Manuscripts on this dataset

1. 

   CMS Manager @ 5:56 am on 07 Feb 2024

   Manuscripts on this dataset, "ds003478"

2. 

   CMS Manager @ 5:56 am on 07 Feb 2024

   1. C Peres da Silva, S Tedesco, B O'Flynn, EEG datasets for healthcare: A scoping review, 2024, Cited by 0, https://cora.ucc.ie/items/e4c14a93-4db4-4788-852e-1c9e44132270
   2. CP Da Silva, S Tedesco, B O'Flynn, EEG datasets for healthcare: a scoping review, IEEE Access, 2024, Cited by 0, https://ieeexplore.ieee.org/abstract/document/10466559/
   3. D Sihn, JS Kim, OS Kwon, SP Kim, Breakdown of long-range spatial correlations of infraslow amplitude fluctuations of EEG oscillations in patients with current and past major depressive disorder, Frontiers in Psychiatry, 2023, Cited by 3, https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1132996/full
   4. J Chang, Y Choi, Depression diagnosis based on electroencephalography power ratios, Brain and Behavior, 2023, Cited by 4, https://onlinelibrary.wiley.com/doi/abs/10.1002/brb3.3173
   5. G Luo, P An, Y Li, R Hong, S Chen, Exploring adaptive graph topologies and temporal graph networks for EEG-based depression detection, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023, Cited by 3, https://ieeexplore.ieee.org/abstract/document/10268256/
   6. N Shusharina, D Yukhnenko, S Botman, V Sapunov, Modern methods of diagnostics and treatment of neurodegenerative diseases and depression, Diagnostics, 2023, Cited by 29, https://www.mdpi.com/2075-4418/13/3/573
   7. X Sun, Y Xu, Y Zhao, X Zheng, Multi-Granularity Graph Convolution Network for Major Depressive Disorder Recognition, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023, Cited by 0, https://ieeexplore.ieee.org/abstract/document/10238750/
   8. N Draudt, BATS: Development of a Biosignal Analysis Toolkit and Pipeline for Polytrauma Research, 2022, Cited by 0, http://web.cs.wpi.edu/~claypool/mqp/bio-21/final-report.pdf
   9. NP Tigga, S Garg, Efficacy of novel attention-based gated recurrent units transformer for depression detection using electroencephalogram signals, Health Information Science and Systems, 2022, Cited by 10, https://link.springer.com/article/10.1007/s13755-022-00205-8
   10. V Savinov, V Sapunov, N Shusharina, Research and selection of the optimal neural network architecture and parameters for depression classification using harmonized datasets, 2022 Fourth International Conference Neurotechnologies and Neurointerfaces (CNN), 2022, Cited by 1, https://ieeexplore.ieee.org/abstract/document/9912567/
   11. C Hung, The Impact of Cross-Validation on the Automated EEG-Based Diagnosis, 2022, Cited by 0
   12. L Minkowski, Classifying Severity of Depression and Anxiety by Analyzing Electroencephalography (EEG) Signals for Neurophysiological Biomarkers, 2021, Cited by 0, https://rshare.library.torontomu.ca/ndownloader/files/43269018
   13. V Savinov, V Sapunov, N Shusharina, EEG-based depression classification using harmonized datasets, 2021 Third International Conference Neurotechnologies and Neurointerfaces (CNN), 2021, Cited by 4, https://ieeexplore.ieee.org/abstract/document/9580293/
   14. Y Zhou, X Yu, H Lin, R Li, J Liang, X Shi, Depression Severity Identification Based on Shallow 2d Self-Attention-Cnn Using Eeg Functional Connectivity Network, Available at SSRN 4813480, Cited by 0, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4813480

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