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Manuscripts on this dataset

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    Manuscripts on this dataset, "ds002778"
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    1. 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 0, https://link.springer.com/article/10.1007/s00521-024-09521-4
    2. J Li, X Li, Y Mao, J Yao, J Gao, X Liu, Classification of Parkinson’s disease EEG signals using 2D-MDAGTS model and multi-scale fuzzy entropy, Biomedical Signal Processing and Control, 2024, Cited by 0, https://www.sciencedirect.com/science/article/pii/S1746809423013058
    3. 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
    4. 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 0, https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.26668
    5. 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/
    6. 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 0, https://link.springer.com/article/10.1007/s00521-024-09569-2
    7. D Candia-Rivera, M Chavez, Measures of the coupling between fluctuating brain network organization and heartbeat dynamics, Network Neuroscience, 2024, Cited by 1, https://direct.mit.edu/netn/article/doi/10.1162/netn_a_00369/119910
    8. 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
    9. 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 19, https://www.sciencedirect.com/science/article/pii/S1746809422005730
    10. 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 9, https://www.sciencedirect.com/science/article/pii/S0010482523004961
    11. 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 3, https://www.sciencedirect.com/science/article/pii/S1388245723005989
    12. 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 0, https://peerj.com/articles/cs-1663/
    13. 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
    14. A Hamidi, M Yousefi, Classification of EEG signals to detect Parkinsons Disease using a computationally method, arXiv preprint arXiv:2305.02234, 2023, Cited by 2, https://arxiv.org/abs/2305.02234
    15. 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 1, https://ieeexplore.ieee.org/abstract/document/10262304/
    16. D Candia-Rivera, M Vidailhet, M Chavez, The coupling between brain connectivity and heartbeat dynamics unveils the altered interoceptive mechanisms in Parkinson's disease, medRxiv, 2023, Cited by 0, https://www.medrxiv.org/content/10.1101/2023.07.20.23292942.abstract
    17. 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 26, https://www.nature.com/articles/s41598-022-26644-7
    18. 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 11, https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.957181
    19. 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 6, https://www.mdpi.com/2504-3110/6/12/737
    20. 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 16, https://onlinelibrary.wiley.com/doi/abs/10.1111/ejn.15774
    21. 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 13, https://www.frontiersin.org/articles/10.3389/fnagi.2022.846017/full
    22. M Shaban, AW Amara, Resting-state electroencephalography based deep-learning for the detection of Parkinson's disease, Plos one, 2022, Cited by 21, https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0263159
    23. 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 33, https://www.mdpi.com/2075-4418/12/5/1033
    24. 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 2, https://ieeexplore.ieee.org/abstract/document/9672279/
    25. 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 55, https://www.mdpi.com/2079-9292/10/14/1740
    26. 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 25, https://ieeexplore.ieee.org/abstract/document/9441065/
    27. 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/
    28. 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 3, https://ieeexplore.ieee.org/abstract/document/9660178/
    29. N Kamalakannan, SPS Balamurugan, A novel approach for the early detection of Parkinson’s disease using EEG signal, Technology (IJEET), 2021, Cited by 4, https://www.academia.edu/download/67471983/IJEET_12_05_008.pdf

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