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MDD Patients and Healthy Controls EEG Data

nm000114 · 32 high-confidence citations

  1. Decision support system for major depression detection using spectrogram and convolution neural network with <scp>EEG</scp> signals

    Hui Wen Loh, Chui Ping Ooi, Emrah Aydemir, Türker Tuncer, Şengül Doğan, U. Rajendra Acharya · 2021 · Expert Systems

    Cites dataset 100 citations

  2. Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis

    Ashima Khosla, Padmavati Khandnor, Trilok Chand · 2021 · Journal of Applied Biomedicine

    Cites dataset 82 citations

  3. Deep-Asymmetry: Asymmetry Matrix Image for Deep Learning Method in Pre-Screening Depression

    Min Kang, Hyunjin Kwon, Jinhyeok Park, Seokhwan Kang, Youngho Lee · 2020 · Sensors

    Cites dataset 71 citations

  4. Automated major depressive disorder detection using melamine pattern with EEG signals

    Emrah Aydemir, Türker Tuncer, Şengül Doğan, Raj Gururajan, U. Rajendra Acharya · 2021 · Applied Intelligence

    Cites dataset 59 citations

  5. Benchmarks for machine learning in depression discrimination using electroencephalography signals

    Ayan Seal, Rishabh Bajpai, Mohan Karnati, Jagriti Agnihotri, Anis Yazidi, Enrique Herrera‐Viedma, Ondřej Krejcar · 2022 · Applied Intelligence

    Cites dataset 44 citations

  6. DiffMDD: A Diffusion-Based Deep Learning Framework for MDD Diagnosis Using EEG

    Yilin Wang, Sha Zhao, Haiteng Jiang, Shijian Li, Benyan Luo, Tao Li, Gang Pan · 2024 · IEEE Transactions on Neural Systems and Rehabilitation Engineering

    Cites dataset 42 citations

  7. Generation of synthetic EEG data for training algorithms supporting the diagnosis of major depressive disorder

    Friedrich Philipp Carrle, Yasmin Hollenbenders, Alexandra Reichenbach · 2023 · Frontiers in Neuroscience

    Cites dataset 32 citations

  8. MAST-GCN: Multi-Scale Adaptive Spatial-Temporal Graph Convolutional Network for EEG-Based Depression Recognition

    Haifeng Lu, Zhiyang You, Yi Guo, Xiping Hu · 2024 · IEEE Transactions on Affective Computing

    Cites dataset 27 citations

  9. DCTNet: hybrid deep neural network-based EEG signal for detecting depression

    Yu Chen, Sheng Wang, Jifeng Guo · 2023 · Multimedia Tools and Applications

    Cites dataset 23 citations

  10. Multi-Granularity Graph Convolution Network for Major Depressive Disorder Recognition

    Xiaofang Sun, Yonghui Xu, Yibowen Zhao, Xiangwei Zheng, Yongqing Zheng, Lizhen Cui · 2023 · IEEE Transactions on Neural Systems and Rehabilitation Engineering

    Cites dataset 21 citations

  11. M-MDD: A multi-task deep learning framework for major depressive disorder diagnosis using EEG

    Yilin Wang, Sha Zhao, Haiteng Jiang, Shijian Li, Tao Li, Gang Pan · 2025 · Neurocomputing

    Cites dataset 15 citations

  12. Graph convolution network-based eeg signal analysis: a review

    Hui Xiong, Yan Yan, Yimei Chen, Jinzhen Liu · 2025 · Medical & Biological Engineering & Computing

    Cites dataset 10 citations

  13. Delaunay Triangulated Simplicial Complex Generation for EEG Signal Classification

    Srikireddy Dhanunjay Reddy, Tharun Kumar Reddy · 2024 · IEEE Sensors Letters

    Cites dataset 10 citations

  14. Neurophysiological biomarkers for depression classification: Utilizing microstate k-mers and a bag-of-words model

    Dongdong Zhou, Xinyu Peng, Lin Zhao, Lingli Ma, Jinhui Hu, Zhenghao Jiang, Xiaoqing He, Wo Wang, R.-W. Chen, Li Kuang · 2023 · Journal of Psychiatric Research

    Cites dataset 8 citations

  15. Decentralized EEG-based detection of major depressive disorder via transformer architectures and split learning

    Muhammad Umair, Jawad Ahmad, Nada Alasbali, Oumaima Saidani, Muhammad Fainan Hanif, Aizaz Ahmad Khattak, Muhammad Shahbaz Khan · 2025 · Frontiers in Computational Neuroscience

    Cites dataset 8 citations

  16. EEG-based depression classification using harmonized datasets

    Vladimir Savinov, Viktor Sapunov, Natalia Shusharina, Stepan Botman, Gleb Kamyshov, A. M. Tynterova · 2021 · n/a

    Cites dataset 8 citations

  17. Optimizing Depression Classification Using Combined Datasets and Hyperparameter Tuning with Optuna

    Ștefana Duță, Alina Sultana · 2025 · Sensors

    Cites dataset 7 citations

  18. EEG foundation models: a critical review of current progress and future directions

    Gayal Kuruppu, Neeraj Wagh, Václav Křemen, Yogatheesan Varatharajah · 2026 · Journal of Neural Engineering

    Cites dataset 5 citations

  19. A Hybrid Neural Network Approach Based on RNN and CNN for the Detection of Major Depressive Disorder

    Konapala Srilakshmi Anjana Priya, Hema Kumar Goru, Kunapareddy Kavya Priya, Bevara Dinesh Sai Manikanta · 2024 · n/a

    Cites dataset 3 citations

  20. LightFFNet: MDD Prediction on EEG Quantitative Biomarkers

    Urvashi Prakash Shukla, Shreeya Garg · 2022 · 2022 International Conference on Engineering and Emerging Technologies (ICEET)

    Cites dataset 1 citations

  21. Depression Diagnosis Using Optimization of Nonlinear EEG Features Based on Parametric Learning Tactics

    Ali Asadi Zeidabadi, Melika Changizi, Mahdi Zolfagharzadeh Kermani, Sara Bargi Barkouk · 2024 · n/a

    Cites dataset 1 citations

  22. Brain Functional Residual Temporal Convolution Network for Major Depressive Disorder Recognition

    Xiaofang Sun, Yonghui Xu, Xiangwei Zheng, Wei Guo, Wei He, Yali Jiang, Yongqing Zheng, Lizhen Cui · 2023 · n/a

    Cites dataset 1 citations

  23. A Hybrid Quantum-Classical Multiscale LSTM Framework for Subject-Level EEG-Based Depression Detection

    Sathiya E, Chunzhuo Wang, T.D. Rao, T. Sunil Kumar · 2026 · medRxiv

    Cites dataset

  24. Methodology of collection, recording and markup of biophysical multimodal data in the study of human psychoemotional states

    Natalia Shusharina · 2024 · Izvestiya of Saratov University Physics

    Cites dataset

  25. infoEEG-TM: A Non-Pretrained EEG Representation Learning Framework Basedon Information Theory

    Jiang Wu, Huan Gao, Shangyang Li, Tao Lu · 2025 · SSRN Electronic Journal

    Cites dataset

  26. A machine learning approach based on EEG signals for detection of depression

    Prajakta Rohan Naregalkar, Arundhati A. Shinde, Mangal Patil · 2025 · Engineering Research Express

    Cites dataset

  27. Advancing Clinical Trust in Deep Learning EEG Depression Detection Model: A Systematic Analysis of Demographic Influences, Task Dynamics, and AI Explainability

    Sumathi Balakrishnan, B.S.M. Ronald, Gregorius Hans Andreanto, WeiWei Goh, M. Nagentrau · 2025 · Algorithms for intelligent systems

    Cites dataset

  28. Efficiency of convolutional neural networks of different architecture for the task of depression diagnosis from EEG data

    Natalia Shusharina · 2024 · Izvestiya VUZ Applied Nonlinear Dynamics

    Cites dataset

  29. Reproducibility of electroencephalography biomarkers for diagnosis of major depressive disorder

    Yasmin Hollenbenders, Christoph Maier, Alexandra Reichenbach, Christoph Maier, Alexandra Reichenbach · 2024 · medRxiv

    Cites dataset

  30. An optimized EEG-based intrinsic brain network for depression detection using differential graph centrality

    Nausheen Ansari, Yusuf Uzzaman Khan, Omar Farooq · 2025 · Biomedical Physics & Engineering Express

    Cites dataset

  31. Handcrafted Versus Deep Transfer Learning Features for EEG-Based Detection of Major Depressive Disorder

    Harsh Bhasin, Nishtha Nagar, Tanish · 2026 · Lecture notes in networks and systems

    Cites dataset

  32. NeuroNarrator: A Generalist EEG-to-Text Foundation Model for Clinical Interpretation via Spectro-Spatial Grounding and Temporal State-Space Reasoning

    Guoan Wang, Shihao Yang, Jun-En Ding, Hao Zhu, Feng Liu · 2026 · bioRxiv (Cold Spring Harbor Laboratory)

    Cites dataset