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Manuscripts on this dataset

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    Manuscripts on this dataset, "ds004504"

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    1. U Lal, AV Chikkankod, L Longo, A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer's Disease with Electroencephalography in …, Brain Sciences, 2024, Cited by 1, https://www.mdpi.com/2076-3425/14/4/335
    2. 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 9, https://link.springer.com/article/10.1007/s11357-023-01041-8
    3. MP Bonomini, E Ghiglioni, NB Rios, Connectivity Patterns in Alzheimer Disease and Frontotemporal Dementia Patients Using Graph Theory, International Work-Conference on the Interplay Between Natural and Artificial Computation, 2024, Cited by 0, https://link.springer.com/chapter/10.1007/978-3-031-61140-7_37
    4. H Zheng, X Xiong, X Zhang, Multi-Threshold Recurrence Rate Plot: A Novel Methodology for EEG Analysis in Alzheimer's Disease and Frontotemporal Dementia, Brain Sciences, 2024, Cited by 0, https://www.mdpi.com/2076-3425/14/6/565
    5. W Wan, Z Gu, CK Peng, X Cui, Beyond Frequency Bands: Complementary-Ensemble-Empirical-Mode-Decomposition-Enhanced Microstate Sequence Non-Randomness Analysis for Aiding …, Brain Sciences, 2024, Cited by 0, https://www.mdpi.com/2076-3425/14/5/487
    6. B Wilkie, K Muñoz Esquivel, J Roche, An LSTM Framework for the Effective Screening of Dementia for Deployment on Edge Devices, Nordic Conference on Digital Health and Wireless Solutions​, 2024, Cited by 0, https://link.springer.com/chapter/10.1007/978-3-031-59080-1_2
    7. Y Wang, N Huang, T Li, Y Yan, X Zhang, Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification, arXiv preprint arXiv:2405.19363, 2024, Cited by 0, https://arxiv.org/abs/2405.19363
    8. 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
    9. 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
    10. M Sano, Y Nishiura, I Morikawa, A Hoshino, J Uemura, Analysis of the alpha activity envelope in electroencephalography in relation to the ratio of excitatory to inhibitory neural activity, PloS one, 2024, Cited by 0, https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0305082
    11. NS Amer, SB Belhaouari, Exploring new horizons in neuroscience disease detection through innovative visual signal analysis, Scientific Reports, 2024, Cited by 1, https://www.nature.com/articles/s41598-024-54416-y
    12. 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
    13. J Kim, S Jeong, J Jeon, HI Suk, Unveiling Diagnostic Potential: EEG Microstate Representation Model for Alzheimer’s Disease and Frontotemporal Dementia, 2024 12th International Winter Conference on Brain-Computer Interface (BCI), 2024, Cited by 0, https://ieeexplore.ieee.org/abstract/document/10480470/
    14. 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 1, https://www.researchsquare.com/article/rs-3779337/latest
    15. Y Chen, H Wang, D Zhang, L Zhang, Multi-feature fusion learning for Alzheimer's disease prediction using EEG signals in resting state, Frontiers in neuroscience, 2023, Cited by 2, https://www.frontiersin.org/articles/10.3389/fnins.2023.1272834/full
    16. 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
    17. 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 1, https://ieeexplore.ieee.org/abstract/document/10308628/
    18. 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 23, https://www.mdpi.com/2306-5729/8/6/95
    19. 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
    20. 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
    21. 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 6, https://www.mdpi.com/1999-4893/16/5/255
    22. A Miltiadous, E Gionanidis, KD Tzimourta, DICE-net: a novel convolution-transformer architecture for Alzheimer detection in EEG signals, IEEE Access, 2023, Cited by 23, https://ieeexplore.ieee.org/abstract/document/10179900/
    23. 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 5, https://www.academia.edu/download/103785357/pdf.pdf
    24. 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
    25. 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/
    26. 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 3, https://scholar.archive.org/work/ps3aklzilffptcbnppakwbatae/access/wayback/https://www.jstage.jst.go.jp/article/jbba/31/1_2/31_MS117/_pdf
    27. 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/
    28. 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 1, 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
    29. J HATALA, ARTEFACTS REMOVAL FROM BRAIN EEG SIGNALS USING ADAPTIVE ALGORITHMS, Cited by 0, https://theses.cz/id/i4p8dx/bachelor_thesis_Archive.pdf
    30. M Särestöniemi, P Keikhosrokiani, D Singh, E Harjula, Digital Health and Wireless Solutions, Cited by 0, https://link.springer.com/content/pdf/10.1007/978-3-031-59080-1.pdf
    31. X Shen, L Ding, L Gu, X Li, Y Wang, Diagnosis of Alzheimer's Disease Based on Particle Swarm Optimization Eeg Signal Channel Selection and Gated Recurrent Unit, Available at SSRN 4844658, Cited by 0, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4844658

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