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\n  \n 2026\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n EEG-based Machine and Deep Learning Approaches for Alzheimer’s and Frontotemporal Dementia: A Systematic Review of the OpenNeuro ds004504 Dataset.\n \n \n \n\n\n \n Youness, M.; Nabila, Z.; Hind, A.; Issam, E.; Nabil, N.; and Ibtissam, B.\n\n\n \n\n\n\n In 2026 6th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), pages 1-6, 2026. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n
@inproceedings{\n title = {EEG-based Machine and Deep Learning Approaches for Alzheimer’s and Frontotemporal Dementia: A Systematic Review of the OpenNeuro ds004504 Dataset},\n type = {inproceedings},\n year = {2026},\n keywords = {Electroencephalography;Modeling;Accuracy;Dementia;Printing;Diseases;Reviews;Machine learning;Alzheimer's disease;Deep learning;Alzheimer’s disease;Frontotemporal dementia;Electroencephalography (EEG);Resting-state;Dementia classification;Machine Learning;Deep learning;OpenNeuro ds004504},\n pages = {1-6},\n id = {d379c48c-d334-3770-a88a-8bdfcfd23cec},\n created = {2026-09-29T23:58:07.594Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {583ef2ae-cee5-34c6-94c6-21e4d9515a06},\n last_modified = {2026-09-29T23:58:07.594Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {INPROCEEDINGS},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Youness, Mahdoubi and Nabila, Zrira and Hind, Aaibid and Issam, Elafi and Nabil, Ngote and Ibtissam, Benmiloud},\n doi = {10.1109/IRASET68627.2026.11538471},\n booktitle = {2026 6th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)}\n}
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