assessment
Wang, Z.-Y., Liu, L., & Liu, Y. (2023) · Scientific Reports · doi:10.1038/s41598-023-35289-z · Cited by 4 · see citing works
Hybrid ANFIS Model For Depression Recognition Consuming Three-Channel EEG Data
Dhabliya, D., Bhuvaneshwari, K. S., Kapoor, P., Sowmiya, S., Garg, A., & Yashoda, L. (2024) · International Conference on Recent Advances in Science and Engineering Technology (ICRASET) · doi:10.1109/icraset63057.2024.10895792 · Cited by 2 · see citing works
Brain Network Analysis Based on Frontal Cortical EEG Sources in Major Depressive Disorder (MDD)
Amirkhan, N., Hasanzadeh, F., Mohebbi, M., & Rostami, R. (2021) · National and International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme54433.2021.9750285 · Cited by 2 · see citing works
Baseline resting EEG measures differentiate rTMS treatment responders and non-respondersPreprint
Kaewpijit, P., Fitzgerald, P. B., Hoy, K. E., & Bailey, N. W. (2023) · medRxiv · doi:10.1101/2023.11.16.23298445 · Cited by 2 · see citing works
Al-Qazzaz, N. K., Ali, S. H. M., & Ahmad, S. A. (2024) · Springer eBooks · doi:10.1007/978-3-031-52856-9_14 · Cited by 1 · see citing works
Ansari, N., Khan, Y. U., & Farooq, O. (2025) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ae2689 · Cited by 1 · see citing works
Luo, X., Xu, Y., Yan, Z., Liu, W. K., Zhou, B., Li, G. Y., & Zhu, Y. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1624997 · Cited by 1 · see citing works
Depression Diagnosis Using Linear Features Based on EEG Signals
Naregalkar, P. R., Shinde, A. A., & Patil, M. V. (2023) · International Conference on Computational Intelligence, Networks and Security (ICCINS) · doi:10.1109/iccins58907.2023.10450119 · Cited by 2 · see citing works
Convolutional Neural Network Based Approach for Depression Detection through EEG Signals
Kumari, K., Dewangan, K. K., & Sahu, R. (2023) · International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI) · doi:10.1109/icaiihi57871.2023.10489298 · Cited by 1 · see citing works
Using Electroencephalography for Diagnosing and Treating Depression
Malik, A. S., & Mumtaz, W. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-817420-3.00005-9
Enhanced classification of depression through eeg and hybrid anfis-ga systems
Mankar, P., Pimple, J., Kewate, A., Pathan, S., Titarmare, S., & Roy, S. (2025) · OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON) · doi:10.1109/otcon65728.2025.11070695
Anxiety or Depression Prediction of MDD in Children Using Ensemble Learning: A Systematic Review
Anju, C. R., & Duela, J. S. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-3105-6_20
CoDD: Convenient-Oriented Depression Detection from Few-Channel EEG with Channel Reconstruction
Xu, Z., Chen, C. L. P., & Zhang, T. (2025) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc58881.2025.11343693
Efficient Depression Detection Through Two EEG Electrodes with Wave-SpatialNet
Choudhary, S., Bajpai, M. K., & Bharti, K. K. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-10250-8_1
Dynamic Evolution of Prefrontal Neural Activity in Depression: A Bayesian Probability Model
Cui, K., Wang, C., Ming, J., Tian, F., Zhao, Q., & Zhao, M. (2026) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2026.3668219
Liang, G., Zhang, X., Yang, Z., Wang, Y., Liao, D. K., Guo, J., Zhang, L., & Xu, X. (2026) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ae55a7
Self-citationShirazi, D. M., Mohebbi, M., Abolfathi, Y., & Rostami, R. (2026) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-026-01975-y
Gülenç, N. G., Koç, G., & Öztürk, M. (2026) · Diagnostics · doi:10.3390/diagnostics16132107
A data-driven approach to depression detection and assessment using minimal brain signal sensing
Vaniya, S. N., Habib, A., Angelova, M., & Karmakar, C. K. (2026) · Healthcare Analytics · doi:10.1016/j.health.2026.100489
AOR: adaptive orthogonal regression for depression feature selection with incomplete EEG channels
Dong, W., Gong, Z., Xu, X., Liu, K., Wei, F., & Zhuo, L. (2026) · Journal of Neural Engineering · doi:10.1088/1741-2552/aeb32f
- Authors
- Cai, H., Chen, Y., Han, J., Zhang, X., & Hu, B.
- Affiliations
- Lanzhou University (CN)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Hu, Bin
- Venue
- Interdisciplinary Sciences: Computational Life Sciences
- Published
- 2018-05-04
- Volume
- 10
- Issue
- 3
- Pages
- 558-565
- ISSN
- 1867-1462 (ISSN-L)
- 1913-2751
- OpenAlex
- W2799687171
- Cited by
- 68
- References
- 30
- Citations by year
- 2026: 8 · 2025: 13 · 2024: 11 · 2023: 10 · 2022: 13 · 2021: 4 · 2020: 7 · 2019: 2
- FWCI
- 1.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Interdisciplinary Sciences Computational Life Sciences (publishedVersion)
- PubMed (publishedVersion)
- Lanzhou University Institutional Repository (submittedVersion)
- 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
- depression detection, feature selection, wrapper-based feature selection, nonlinear EEG features, Hjorth parameters, correlation dimension, Shannon entropy, Kolmogorov-Sinai entropy, power spectrum entropy, EEG complexity, early detection of depression, psychiatric diagnosis
- MeSH terms
- Algorithms, Depression, Electrodes, Electroencephalography, Humans, Entropy, 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