@inproceedings{nguyen-etal-2025-transforming,
title = "Transforming Brainwaves into Language: {EEG} Microstates Meet Text Embedding Models for Dementia Detection",
author = "Nguyen, Quoc-Toan and
Le, Linh and
Tran, Xuan-The and
Bai, Dorothy and
Duong-Trung, Nghia and
Do, Thomas and
Lin, Chin-teng",
editor = "Zhao, Jin and
Wang, Mingyang and
Liu, Zhu",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-srw.12/",
doi = "10.18653/v1/2025.acl-srw.12",
pages = "186--202",
ISBN = "979-8-89176-254-1",
abstract = "This study proposes a novel, scalable, non-invasive and channel-independent approach for early dementia detection, particularly Alzheimer{'}s Disease (AD), by representing Electroencephalography (EEG) microstates as symbolic, language-like sequences. These representations are processed via text embedding and time-series deep learning models for classification. Developed on EEG data from 1001 participants across multiple countries, the proposed method achieves a high accuracy of 94.31{\%} for AD detection. By eliminating the need for fixed EEG configurations and costly/invasive modalities, the introduced approach improves generalisability and enables cost-effective deployment without requiring separate AI models or specific devices. It facilitates scalable and accessible dementia screening, supporting timely interventions and enhancing AD detection in resource-limited communities."
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%0 Conference Proceedings
%T Transforming Brainwaves into Language: EEG Microstates Meet Text Embedding Models for Dementia Detection
%A Nguyen, Quoc-Toan
%A Le, Linh
%A Tran, Xuan-The
%A Bai, Dorothy
%A Duong-Trung, Nghia
%A Do, Thomas
%A Lin, Chin-teng
%Y Zhao, Jin
%Y Wang, Mingyang
%Y Liu, Zhu
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-254-1
%F nguyen-etal-2025-transforming
%X This study proposes a novel, scalable, non-invasive and channel-independent approach for early dementia detection, particularly Alzheimer’s Disease (AD), by representing Electroencephalography (EEG) microstates as symbolic, language-like sequences. These representations are processed via text embedding and time-series deep learning models for classification. Developed on EEG data from 1001 participants across multiple countries, the proposed method achieves a high accuracy of 94.31% for AD detection. By eliminating the need for fixed EEG configurations and costly/invasive modalities, the introduced approach improves generalisability and enables cost-effective deployment without requiring separate AI models or specific devices. It facilitates scalable and accessible dementia screening, supporting timely interventions and enhancing AD detection in resource-limited communities.
%R 10.18653/v1/2025.acl-srw.12
%U https://aclanthology.org/2025.acl-srw.12/
%U https://doi.org/10.18653/v1/2025.acl-srw.12
%P 186-202
Markdown (Informal)
[Transforming Brainwaves into Language: EEG Microstates Meet Text Embedding Models for Dementia Detection](https://aclanthology.org/2025.acl-srw.12/) (Nguyen et al., ACL 2025)
ACL