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Manuscripts on this dataset

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    Manuscripts on this dataset, "ds002778"
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    1. F Del Pup, A Zanola, LF Tshimanga, The more, the better? Evaluating the role of EEG preprocessing for deep learning applications., IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2025, Cited by 0, https://ieeexplore.ieee.org/abstract/document/10909332/
    2. MM Afonso, DR Edla, RR Reddy, Optimizing Parkinson's Disease Detection: Hybrid S-transform-EEG Feature Reduction Through Trajectory Analysis, SN Computer Science, 2025, Cited by 0, https://link.springer.com/article/10.1007/s42979-025-03685-z
    3. F Garehdaghi, Y Sarbaz, A robust method for parkinson's disease diagnosis: Combining electroencephalography signal features with reconstructed phase space images, Medical Engineering & Physics, 2025, Cited by 0, https://www.sciencedirect.com/science/article/pii/S1350453324001760
    4. K Kumar, R Ghosh, Parkinson's disease diagnosis using deep learning model by analyzing the channels of electroencephalography signals from substansia niagra and ventral tegmental …, International Journal of Information Technology, 2025, Cited by 0, https://link.springer.com/article/10.1007/s41870-024-02377-w
    5. BR AL-QAYSI, MR Zurera, ALIA AL-DUJAILI, Non-Linear Synthetic Time Series Generation for EEG Data Using LSTM Models, 2025, Cited by 0, https://www.preprints.org/frontend/manuscript/28386788fb432544316c60600a85fb3e/download_pub
    6. A Jaramillo-Jimenez, YJ Mantilla-Ramos, Characterizing resting-state EEG oscillatory and aperiodic activity in neurodegenerative diseases: A multicentric study, medRxiv, 2025, Cited by 0, https://www.medrxiv.org/content/10.1101/2025.02.14.25322283.abstract
    7. 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 1, https://link.springer.com/article/10.1186/s41983-024-00839-3
    8. CL Alves, LF Sallum, FA Rodrigues, GLO Thaise, Temporal Patterns of EEG Connectivity Unveil Parkinson's Disease Progression: Insights from Machine Learning Analysis, 2024, Cited by 0, https://www.researchsquare.com/article/rs-4095364/latest
    9. T Islam, P Washington, Non-invasive biosensing for healthcare using artificial intelligence: a semi-systematic review, Biosensors, 2024, Cited by 9, https://www.mdpi.com/2079-6374/14/4/183
    10. CP Da Silva, S Tedesco, B O'Flynn, EEG datasets for healthcare: a scoping review, IEEE Access, 2024, Cited by 5, https://ieeexplore.ieee.org/abstract/document/10466559/
    11. A Zanola, F Del Pup, C Porcaro, BIDSAlign: a library for automatic merging and preprocessing of multiple EEG repositories, Journal of Neural Engineering, 2024, Cited by 1, https://iopscience.iop.org/article/10.1088/1741-2552/ad6a8c/meta
    12. N Delfan, M Shahsavari, S Hussain, A Hybrid Deep Spatiotemporal Attention-Based Model for Parkinson's Disease Diagnosis Using Resting State EEG Signals, International Journal of Imaging Systems and Technology, 2024, Cited by 9, https://onlinelibrary.wiley.com/doi/abs/10.1002/ima.23120
    13. D Candia‐Rivera, M Vidailhet, M Chavez, A framework for quantifying the coupling between brain connectivity and heartbeat dynamics: Insights into the disrupted network physiology in Parkinson's disease, Human Brain Mapping, 2024, Cited by 6, https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.26668
    14. U Lal, AV Chikkankod, L Longo, Fractal dimensions and machine learning for detection of Parkinson's disease in resting-state electroencephalography, Neural Computing and Applications, 2024, Cited by 11, https://link.springer.com/article/10.1007/s00521-024-09521-4
    15. L Nucci, F Miraglia, C Pappalettera, PM Rossini, Exploring the complexity of EEG patterns in Parkinson's disease, GeroScience, 2024, Cited by 2, https://link.springer.com/article/10.1007/s11357-024-01277-y
    16. D Candia-Rivera, M Chavez, Measures of the coupling between fluctuating brain network organization and heartbeat dynamics, Network Neuroscience, 2024, Cited by 13, https://direct.mit.edu/netn/article/8/2/557/119910
    17. S Jain, R Srivastava, Multi-modality NDE fusion using encoder–decoder networks for identify multiple neurological disorders from EEG signals, Technology and Health Care, 2024, Cited by 0, https://journals.sagepub.com/doi/abs/10.1177/09287329241291334
    18. FA Jibon, A Tasbir, MA Talukder, MA Uddin, Parkinson's disease detection from EEG signal employing autoencoder and RBFNN-based hybrid deep learning framework utilizing power spectral density, Digital Health, 2024, Cited by 3, https://journals.sagepub.com/doi/abs/10.1177/20552076241297355
    19. M Önder, Ü Şentürk, K Polat, Shallow Learning Versus Deep Learning in Biomedical Applications, Shallow Learning vs. Deep Learning: A Practical Guide for Machine Learning Solutions, 2024, Cited by 0, https://link.springer.com/chapter/10.1007/978-3-031-69499-8_6
    20. A Hamidi, K Mohamed-Pour, Forged Channel: A Breakthrough Approach for Accurate Parkinson's Disease Classification using Leave-One-Subject-Out Cross-Validation, 2024 32nd International Conference on Electrical Engineering (ICEE), 2024, Cited by 1, https://ieeexplore.ieee.org/abstract/document/10667765/
    21. R Krishna, N Chopra, S Kumar, Deep Learning for Parkinson's Disease Detection: An Analytical Study, 2024 14th International Conference on Cloud Computing, Data Science \& Engineering (Confluence), 2024, Cited by 0, https://ieeexplore.ieee.org/abstract/document/10463362/
    22. F Latifoğlu, S Penekli, F Orhanbulucu, A novel approach for Parkinson’s disease detection using Vold-Kalman order filtering and machine learning algorithms, Neural Computing and Applications, 2024, Cited by 7, https://link.springer.com/article/10.1007/s00521-024-09569-2
    23. Z Yuan, F Shen, M Li, Y Yu, C Tan, Y Yang, BrainWave: A Brain Signal Foundation Model for Clinical Applications, arXiv preprint arXiv:2402.10251, 2024, Cited by 3, https://arxiv.org/abs/2402.10251
    24. 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
    25. TE Özkurt, Abnormally low sensorimotor α band nonlinearity serves as an effective EEG biomarker of Parkinson's disease, Journal of Neurophysiology, 2024, Cited by 0, https://journals.physiology.org/doi/abs/10.1152/jn.00272.2023
    26. GK Baboo, S Dubey, V Baths, Comparative Study of Neural Networks (G/C/RNN) and Traditional Machine Learning Models on EEG Datasets, Proceedings of the International Conference on Cognitive and Intelligent Computing: ICCIC 2021, Volume 2, 2023, Cited by 0, https://link.springer.com/chapter/10.1007/978-981-19-2358-6_17
    27. M Nour, U Senturk, K Polat, Diagnosis and classification of Parkinson's disease using ensemble learning and 1D-PDCovNN, Computers in biology and medicine, 2023, Cited by 18, https://www.sciencedirect.com/science/article/pii/S0010482523004961
    28. SQA Rizvi, G Wang, A Khan, MK Hasan, Classifying Parkinson’s disease using resting state electroencephalogram signals and U EN-PDNet, IEEE access, 2023, Cited by 17, https://ieeexplore.ieee.org/abstract/document/10262304/
    29. U Lal, AV Chikkankod, L Longo, Fractal Dimensions and Machine Learning for Detection of Parkinson's Disease in Resting-State EEG, 2023, Cited by 0, https://www.researchsquare.com/article/rs-3270985/latest
    30. A Jaramillo-Jimenez, DA Tovar-Rios, JA Ospina, Spectral features of resting-state EEG in Parkinson's Disease: a multicenter study using functional data analysis, Clinical Neurophysiology, 2023, Cited by 13, https://www.sciencedirect.com/science/article/pii/S1388245723005989
    31. D Candia-Rivera, M Vidailhet, M Chavez, The coupling between brain connectivity and heartbeat dynamics unveils the altered interoceptive mechanisms in Parkinson's disease, Available at SSRN 4540760, 2023, Cited by 0, https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4540760
    32. CA Tobón Quintero, FJ Lopera Restrepo, Spectral features of resting-state EEG in Parkinson's Disease: A multicenter study using functional data analysis, 2023, Cited by 0, https://bibliotecadigital.udea.edu.co/handle/10495/42131
    33. M Obayya, MK Saeed, M Maashi, SS Alotaibi, A novel automated Parkinson’s disease identification approach using deep learning and EEG, PeerJ Computer Science, 2023, Cited by 5, https://peerj.com/articles/cs-1663/
    34. P Chawla, SB Rana, H Kaur, K Singh, R Yuvaraj, A decision support system for automated diagnosis of Parkinson’s disease from EEG using FAWT and entropy features, Biomedical Signal Processing and Control, 2023, Cited by 36, https://www.sciencedirect.com/science/article/pii/S1746809422005730
    35. M Aljalal, SA Aldosari, M Molinas, K AlSharabi, Detection of Parkinson's disease from EEG signals using discrete wavelet transform, different entropy measures, and machine learning techniques, Scientific Reports, 2022, Cited by 63, https://www.nature.com/articles/s41598-022-26644-7
    36. Z Wang, Y Mo, Y Sun, K Hu, C Peng, Separating the aperiodic and periodic components of neural activity in Parkinson's disease, European Journal of Neuroscience, 2022, Cited by 26, https://onlinelibrary.wiley.com/doi/abs/10.1111/ejn.15774
    37. M Aljalal, SA Aldosari, K AlSharabi, AM Abdurraqeeb, Parkinson's disease detection from resting-state EEG signals using common spatial pattern, entropy, and machine learning techniques, Diagnostics, 2022, Cited by 67, https://www.mdpi.com/2075-4418/12/5/1033
    38. O Stylianou, Z Kaposzta, A Czoch, L Stefanovski, Scale-free functional brain networks exhibit increased connectivity, are more integrated and less segregated in patients with Parkinson’s disease following dopaminergic treatment, Fractal and fractional, 2022, Cited by 8, https://www.mdpi.com/2504-3110/6/12/737
    39. L Qiu, J Li, J Pan, Parkinson's disease detection based on multi-pattern analysis and multi-scale convolutional neural networks, Frontiers in Neuroscience, 2022, Cited by 24, https://www.frontiersin.org/articles/10.3389/fnins.2022.957181/full
    40. M Shaban, AW Amara, Resting-state electroencephalography based deep-learning for the detection of Parkinson's disease, Plos one, 2022, Cited by 41, https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0263159
    41. J Zhang, A Villringer, VV Nikulin, Dopaminergic modulation of local non-oscillatory activity and global-network properties in Parkinson's disease: an EEG study, Frontiers in Aging Neuroscience, 2022, Cited by 18, https://www.frontiersin.org/articles/10.3389/fnagi.2022.846017/full
    42. S Cahoon, F Khan, M Polk, Wavelet-Based Convolutional Neural Network for Parkinson's Disease Detection in Resting-State Electroencephalography, 2021 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2021, Cited by 6, https://ieeexplore.ieee.org/abstract/document/9672279/
    43. HW Loh, CP Ooi, E Palmer, PD Barua, S Dogan, GaborPDNet: Gabor transformation and deep neural network for Parkinson's disease detection using EEG signals, Electronics, 2021, Cited by 98, https://www.mdpi.com/2079-9292/10/14/1740
    44. M Shaban, Automated screening of Parkinson's disease using deep learning based electroencephalography, 2021 10th international IEEE/EMBS conference on neural engineering (NER), 2021, Cited by 36, https://ieeexplore.ieee.org/abstract/document/9441065/
    45. E Rümeysa, R İleri, F Latifoğlu, A new approach to detection of Parkinson’s disease using variational mode decomposition method and deep neural networks, 2021 Medical Technologies Congress (TIPTEKNO), 2021, Cited by 2, https://ieeexplore.ieee.org/abstract/document/9632951/
    46. M Shaban, S Cahoon, F Khan, Exploiting the Differential Wavelet Domain of Resting-State EEG Using a Deep-CNN for Screening Parkinson's Disease, 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021, Cited by 6, https://ieeexplore.ieee.org/abstract/document/9660178/
    47. N Kamalakannan, SPS Balamurugan, A novel approach for the early detection of Parkinson’s disease using EEG signal, Technology (IJEET), 2021, Cited by 6, https://www.academia.edu/download/100976473/IJEET_12_05_008.pdf
    48. AP Rockhill, N Jackson, J George, A Aron, NC Swann, UC San Diego resting state EEG data from patients with Parkinson's disease, 2021, Cited by 7

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