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arXiv · 2605.15009

DeepTokenEEG Enhancing Mild Cognitive Impairment and Alzheimers Classification via Tokenized EEG Features

Thinh Nguyen-Quang·Minh Long Ngo·Ngoc-Son Nguyen·Nguyen Thanh Vinh·Huy-Dung Han·Bui Thanh Tung·Nguyen Quang Linh·Khuong Vo·Manoj Vishwanath·Hung Cao
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Abstract

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, individuals with other neurological conditions, and healthy subjects. Unlike traditional heavy-weight models, DeepTokenEEG ultilizes spatial and temporal tokenizer that effectively captures AD-related biomarkers in both temporal and frequency domain with only 0.29 million paramaters. Trained in a combined dataset of 274 subjects, including 180 AD cases, and 94 healthy controls, the proposed method 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. These results indicate the potential of DeepTokenEEG for early detection and screening of AD, with promising applicability for deployment due to its compact size.

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Thinh Nguyen-Quang, Minh Long Ngo, Ngoc-Son Nguyen, Nguyen Thanh Vinh, Huy-Dung Han, Bui Thanh Tung, Nguyen Quang Linh, Khuong Vo, Manoj Vishwanath, Hung Cao. 2026-05-14. DeepTokenEEG Enhancing Mild Cognitive Impairment and Alzheimers Classification via Tokenized EEG Features. https://arxiv.org/abs/2605.15009

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