HiMDA: Transferring Hierarchical Microstate Dynamics, Not Labels, for Cross-Task EEG Decoding

Cross-task EEG decoding requires transferring knowledge between datasets whose label spaces share no meaningful correspondence, as when operational states must inform emotion recognition or clinical outcome prediction. Conventional feature alignment can conflate acquisition variation with neural temporal organization, whereas transferring a classifier imposes source semantics on an unrelated target task. We propose Hierarchical Microstate Dynamics Adaptation (HiMDA), a label-space-agnostic framework that transfers hierarchical temporal dynamics rather than labels or decision boundaries. HiMDA first constructs a domain-specific observation interface and calibrates polarity-invariant target microstate prototypes under a finite source reference. It then fits Bayesian switching dynamical systems independently in each domain, aligns their slow regimes through label-free dynamics fingerprints, and lets soft regime posteriors condition time-local fast microstate switches. Source slow-regime and regime-conditioned fast-transition operators parameterize finite Dirichlet priors whose posterior means are updated row-wise by target evidence, so well-supported target dynamics depart from the source while sparsely observed transitions remain stabilized. A random-forest readout trained from scratch on the adapted representation defines the target semantics, and no source logit or prediction head is transferred. Under a subject-dependent, trial-level five-fold protocol on SEED-IV, HiMDA achieves $77.04\%\pm 1.09\%$ accuracy. Under patient-disjoint, leave-one-hospital-out evaluation on I-CARE, it attains an AUROC of $0.85$ and an AUPRC of $0.90$. Together, these results show that hierarchical dynamics transfer across affective and clinical EEG tasks without source–target class correspondence.