ST-BiT: Spatio-Temporal Bipartite Transformer Network for Interaction-Preserving EEG-Based Dementia Subtyping

摘要

EEG-based dementia classification often degrades under clinically realistic subject-wise evaluation due to non-stationarity and large inter-subject variability. A key modeling limitation is relation compression: many EEG-GNN pipelines encode functional connectivity as scalar edge weights and blur interaction structure during message passing, while models may also exploit subject-specific cues. We propose ST-BiT, a Spatio-Temporal Bipartite Transformer that represents electrodes as node tokens and functional interactions as explicit, learnable edge tokens. Edge tokens preserve pairwise coupling patterns and are updated only from their endpoint electrodes via incidence-masked cross-attention, while electrode tokens aggregate information only from incident edges. Window-wise sparse graphs are constructed from time-sample correlations of band-limited signals to sparsify and initialize edge tokens. ST-BiT combines this interaction-preserving backbone with temporal self-attention, lightweight band attention, and domain-adversarial alignment to reduce subject bias. On OpenNeuro ds006036 (eyes-open with photic stimulation), using leakage-free subject-wise stratified 5-fold cross-validation with nested model selection, ST-BiT achieves 93.0% accuracy for CN vs. (AD+FTD) and 76.1% for CN/AD/FTD, outperforming classical ML and GNN baselines under identical folds. To assess robustness across recording states and align with prior work on this cohort, we also evaluate on the ds004504 (eyes-closed) dataset.