{"paper":{"title":"DeepTokenEEG Enhancing Mild Cognitive Impairment and Alzheimers Classification via Tokenized EEG Features","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"DeepTokenEEG uses tokenized EEG features in a lightweight model to achieve up to 100% accuracy in Alzheimer's classification, outperforming prior methods by 1.41-15.35%.","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bui Thanh Tung, Hung Cao, Huy-Dung Han, Khuong Vo, Manoj Vishwanath, Minh Long Ngo, Ngoc-Son Nguyen, Nguyen Quang Linh, Nguyen Thanh Vinh, Thinh Nguyen-Quang","submitted_at":"2026-05-14T16:10:03Z","abstract_excerpt":"The detection of Alzheimers disease (AD) is considered crucial, as timely intervention can improve patient outcomes. Electroencephalogram (EEG)-based diagnosis has been recognized as a non-invasive, accessible, and cost-effective approach for AD detection; however, it faces challenges related to data availability, accuracy of modern deep learning methods, and the time-consuming nature of expert-based interpretation. In this study, a novel lightweight and high-performance model, DeepTokenEEG, was designed for the diagnosis of AD and the classification of EEG signals from AD patients, individual"},"claims":{"count":3,"items":[{"kind":"strongest_claim","text":"a novel lightweight and high-performance model, DeepTokenEEG, was designed for the diagnosis of AD and the classification of EEG signals from AD patients, individuals with other neurological conditions, and healthy subjects. ... achieves a maximum recorded accuracy of 100% on specific frequency bands, representing an improvement of 1.41-15.35% over state-of-the-art methods on the same dataset.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the 100% accuracy on specific frequency bands generalizes beyond the 274-subject dataset (180 AD, 94 controls) and is not due to overfitting, data leakage, or unaccounted artifacts in the EEG recordings.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"DeepTokenEEG uses tokenized EEG features in a lightweight model to achieve up to 100% accuracy in Alzheimer's classification, outperforming prior methods by 1.41-15.35%.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"}],"snapshot_sha256":"fa102da633692dae6583da8d5b046479404f1603d9af5386dae9ad91ae1ff4ac"},"source":{"id":"2605.15009","kind":"arxiv","version":1},"verdict":{"id":"71c4e22a-c424-4fd4-997b-2efd2fb31980","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T03:19:43.052306Z","strongest_claim":"a novel lightweight and high-performance model, DeepTokenEEG, was designed for the diagnosis of AD and the classification of EEG signals from AD patients, individuals with other neurological conditions, and healthy subjects. ... achieves a maximum recorded accuracy of 100% on specific frequency bands, representing an improvement of 1.41-15.35% over state-of-the-art methods on the same dataset.","one_line_summary":"DeepTokenEEG uses tokenized EEG features in a lightweight model to achieve up to 100% accuracy in Alzheimer's classification, outperforming prior methods by 1.41-15.35%.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the 100% accuracy on specific frequency bands generalizes beyond the 274-subject dataset (180 AD, 94 controls) and is not due to overfitting, data leakage, or unaccounted artifacts in the EEG recordings.","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}