VINEURO: A MULTIMODAL EEG-BLOOD FUSION MODEL FOR ALZHEIMER’S RISK PREDICTION

Abstract

Early prediction of Alzheimer’s disease risk is crucial for timely intervention but
remains challenging in routine clinical practice. Electroencephalography (EEG) is
inexpensive and non-invasive, yet EEG alone often lacks sufficient sensitivity and
robustness for reliable early-stage risk estimation. In parallel, routine blood tests capture
peripheral immune, inflammatory, and metabolic changes associated with cognitive decline,
suggesting that combining EEG with blood-based biomarkers could yield more informative
risk stratification. In this work, ViNeuro, a multimodal EEG–blood model tailored to
Alzheimer’s risk prediction, is proposed. A single EEG foundation encoder, termed
ViNeuro-EEG, is first pretrained using the dual self-supervised objective of the EEGPT
model with the criss-cross backbone and learned positional encoding of the CBraMOD
model. Pretraining is conducted on a unified corpus of multi-channel clinical EEG data that
includes Vietnamese recordings from 108 Military Central Hospital and international
datasets. On top of this encoder, a multimodal extension, ViNeuro-MM, is constructed by
projecting routine blood biomarkers into the EEG embedding space and using them as
queries in a cross-attention layer over EEG tokens. The proposed framework is evaluated on
the PEARL-Neuro cohort for Alzheimer’s risk prediction. Compared to its EEG-only
counterpart, ViNeuro-MM achieves substantial performance gains, with relative
improvements of up to 24.72% in balanced accuracy, demonstrating that fusing routine
blood-based biomarkers with EEG foundation representations can markedly enhance early
Alzheimer’s risk prediction.