Transforming Brainwaves into Language: EEG Microstates Meet Text Embedding Models for Dementia Detection

Quoc-Toan Nguyen, Linh Le, Xuan-The Tran, Dorothy Bai, Nghia Duong-Trung, Thomas Do, Chin-teng Lin

Keywords: Text Embedding Model, EEG, Brain-Computer Interface, Dementia, Alzheimer's, AI in Healthcare

TL;DR: We propose a scalable, channel-independent framework that models EEG microstates as text-like sequences for deep learning-based Alzheimer’s detection (accuracy of 94.31%), enabling low-cost, accessible screening in resource-limited settings.

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.

Archival Status: Archival

Paper Length: Long Paper (up to 8 pages of content)

Submission Number: 41