A multi-modal open dataset for mental-disorder analysis

A rating scale for depression

Hamilton, M. (1960) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp.23.1.56 · Cited by 32,817

Untitled work

(1998) · The Journal of Clinical Psychiatry · Cited by 19,139

Journal of Clinical Psychiatry

(2018) · Journal of Clinical Psychiatry · doi:10.5505/kpd · Cited by 13,703

Untitled work

(1998) · PubMed · Cited by 10,216

Validation and Utility of a Self-report Version of PRIME-MD<SUBTITLE>The PHQ Primary Care Study</SUBTITLE>

Spitzer, R. L. (1999) · JAMA · doi:10.1001/jama.282.18.1737 · Cited by 9,802

Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015

Vos, T., Allen, C., Arora, M., Barber, R. M., Bhutta, Z. A., Brown, A., Carter, A., Casey, D. C., Charlson, F. J., Chen, A. Z., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(16)31678-6 · Cited by 7,253

A Rating Scale for Depression

Ostwald, P. F. (1960) · American Journal of Psychotherapy · doi:10.1176/appi.psychotherapy.1960.14.4.817 · Cited by 1,422

Mental health: a world of depression

Smith, K. (2014) · Nature · doi:10.1038/515180a · Cited by 1,148

Automated EEG-based screening of depression using deep convolutional neural network

Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., Adeli, H., & Subha, D. P. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.04.012 · Cited by 650

American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders (DSM-IV)

(2012) · SpringerReference · doi:10.1007/springerreference_179660 · Cited by 527

Depression Detection via Contextual Correlation Extractor and Individual-Level Clustering

Zhao, L., Xu, C., Li, W., Hou, H.-R., & Meng, Q. (2025) · Chinese Control Conference (CCC) · doi:10.23919/ccc64809.2025.11179266 · Cited by 1

A Hybrid Deep Learning Model (CNN-GRU) with MRMR for EEG-Based Depression Detection

Yousefi, M. R., Ebrahimzadeh, E., Al-Tamimi, H. I., Rajabion, L., & Dehghani, A. (2026) · Diagnostics · doi:10.21203/rs.3.rs-10781185/v1

[A method of mental disorder recognition based on visibility graph].

Zhang, B., Wei, D., Chang, W., Yang, Z., & Li, Y. (2023) · PubMed Central · doi:10.7507/1001-5515.202208077

Electroencephalogram Based Depression Detection Using Ensemble Approach

Mhaske, A., Nair, A., Nikam, A., Nikam, Y., & Mantri, S. (2023) · International Conference on Applied Intelligence and Sustainable Computing (ICAISC) · doi:10.1109/icaisc58445.2023.10201102

Editorial: Special Issue on Unobtrusive Physiological Measurement Methods for Affective Applications

Pavlidis, I. T., Chaspari, T., & McDuff, D. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3286769 · Cited by 1

EMAA: An Ensemble Multimodal Approach based on Auto-decoder for Depression Detection

Pan, Y., Jiang, J., Wang, C., Jie, J., & Yin, M. (2023) · International Conference on Communication and Information Processing (ICCIP) · doi:10.1145/3638884.3638921

Detection of Depression in EEG Signals Based on Convolutional Transformer and Adaptive Transfer Learning

Tan, Q., & Miao, M. (2024) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-71253-1_3

Joint Distribution-Aligned Dual-Sparse Linear Regression for Cross-Stimulus Speech-Based Depression Detection

Lu, Y., Lu, C., Zong, Y., Zhou, F., & Tang, C. (2024) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2024edl8054

Cross-Cultural Automatic Depression Detection Based on Audio Signals

Mamontov, D., Zepf, S., Karpov, A. A., & Minker, W. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-77961-9_23

Enhanced Depression Detection through Optimally Weighted Spectrogram Feature Fusion

Wu, P., Xu, F., & Lin, H. (2024) · International Conference on Computing and Pattern Recognition (ICCPR) · doi:10.1145/3704323.3704375

Benchmark image and clinical datasets for analysis in the medical system

Narayanan, S., Anbarasi, L. J., Sharon, J. J., Neeraja, R., & Gabriel, J. J. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-32862-6.00003-1

Diagnostic Study on Fusion of Spatio-Temporal Data Based on EST - ConvLSTM Model

Pan, Y., Kong, L., Huang, J., & Wu, J. (2024) · International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI) · doi:10.1109/cei63587.2024.10871917

Data Collection for Automatic Depression Identification in Spanish Speakers Using Deep Learning Algorithms: Protocol for a Case-Control Study

Brenes, L. F., Trejo, L. A., Cantoral-Ceballos, J. A., León, D. A. D., & Moreno, F. P. H. (2025) · JMIR Research Protocols · doi:10.2196/60439

Automated speech content analysis to detect depression with large language models: towards multilingual and few-shot capabilitiesPreprint

Riad, R., Ducorroy, A., Guessoum, S. B., Roquefort, F., Lesage, A., Cao, X.-N., & Bourla, A. (2025) · Research Square · doi:10.21203/rs.3.rs-6594999/v1

EEG Feature Selection-Based Depression Recognition Using Intensity and Direction-Guided Artificial Bee Colony Algorithm

Chen, G., Yan, N., & Zhang, X. (2024) · Asian Conference on Artificial Intelligence Technology (ACAIT) · doi:10.1109/acait63902.2024.11022142

A hybrid spiking neural framework for depression recognition with EEG signals

Zeng, H., Hu, B., Guan, Z., Zhang, B., & Chen, L. (2025) · IEEE Data Driven Control and Learning Systems Conference (DDCLS) · doi:10.1109/ddcls66240.2025.11065131

Global-Local Distribution Alignment for Cross-Dataset EEG-Based Major Depressive Disorder Detection

Li, Y., Zong, Y., Cheng, L., Wang, J., & Zheng, W. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-6957-8_14

CIDDA: Classifier-Driven Implicit Discriminator Domain Adaptation for EEG-Based Emotion Recognition and Depression Severity Grading

Hu, R., Yang, Z., Shan, J., Su, N., Tang, Y., Yan, H., Lv, X., Fu, D., Zhang, H., Jiang, T., et al. (2025) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2025.3595203

Brain Network Analysis and Recognition Algorithm for MDD Based on Class-Specific Correlation Feature Selection

Zhang, Z., Hu, Y., Lu, J., & Gao, Y. (2025) · Information · doi:10.3390/info16100912

Applying Parameter-Oriented Learning to Identify Statistical EEG Features Associated with Depression

Barkouk, S. B., Changizi, M., Kermani, M. Z., & Zeidabadi, A. A. (2025) · doi:10.1109/icee67339.2025.11213795

A Hybrid Graph Neural Network for Enhanced EEG-Based Depression Detection

Wang, Y., Zheng, W., Li, Y., & Yang, H. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227546 · Cited by 3

Sleep Staging Foundation Models Encode Neural Disorder-Related EEG Representations that Generalize to WakefulnessPreprint

Velez, X. A., Li, Q., Nasiri, S., Kiarashi, Y., & Clifford, G. D. (2025) · medRxiv · doi:10.1101/2025.11.15.25340316

A Multimodal Graph-Based Machine Learning Approach for Depression Detection

Campos, M. S. R., & Orjuela-Cañón, Á. D. (2025) · IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI) · doi:10.1109/colcaci67437.2025.11230904

Beyond Pairwise Connectivity: High-Order EEG Functional Network Representations Enhance Individualized Prediction of Depression Severity

Wang, D., Liu, J., Chu, N., Li, C., Liu, J., & Zhu, L. (2025) · ICME International Conference on Complex Medical Engineering (CME) · doi:10.1109/cme67420.2025.11239057

PPD Recognition based on Portable EEG Acquisition and Unsupervised Clustering Algorithm

Teng, L., Li, K., Li, R., Ga, Z., Guo, H., Zhao, Q., Peng, H., & Shen, J. (2025) · ICME International Conference on Complex Medical Engineering (CME) · doi:10.1109/cme67420.2025.11239048

EEG-induced Effective Connectivity Analysis in Major Depressive Disorder

Karmakar, S., Ghosh, L., & Guha, R. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11254429

Body sound denoising technologies: A survey and validation

Li, E., Zhang, H., Qian, K., Tian, F., Hu, B., Schuller, B. W., & Yamamoto, Y. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109302

Enhancing Depression Detection Accuracy with Time and Frequency Domain Features Extracted from EEG Signals

Delimayanti, M. K., Liliana, D. Y., Adi, R., Faisal, M. R., Azzahra, D. L., & Prastiwinarti, W. (2025) · Proceeding of the Electrical Engineering Computer Science and Informatics · doi:10.1109/eecsi67060.2025.11290252

Prompt-MAML: Model-Agnostic Meta-in-Context Learning for Major Depressive Disorder Classification

Lifelo, Z., Ding, J., Wang, Z., Shi, F., Ning, H., & Dhelim, S. (2025) · Tsinghua Science & Technology · doi:10.26599/tst.2025.9010116

Depression Detection in Speech Using Generative Models

Elghandour, L. A., Eltoweiry, A., Salem, M. A.-M., & Sharaf, N. (2025) · International Conference on Intelligent Computing and Information Systems (ICICIS) · doi:10.1109/icicis66182.2025.11313213

EEG-Based Mental Health Analysis and Classification for Healthy Brain Network(HBN)

Nikhara, A., Arya, A., Shridevi, S., & Won, D. (2025) · Innovations in Power and Advanced Computing Technologies (i-PACT) · doi:10.1109/i-pact65952.2025.11307890

EEG-Based Prediction of Major Depressive Disorder with Hybrid Machine Learning Models and Synthetic Data Augmentation

Campos, C. F. S., de Oliveira, J. F. L., Neto, F. B. L., & de Carvalho, J. G. R. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-10721-3_51

Kymata Soto Language Dataset: an electro-magnetoencephalographic dataset for natural speech processing

Yang, C., Parish, O., Klimovich‐Gray, A., Wingfield, C., Marslen‐Wilson, W. D., Zhang, C., Woolgar, A., & Thwaites, A. (2026) · Scientific Data · doi:10.1038/s41597-026-06579-8

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

Task-Dependent Effects of Channel Density on Multivariate Pattern Analysis and Spatial Representational Similarity Analysis: Evidence from Three EEG DatasetsPreprint

Hou, L., Zhang, G., Hao, Y., Kärkkäinen, T., & Cong, F. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.26.701731

A Novel Spatial-Temporal Graph Neural Network for Major Depressive Disorder Detection Based on Resting State EEG Signals

Sun, Y., Sun, J., Zhou, Z., Cai, J., Cui, W., & Liu, G. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3657750

Depression recognition from EEG based on nonlinear analysis and adaptive feature fusion

Wu, T., Liu, X., Zhang, C., & Chen, S. (2026) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2026.2626475

An EEG Depression Identification Model via Spatiotemporal Filtering

Wu, Z., Tan, H., zhang, Y., Zhang, H., & Xu, D. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-6123-0_34

Multimodal EEG study of emotional face processing in major depressive disorder

Zhang, Y., Wang, D., Zhu, L., Cao, Z., Zheng, Y., Wang, L., & An, C. (2026) · Behavioural Brain Research · doi:10.1016/j.bbr.2026.116107

CFSDBN: Emotion Recognition via Channel Feature Selection and Dynamic Brain Network

Du, J., Zhi, Y., Li, J., Wang, H., Guo, Y., & Xia, F. (2026) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2026.3667858

EEG and Speech Bimodal Fusion for Mental Disorder Recognition

Wang, X., Xing, B., Zhao, H., Li, W., Wei, J., & Song, X. (2025) · International Conference on Machine Learning, Computational Intelligence and Pattern Recognition (MLCIPR) · doi:10.1109/mlcipr68329.2025.11407285

DSMFF-UNet: A dual-stream U-Net network based on multimodal feature fusion for EEG depression recognition

Li, Y., & Yuan, L. (2025) · International Conference on Signal Processing, Computer Networks and Communications (SPCNC) · doi:10.1109/spcnc68200.2025.11406702

DisNet : Learning interpretable depression representations in speech

Yang, W., Cao, P., Wang, Y., Yang, J., & Wang, F. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.108820

F2FNet: An Efficient Affective State Analysis Network Reconstructing EEG From Few-Channel to Full-Channel

Xu, Z. J., Li, T., Chen, C. L. P., & Zhang, T. (2026) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2026.3671843

Speech Feature Extraction and Selection for Mental Health Analysis: Methods, Tools, and Challenges

Sun, Q., Zhang, X., Gao, Y., Li, Y., Li, H., & Li, X. (2025) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw69685.2025.00157

An Instrument for Measuring Depression From EEG Signals Using Attention-Enabled Residual-Based Convolutional Neural Network

Bhagat, N., Soni, S., Seal, A., Penhaker, M., & Krejcar, O. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3670517

Differentiating depression subtypes through three-electrode EEG and emotional stimuli: a machine learning approach

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

CFRAFN: A Cross-Feature Residual Attention Fusion Network for Major Depressive Disorder Prediction Using Clinical Voice Recordings

Pan, R., Feng, S., Sun, Y., Xu, J., Zhai, T., Wu, X., Tan, L., Yuan, Y., Cao, H., Maimaitimin, M., et al. (2026) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2026.3677472

Depression Severity Prediction Among Higher Education Students Using Neural Network Model

Agrawal, K., Kavitha Nair, R., & Kumar, D. V. (2026) · International Conference on Sentiment Analysis and Deep Learning (ICSADL) · doi:10.1109/icsadl67539.2026.11451970

ESNN: End-to-End Shuttle Neural Network for Major Depressive Disorder Recognition via Electroencephalographic Signals

Yang, C., You, L., Zhang, F., Sun, Z., Wang, X., Wang, Y., Jeannès, R. L. B., Shu, H., Xiang, W., & Yuan, Y. (2026) · IRBM · doi:10.1016/j.irbm.2026.100941

LightDepressionNet: A Lightweight Depression Diagnostic System Based on CNN-Transformer

Liu, X., Zhou, L., Zhang, W., Shao, Z., Liu, X., & Chen, C. (2025) · International Conference on Biomedical Engineering and Technology (ICBET) · doi:10.1109/icbet66094.2025.11470277

Efficient Multimodal Graph-Based Machine Learning for Depression Detection Using Feature Selection

Campos, M. S. R., & Orjuela-Cañón, A. D. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-20900-9_17

CTF-DANN: Improving Depression Detection via Domain Adaptation with Cross-Channel Temporal-Frequency Attention in EEG Signals

Lilin, L., Zhang, J., Yang, C., & Zhang, Y. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463504

A model for speech depression detection based on star operation and relative position encoding

Deng, Y., Zhao, L., Liu, Y., Xu, R., & Tu, Z. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110342

Blockchain‐Based Cognitive Health Monitoring Using Incremental Learning

Srivastava, S., Chamola, V., & Kumar, D. S. (2026) · Security and Privacy · doi:10.1002/spy2.70219

Dual-Branch Cross-Temporal Graph Neural Network for EEG-Based Depression Detection

Wang, Y., Zhao, T., Yang, H. S., Zhou, X., & Zheng, W. (2026) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2026.3691251

CDDDNet: A Cross-Domain Depression Detection Network Based on Transfer Learning

Liu, X., Zhou, L., Zhang, W., Shao, Z., Liu, X., & Chen, C. (2025) · International Conference on Biomedical and Bioinformatics Engineering (ICBBE) · doi:10.1145/3794209.3794284

EEGCo‐Diff: Manifold‐Guided Diffusion and an Oscillation‐Aware Classifier for Sample‐Efficient EEG Depression Detection

Huang, H., Li, X., Wu, M., Zhuang, G., Jin, J., & Lv, Z. (2026) · International Journal of Intelligent Systems · doi:10.1155/int/4496634

Comparative Performance Analysis of Machine Learning Methods on Depression Dataset

Gokhale, K. V., & Shelke, R. (2026) · International Conference on Intelligent Processing, Hardware, Electronics, and Radio Systems (CIPHER) · doi:10.1109/cipher70417.2026.11524040

$$\hbox {D}^{2}$$AC-Dep: dynamic adaptive feature fusion and domain adaptation collaboration for cross-subject depression detection

Huang, H., Li, X., Wu, M., Ouyang, R., Li, J. J., & Lv, Z. (2026) · Cognitive Neurodynamics · doi:10.1007/s11571-026-10474-8

Resting-state EEG microstates as biomarkers for major depressive disorder

Feng, Q., Sheng, T., Chen, H., Fan, Z., & Chen, Z. (2026) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmt-2025-0451

A Dual-Teacher Distillation Approach for EEG-based Assessment of Depression Severity

Li, Y. (2026) · International Conference on Image, Signal Processing and Pattern Recognition (ISPP) · doi:10.1109/ispp69262.2026.11542891

Research Progress and Trends of Intelligent Speech in Pathological Healthcare

Gao, Y., Wu, Y., Yang, F., Zhou, Y., Li, Y., & Wu, M. (2026) · Journal of Shanghai Jiaotong University (Science) · doi:10.1007/s12204-026-2943-8

A Multimodal LLM Framework Fusing EEG and Text for Personalized Depression Reporting

Su, X., Hu, B., & Guan, Z. (2026) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc69976.2026.11560166

Mining Sentiment Characteristics of the Elderly Based on Text Sentiment Analysis

Wei, W., Xue, Y., Wang, S., Zhou, W., & Qi, K. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-30833-7_51

Bayesian knowledge-guided confidence aware model based on multimodal pre-trained LLMs for depression detection

Wang, Z., Zhai, H., Jia, T., Wang, Z., Zhang, H., Qian, K., Xu, C., Zhang, Z., Xue, W., Qi, W., et al. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114388

A Comparative Analysis of EWT and EMD Techniques in the Diagnosis of Major Depressive Disorder Using EEG: Asymmetry Features and Explainability via SHAP

Gülenç, N. G., Koç, G., & Öztürk, M. (2026) · Diagnostics · doi:10.3390/diagnostics16132107

MentalCare: Contrastive Disentanglement and Positive Transfer for Generalizable Psychiatric Disorders Detection With a Wrist-Worn Device

Zhang, Y., Kuang, W., Wu, K., Huang, Z., Fan, C., & Zou, Y. (2026) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2026.3711501

Mental Depression Detection from Mandarin Speech

Alam, T., Koc, S., Espino, E. N., Dang, D., Hirschi, K., Quarles, J., & Flores, M. A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-20897-2_37

PSAGAN : Prior-guided sample-adaptive graph attention network with cross-domain alignment for EEG-based emotion recognition

Zhao, Y., Jiang, D., He, L., & Chen, H. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104594

Reproducibility of electroencephalography biomarkers for diagnosis of major depressive disorder

Hollenbenders, Y., Carrle, F. P., Mahler, R., Maier, C., & Reichenbach, A. (2026) · Aperture Neuro · doi:10.52294/001c.165175

A systematic review of multimodal depression detection: from technological pipelines to the clinical depression management cycle

Hong, Q., Zakaria, L. Q., & Tiun, S. B. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3935

Deep learning for EEG-based depression detection: A systematic literature review of methods and interpretability techniques

Raheem, K., Shafi, I., Ahmed, N., & Ashraf, I. (2026) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2026.111872

A Multimodal Deep Learning Based Framework for Early and Accurate Diagnosis of Depression Using Electroencephalography and Event‐Related Potential Signals

Attar, A. A., Moattar, M. H., & Forghani, Y. (2026) · Applied AI Letters · doi:10.1002/ail2.70042

Multilevel brain functional heterogeneity in major depressive disorder based on normative modeling of multicenter resting-state EEG

Sun, S., Guo, H., Kong, W., Wang, Z., Chen, H., Li, X., & Hu, B. (2026) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2026.111909

Analysis of EEG microstate transition patterns and multi-instance learning-based recognition model for depression

Zhang, W., Li, W., Fan, X., Wang, S., & Zou, L. (2026) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-026-03662-5

Speech-based early depressive symptom screening using MAX-Net: a multi-scale autoencoder-augmented xLSTM framework

Zheng, Z., Liang, L., Zhang, J., Wang, G., & Xue, C. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2026.1907554

Harmonic and Entropy Neural Modeling with Demographic Fusion for Speech-Based Depression Detection

Yang, J.-S., Nishizaki, H., Leow, C. S., Gan, L., & Liu, H. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38407-2_49

MindVoice: A Web-Based Multi-Dataset Speech Depression Screening System with Pipeline-Aware Grad-CAM Explainability

Choubey, A., Pandey, M. K., Dubey, A. K., & Rocha, Á. (2026) · Computers · doi:10.3390/computers15100646

MF-PNet: A Lightweight Multi-Frequency Progressive Neural Network for Depression Classification Deployed on Wearable EEG Sensors

Tian, F., Lv, R., Zhang, L., Jiang, H., Liu, J., Zhao, Q., & Hu, B. (2026) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2026.3737581

Acoustic Speech Features for Mental State Monitoring: Systematic Review (Preprint)Preprint

Stubbs, M., Thrash, J., Rahavi, S., Hartman, A., Ochieng, L., Hartman, A., Scafi, S., Devor, Y., & Shinar, Z. (2026) · doi:10.2196/preprints.113062

A Secure Remote Patient Monitoring IoMT Framework Based on Directed Acyclic Graph Blockchain

Sai, S., Kansal, K., & Chamola, V. (2026) · IEEE Internet of Things Magazine · doi:10.1109/miot.2026.3719633

A review on machine learning and deep learning approaches for depressive disorder diagnosis based on EEG and ERP signals

Attar, A. A., Moattar, M. H., & Kheirkhah, E. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02334-5

A brain functional network framework based on EEG connectivity for major depressive disorder recognition

Fang, M., Cao, T., Wu, J., Liu, H., 童基均, & Chen, Z. (2026) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2026.1930925

Analysis of major depressive disorder with EEG signals using adaptive signal processing and machine learning techniques

Lati̇foğlu, F., TURTUROVA, S., & Orhanbulucu, F. (2026) · Pamukkale University Journal of Engineering Sciences · doi:10.65206/pajes.25438

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

Assessment, intervention, and ethical considerations of artificial intelligence for depression and suicide risk: A scoping review

Cho, C., & Kim, Y. (2026) · Psychiatry Research · doi:10.1016/j.psychres.2026.117528

DSEA-Net: Multi-Dimensional Attention Fusion Framework for EEG-Based Depression Diagnosis

Zhou, T., Wang, X., Chen, J., Zhou, Z., Ling, Y., & Mao, K. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11729406

MKDC-IGN: An interpretable graph network with multi-layer key directed connections for the diagnosis of depression

Chu, N., Wang, N., Zhang, X., Liu, R., Sun, S., Hu, B., Wang, G., Liu, J., & Dong, Q. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.115183

GROUP-WISE SPATIAL DEPTHWISE CONVOLUTION AND INVERTED RESIDUALS FOR LIGHTWEIGHT EEG-BASED DEPRESSION SCREENING

Sui, P., Xia, M., & Ba, J. (2026) · International Conference on Biomedical and Intelligent Systems (IC-BIS) · doi:10.1145/3841905.3841913

A tutorial review of AI-powered physiological computing for mental health assessment: application scenarios, open issues, and current solutions

Zhang, Y.-F., Kuang, W.-T., Li, Y.-L., Cheng, Y.-Q., Wu, K.-S., & Zou, Y.-P. (2026) · CCF Transactions on Pervasive Computing and Interaction · doi:10.1007/s42486-026-00249-9

Multi-modal Open Dataset for Mental-disorder Analysis, Experimental Data 2014-2016

Hu, B. (2022) · TU/e Research Portal · doi:10.5255/ukda-sn-854301 · Cited by 2

MODMA dataset: a Multi-modal Open Dataset for Mental-disorder AnalysisPreprint

Cai, H., Gao, Y., Sun, S., Li, N., Tian, F., Xiao, H., Li, J., Yang, Z., Li, X., Zhao, Q., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.09283 · Cited by 97

Extraction and Analysis of Multi-modal Dataset for Mental Disorder Detection

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