@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://preview.aclanthology.org/landing_page/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."
}