Adaptive Multi-Expert Graph Transformer for Interpretable EEG-Based Diagnostics

Abstract:Electroencephalographic (EEG) abnormalities arise from dynamic changes in neural synchrony across spatial and temporal scales, yet many computational approaches reduce these dynamics to static features. We present a Spatial Multi-Expert Graph Transformer that models each EEG recording as a sequence of dynamic functional connectivity graphs. Time-resolved connectivity is estimated using the weighted Phase Lag Index (wPLI), and hierarchical graph encoding aggregates information from electrode to regional and global levels. A multi-expert transformer architecture enables subtype-aware reasoning, with a gating mechanism adaptively fusing expert outputs for global abnormality prediction. Experiments on the TUAB dataset show competitive abnormal EEG detection performance and demonstrate the potential of dynamic graph modeling with adaptive expert fusion for interpretable, subtype-aware spatial--temporal analysis.

Machine Learning (cs.LG)

arXiv:2607.19429 [cs.LG]

(or arXiv:2607.19429v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.19429 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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