@article{Wei2025, 
author = {Jiaxin Wei and Bin Jiao and Hanhe Lin and Xu Tian and Lu Shen and Jin Liu},
title = {Multi-Scale Adaptive Graph Learning Based on Multi-Wave EEG Data for Dementia Diagnosis},
year = {2025},
journal = {Big Data Mining and Analytics},
volume = {8},
number = {5},
pages = {1023-1043},
keywords = {Electroencephalogram (EEG), data augmentation, dementia diagnosis, Adaptive Graph Learning (AGL)},
url = {https://www.sciopen.com/article/10.26599/BDMA.2025.9020015},
doi = {10.26599/BDMA.2025.9020015},
abstract = {Dementia is a syndrome causing a progressive loss of brain function and mainly includes subtypes, such as Alzheimer’s Disease (AD), FrontoTemporal Dementia (FTD), and Vascular Cognitive Impairment (VCI). Electroencephalography (EEG) is widely used in dementia diagnosis to detect brain electrophysiological signals efficiently. However, the small number of samples available in EEG-based dementia diagnosis results in poor performance of existing methods. To address this issue, we propose a Multi-scale Adaptive Graph Learning based on Multi-wave EEG data (MAGLM) for dementia diagnosis. Firstly, we extract both time-domain and frequency-domain features of multi-wave EEG data. Secondly, to reliably expand the insufficient samples, we propose a multi-wave EEG data augmentation model based on generative learning. Finally, to explore the rich patterns between scales, waves, and samples, we propose a multi-scale adaptive graph learning model to perform dementia diagnosis based on augmented EEG data. MAGLM is validated on an in-house EEG dataset, including AD, FTD, and VCI. The experimental and visualization results show the superiority of the proposed MAGLM over the state-of-the-art methods. In conclusion, MAGLM is not only effective in dementia diagnosis, but also provides experience for EEG-based brain science research.}
}