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Manuscripts on this dataset

  1. CMS Manager

    Manuscripts on this dataset, "ds004504"

  2. CMS Manager

    1. S Goerttler, F He, M Wu, Balancing Spectral, Temporal and Spatial Information for EEG-based Alzheimer's Disease Classification, arXiv preprint arXiv:2402.13523, 2024, Cited by 0, https://arxiv.org/abs/2402.13523
    2. S Ranjan, L Kumar, Dementia Severity Index: A Threshold-Based Approach to Classifying Dementia Level, 2024, Cited by 0, https://www.researchsquare.com/article/rs-4092892/latest
    3. Z Wang, A Liu, J Yu, P Wang, Y Bi, S Xue, J Zhang, The effect of aperiodic components in distinguishing Alzheimer's disease from frontotemporal dementia, GeroScience, 2024, Cited by 6, https://link.springer.com/article/10.1007/s11357-023-01041-8
    4. S Goerttler, F He, M Wu, Stochastic Graph Heat Modelling for Diffusion-based Connectivity Retrieval, arXiv preprint arXiv:2402.12785, 2024, Cited by 0, https://arxiv.org/abs/2402.12785
    5. NS Amer, SB Belhaouari, Exploring new horizons in neuroscience disease detection through innovative visual signal analysis, Scientific Reports, 2024, Cited by 0, https://www.nature.com/articles/s41598-024-54416-y
    6. A Miltiadous, KD Tzimourta, T Afrantou, P Ioannidis, A dataset of scalp EEG recordings of Alzheimer's disease, frontotemporal dementia and healthy subjects from routine EEG, Data, 2023, Cited by 14, https://www.mdpi.com/2306-5729/8/6/95
    7. XS Mootoo, A Fours, C Dinesh, M Ashkani, A Kiss, Detecting Alzheimer's Disease in EEG Data with Machine Learning and the Graph Discrete Fourier Transform, medRxiv, 2023, Cited by 0, https://www.medrxiv.org/content/10.1101/2023.11.01.23297940.abstract
    8. Y Si, R He, L Jiang, D Yao, H Zhang, Differentiating between Alzheimer’s Disease and Frontotemporal Dementia Based on the Resting-state Multilayer EEG Network, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023, Cited by 0, https://ieeexplore.ieee.org/abstract/document/10308628/
    9. J Chang, C Chang, Quantitative Electroencephalography Markers for an Accurate Diagnosis of Frontotemporal Dementia: A Spectral Power Ratio Approach, Medicina, 2023, Cited by 0, https://www.mdpi.com/1648-9144/59/12/2155
    10. U Lal, AV Chikkankod, L Longo, Leveraging SVD Entropy and Explainable Machine Learning for Alzheimerâ\euro™ s and Frontotemporal Dementia Detection using EEG, Authorea Preprints, 2023, Cited by 0, https://www.techrxiv.org/doi/full/10.36227/techrxiv.23992554.v2
    11. S Wu, P Zhan, G Wang, X Yu, H Liu, W Wang, Changes of brain functional network in Alzheimer's disease and frontotemporal dementia: a graph-theoretic analysis, 2023, Cited by 0, https://www.researchsquare.com/article/rs-3779337/latest
    12. A Miltiadous, KD Tzimourta, T Afrantou, P Ioannidis, A dataset of scalp EEG recordings of Alzheimer's disease, frontotemporal dementia and healthy subjects from routine EEG. Data. 2023; 8: 95, 2023, Cited by 2, https://www.academia.edu/download/103785357/pdf.pdf
    13. A Miltiadous, E Gionanidis, KD Tzimourta, DICE-net: a novel convolution-transformer architecture for Alzheimer detection in EEG signals, IEEE Access, 2023, Cited by 13, https://ieeexplore.ieee.org/abstract/document/10179900/
    14. Y Chen, H Wang, D Zhang, Multi-feature fusion learning for Alzheimer's disease prediction using EEG signals in resting state, Frontiers in neuroscience, 2023, Cited by 1, https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1272834/full
    15. A Velichko, M Belyaev, Y Izotov, M Murugappan, Neural Network Entropy (NNetEn): Entropy-Based EEG Signal and Chaotic Time Series Classification, Python Package for NNetEn Calculation, Algorithms, 2023, Cited by 5, https://www.mdpi.com/1999-4893/16/5/255
    16. A Velichko, M Belyaev, Y Izotov, M Murugappan, Neural Network Entropy (NNetEn): EEG Signals and Chaotic Time Series Separation by Entropy Features, Python Package for NNetEn Calculation, arXiv preprint arXiv:2303.17995, 2023, Cited by 0, https://arxiv.org/abs/2303.17995
    17. A Jha, N Kuruvilla, P Garg, Harnessing Creative Methods for EEG Feature Extraction and Modeling in Neurological Disorder Diagnoses, 2023 7th International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), 2023, Cited by 0, https://ieeexplore.ieee.org/abstract/document/10334244/
    18. Y Sawa, T Sato, T Ikeuchi, Banding survey at colonies of Brent Goose, Branta bernicla in the Lena Delta, Russia, and a recovery record, Bull Jpn Bird Banding Assoc, 2019, Cited by 2, https://scholar.archive.org/work/ps3aklzilffptcbnppakwbatae/access/wayback/https://www.jstage.jst.go.jp/article/jbba/31/1_2/31_MS117/_pdf
    19. 澤祐介， 佐藤達夫， 池内俊雄, ロシア・レナデルタのコロニーにおけるコクガンの標識調査および回収記録, 日本鳥類標識協会誌, 2019, Cited by 0, https://www.jstage.jst.go.jp/article/jbba/31/1_2/31_MS117/_article/-char/ja/
    20. A Parihar, PD Swami, EEG Classification of Alzheimer's Disease, Frontotemporal Dementia and Control Normal Subjects using Supervised Machine Learning Algorithms on various …, Cited by 0, https://www.researchgate.net/profile/Akanksha-Parihar-2/publication/373302163_EEG_Classification_of_Alzheimer's_Disease_Frontotemporal_Dementia_and_Control_Normal_Subjects_using_Supervised_Machine_Learning_Algorithms_on_various_EEG_Frequency_Bands/links/64e5be560453074fbda7b762/EEG-Classification-of-Alzheimers-Disease-Frontotemporal-Dementia-and-Control-Normal-Subjects-using-Supervised-Machine-Learning-Algorithms-on-various-EEG-Frequency-Bands.pdf
    21. J HATALA, ARTEFACTS REMOVAL FROM BRAIN EEG SIGNALS USING ADAPTIVE ALGORITHMS, Cited by 0, https://theses.cz/id/i4p8dx/bachelor_thesis_Archive.pdf

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