Manuscripts on this dataset

1. 

   CMS Manager @ 3:15 pm on 23 Dec 2023

   Manuscripts on this dataset, "ds003490"

2. 

   CMS Manager @ 5:57 pm on 12 Feb 2024

   1. H Ding, X Weng, M Xu, J Shen, Z Wu, Dynamic channelwise functional-connectivity states extracted from resting-state EEG signals of patients with Parkinson’s disease, The Egyptian Journal of Neurology, Psychiatry and Neurosurgery, 2024, Cited by 0, https://link.springer.com/article/10.1186/s41983-024-00839-3
   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. EE Tülay, Ensemble classifiers fed by functional connectivity during cognitive processing differentiate Parkinson's disease even being under medication, Machine Learning: Science and Technology, 2024, Cited by 0, https://iopscience.iop.org/article/10.1088/2632-2153/ad370d/meta
   4. 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
   5. RA Abumalloh, M Nilashi, S Samad, H Ahmadi, Parkinson’s Disease Diagnosis Using Deep Learning: A Bibliometric Analysis and Literature Review, Ageing Research Reviews, 2024, Cited by 0, https://www.sciencedirect.com/science/article/pii/S156816372400103X
   6. EE TÜLAY, Detection of orienting response to novel sounds in healthy elderly subjects: A machine learning approach using EEG features, Acta Infologica, 2023, Cited by 1, https://dergipark.org.tr/en/pub/acin/issue/82503/1234106
   7. T Emine, Sağlıklı Yaşlı Bireylerde Yeni Seslere Yönlendirme Yanıtının Tespiti: EEG Özelliklerini Kullanan Bir Makine Öğrenimi Yaklaşımı, Acta Infologica, 2023, Cited by 0, https://iupress.istanbul.edu.tr/tr/journal/acin/article/detection-of-orienting-response-to-novel-sounds-in-healthy-elderly-subjects-a-machine-learning-approach-using-eeg-features
   8. E Vallarino, S Sommariva, F Famà, M Piana, F Nobili, Transfreq: A Python package for computing the theta‐to‐alpha transition frequency from resting state electroencephalographic data, 2022, Cited by 2, https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.25995
   9. M Shabanpour, N Kaboodvand, B Iravani, Parkinson's disease is characterized by sub-second resting-state spatio-oscillatory patterns: A contribution from deep convolutional neural network, NeuroImage: Clinical, 2022, Cited by 6, https://www.sciencedirect.com/science/article/pii/S221315822200331X
   10. E Vallarino, S Sommariva, D Arnaldi, F Famà, M Piana, Transfreq: A Python package for computing the theta-to-alpha transition frequency from resting state EEG data, bioRxiv, 2021, Cited by 3, https://www.biorxiv.org/content/10.1101/2021.12.03.471064.abstract
   11. D Borra, E Magosso, Deep learning-based EEG analysis: investigating P3 ERP components, Journal of Integrative Neuroscience, 2021, Cited by 18, https://cris.unibo.it/handle/11585/855872
   12. J Wilson, Quantify consciousness, Cited by 0

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